Certain aspects of the present disclosure provide techniques for providing an augmented reality user interface, including: receiving, by an image sensor of an electronic device, an image of a physical document; determining a document type associated with the physical document by performing image recognition on the image of the physical document; determining an augmented reality template to display on a display of the electronic device; displaying the augmented reality template on the display of the electronic device, wherein the augmented reality template is aligned in three dimensions with the physical document; determining a distance between the physical document and the electronic device; and enabling one or more interactive user interface elements within the augmented reality template displayed on the display of the electronic device if the determined distance between the physical document and the electronic device is less than a threshold distance.
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 3/04886 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus
Systems and methods for identifying and extracting specific product usage patterns of potential customers and utilizing a machine learning evaluation model to predict the potential customers that are most likely to convert their subscriptions.
Certain aspects of the present disclosure provide techniques for rendering visual artifacts in virtual worlds using machine learning models. An example method generally includes identifying, based on a machine learning model and a streaming natural language input, an intent associated with the streaming natural language input; generating, based on the identified intent associated with the streaming natural language input, one or more virtual objects for rendering in a virtual environment displayed on one or more displays of an electronic device; and rendering the generated one or more virtual objects in the virtual environment.
A method including receiving a natural language query from a user interface of a chatbot. The method also includes generating an input vector by performing vectorization on the natural language query. The method also includes inputting the input vector to a shallow-deep classifier. The shallow-deep learning classifier includes a classification machine learning model programmed to classify the input vector as being one of a shallow machine learning classification problem and a deep machine learning classification problem. The method also includes outputting, by the shallow-deep classifier, an output label. The output label includes one of the shallow machine learning classification problem and the deep machine learning classification problem.
G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
A method models and manages affinity networks. The method includes receiving real-time transaction data; processing a transaction of the real-time transaction data to identify a source node and a target node of a graph; and processing the transaction to update an affinity score of an edge from the source node to the target node. The method further includes receiving a request; selecting, responsive to the request, the target node using the affinity score after updating the affinity score; and presenting a response using the target node.
The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes receiving transaction data indicative of a transaction, generating one or more n-grams based on the transaction data, receiving a dictionary that comprises one or more lists of probability values comprising respective lists of probability values associated with the one or more n-grams, computing, for each respective vendor of the one or more vendors, a vendor probability value with respect to the transaction based on the one or more lists, and recommending a vendor for the transaction to a user based on the vendor probability value with respect to the transaction for each respective vendor of the one or more vendors.
Certain aspects of the present disclosure provide techniques for executing structured query language queries having a schema associated therewith against an application programming interface using natural language. The schema can be chunked such that embeddings of the resulting chunks are stored in a vector store. Schemas (or subschemas) generated using on or more chunks of the vector store may be provided to a large language model along with a NL query to generate a structured query language query which may be executed against the application programming interface. This allows large language models to produce structured query language queries, such as GraphQL queries even if a GraphQL schema is too large to be provided to the model as context. Aspects disclosed herein also provide techniques for client code generation and client software development kit generation.
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Downloadable computer software for tax planning, tax calculation, and tax return preparation and filing; downloadable computer software for organizing, tracking and reporting tax deductible expenses; downloadable computer software for tracking charitable donations and calculating charitable tax deductions and fair market value of goods and services; downloadable computer software for use in providing assistance with regard to accounting and taxes (1) Consultation and assessment services with regard to taxes, namely, tax calculation, tax planning, tax return preparation, tax consultation, tax assessment, tax refund, tax management, preparation of tax forms, and tax return filing; providing information concerning taxes, tax calculation, tax planning, tax return preparation, tax refunds, tax management, preparation of tax forms, and tax returns filing; providing tax calculation, planning, preparation, and filing services; electronic tax filing services; consultation, analysis, and assistance services with regard to aggregating wage, interest, dividend, and other income and expense information from a wide variety of sources for tax related purposes; consultation, analysis, and assistance services with regard to organizing, tracking and reporting tax deductible expenses
(2) Providing temporary use of online non-downloadable software for tax planning, tax calculation, and tax return preparation and filing; providing temporary use of online non-downloadable software for organizing, tracking and reporting tax deductible expenses; providing temporary use of online non-downloadable software for tracking charitable donations and calculating charitable tax deductions and fair market value of goods and services; providing temporary use of online non-downloadable software for use in providing assistance with regard to accounting and taxes
12.
FRAMEWORK AGNOSTIC SUMMARIZATION OF MULTI-CHANNEL COMMUNICATION
Aspects of the present disclosure provide techniques for improved automated parsing and display of electronic documents. Embodiments include identifying a set of topics in a first electronic document based on one or more rules related to one or more keywords in the first electronic document. Embodiments include providing one or more inputs to a machine learning model based on the set of topics and a second electronic document related to the first electronic document. Embodiments include receiving, from the machine learning model in response to the one or more inputs, one or more outputs related to formatting the second electronic document for display. Embodiments include generating a formatted version of the first electronic document based on the set of topics and generating a formatted version of the second electronic document based on the one or more outputs.
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for tax planning, tax calculation, and tax return preparation and filing; downloadable computer software for organizing, tracking and reporting tax deductible expenses; downloadable computer software for tracking charitable donations and calculating charitable tax deductions and fair market value of goods and services; downloadable computer software for use in providing assistance with regard to accounting and taxes Consultation and assessment services with regard to taxes, namely, tax calculation, tax planning, tax return preparation, tax consultation, tax assessment, tax refund, tax management, preparation of tax forms, and tax return filing; providing information concerning taxes, tax calculation, tax planning, tax return preparation, tax refunds, tax management, preparation of tax forms, and tax returns filing; providing tax calculation, planning, preparation, and filing services; electronic tax filing services; consultation, analysis, and assistance services with regard to aggregating wage, interest, dividend, and other income and expense information from a wide variety of sources for tax related purposes; consultation, analysis, and assistance services with regard to organizing, tracking and reporting tax deductible expenses Providing temporary use of online non-downloadable software for tax planning, tax calculation, and tax return preparation and filing; providing temporary use of online non-downloadable software for organizing, tracking and reporting tax deductible expenses; providing temporary use of online non-downloadable software for tracking charitable donations and calculating charitable tax deductions and fair market value of goods and services; providing temporary use of online non-downloadable software for use in providing assistance with regard to accounting and taxes
14.
Efficient automatic web scraping systems and methods
A processor may identify a plurality of data sets subject to upcoming update processing in a next update cycle. For each of the plurality of data sets, the processor may determine a probability that data included in the data set has changed since a most recent update processing. The processor may exclude a first subset of the plurality of data sets having respective probabilities below a threshold value from the upcoming update processing until the respective probabilities are determined again in a subsequent update cycle. The processor may perform the upcoming update processing on the plurality of the data sets not included in the first subset, where the upcoming update processing may include obtaining updated data from at least one external data source.
Machine learning based approach for recommending different categories of tax deductible expenses and related examples of tax deductible expenses for each category
A method for automatically recommending to a user of a software application one or more categories of a plurality of different categories of tax deductible expenses includes providing input data to a trained machine learning model and receiving output from the trained machine learning model based on the input data. The output includes a recommendation for the user that includes (i) one or more categories of the plurality of different categories of tax deductible expenses; and (ii) a plurality of examples of tax deductible expenses for each of the one or more categories. The method includes receiving feedback from the user on the recommendation and generating updated training data for the trained machine learning model based on the feedback.
A method includes receiving a data stream comprising content generated by an application executing on a user device. The data stream is received from a guidance service that is separate from the application. The data stream is processed using a set of machine leaming models to identify a first set of artifacts within the content. A first state of the application is identified based on the first set of artifacts. First transition data is identified in a logic flow of the application. The first transition data corresponds to transitioning from the first state to a second state of the application. Based on the first transition data, first guidance data is generated that describes user input for transitioning the application from the first state to the second state. The first guidance data is sent to the user device, where it is separately presented from the application by the guidance service.
A method implements private categorization using shared keys. The method includes selecting an encryption key, encrypting a transaction vector, generated from a transaction record, with the encryption key to generate an encrypted transaction vector, and receiving an encrypted category vector generated by a classifier model, corresponding to the encryption key, from the encrypted transaction vector. The method further includes decrypting a category from the encrypted category vector with a decryption key corresponding to the encryption key and presenting the category.
A method and system that proactively generate alerts for updating a scraping script to avoid scraping script errors. A predetermined number of webpages targeted by the scraping script are randomly sampled. The scraping script is appended to each webpage in the sample. A structured list of text fragments across the webpages with the appended script is generated. At predetermined time intervals, a fresh set of webpages is sampled, the scraping script is appended to the webpages, and a new structured list is generated. If the new structured list and the previous structured list do not match, the webpages may have been changed and the scraping script may have to be updated. An alert is generated indicating that such update is required and may include a location of the mismatch. Therefore, scraping script errors are proactively detected and can be rectified before an actual error occurs and propagates.
A method of customizing a personal software program for a user, comprising collecting attributes of each of a plurality of users registered to access the personal software program, generating a plurality of user profiles based on the collected attributes of the users, monitoring the interactions or non-interactions of the users with a module of the personal software program, deriving a reference user profile from the plurality of user profiles, linking the module with the reference user profile based on the monitored interactions or non-interactions of the users with the module, acquiring attributes of the user, generating a user profile based on the acquired attributes of the user, comparing the user profile to the reference user profile, determining a match between the user profile and the reference user profile based on the comparison, and setting the availability of the module to the user.
In one or more embodiments, transaction data between multiple users and multiple merchants is retrieved. The retrieved transaction data is aggregated for each of the multiple users and each of the multiple merchants. The aggregated data may then be normalized. An example normalization process may include income normalization, where a user's total transaction amount at a particular merchant is normalized by the user's income. Other forms of normalization may also be employed. Using the normalized data, user-merchant affinity may be predicted based on collaborative filtering models, cascading tree models, and or cosine similarity models. A recommendation engine may provide personalized advertisements based on the predicted affinity. Because of the normalization of the data, the affinity and therefore the recommendation is less biased toward larger merchants.
Methods, computer systems and computer program product are provided for retrieving contextually relevant documents in near real time. When text data it's received from an application, the text data is processed through a text segmentation model to generate a set of documents. Each document corresponds to a segment of the text data. A first vector representation is generated for a first document of the set of documents. A machine learning process compares the first vector representation and a set of vector representations for a set of documents within a data repository to determine a subset of the documents. A composite rank is generated for each respective document of the subset. The subset of documents is then presented through an interface, sorted according to the respective composite ranks.
Certain aspects of the disclosure pertain to inferring a candidate entity associated with a transaction with a machine learning model. An organization identifier and description associated with a transaction can be received as input. In response, an entity embedding, comprising a vector for each entity of an organization based on the organization identifier, can be retrieved from storage. A machine learning model can be invoked with the entity embedding and description. The machine learning model can be trained to infer a transaction embedding from the description and compute a similarity score between the transaction embedding and each vector of the entity embedding. A candidate entity with a similarity score satisfying a threshold can be identified and returned. The candidate entity with the highest similarity score can be identified in certain aspects.
Certain aspects of the present disclosure provide techniques for orchestrating a user experience using natural language input. A user experience is orchestrated within an ecosystem of features for which a plurality of corresponding tokens is defined. Natural language describing a desired user experience result is received by a user experience orchestrator. A sequence of tokens corresponding to operations belonging to an ecosystem of features which produce a correct result for the natural language input can be identified by a trained large language model and executed by the user experience orchestrator using a token operator. The output operations determined by the model to produce or be likely to produce the correct result based on the natural language input can be disambiguated, confirmed, and/or executed.
Certain aspects of the present disclosure provide techniques for managing the transmission of mixed-modality messages using machine learning models. An example method generally includes generating, using a first machine learning model, an embedding representation of a mixed-modality message. The mixed-modality message is classified as an effective message or an ineffective message using a second machine learning model and the embedding representation of the mixed-modality message. One or more actions are taken to manage transmission of the mixed-modality message based on the classifying the mixed-modality message as an effective message or an ineffective message.
Certain aspects of the present disclosure provide techniques for adjusting access control policies of access controlled systems, such as techniques for identifying a vulnerability or for identifying parameters and values achieving a specified result from a system whose access is controlled by the policy. Requests to the system can be made using a testing system that executes test scripts using avatars having various parameter types and values. The avatar information and results of the test scripts are provided as training data to a machine learning model training system to generate a model that provides recommendations for parameter types and values likely to achieve a particular result. The recommendations are used to execute the test script to determine results including a rate of success for the recommended parameters and/or values. Various actions, such as adjusting or adding a rule to an access control policy, can be performed based on the results.
A computer-implemented method includes receiving training data that includes a plurality of API requests from a plurality of client devices. The method includes generating a plurality of permissible API sessions based on the training data. Each of the permissible API sessions is associated with a corresponding client device of the plurality of client devices and includes a sequence of API requests originating from the corresponding client device. The method includes applying a sequence embedding technique to the plurality of permissible API sessions to generate a plurality of embeddings and applying a dimensionality reduction technique to the plurality of embedding to generate a plurality of compact embeddings. The method includes storing each of the compact embeddings in a space partitioning data structure at storage locations within the space partitioning data structure that are determined based on similarities between the compact embeddings.
G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
G06F 21/54 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by adding security routines or objects to programs
G06F 21/55 - Detecting local intrusion or implementing counter-measures
27.
Use of semantic confidence metrics for uncertainty estimation in large language models
A method including receiving a user input for input to a language processing machine learning model (MLM). The method also includes generating modified inputs that are based on, and semantically related to, the user input. The method also includes executing the MLM to generate model outputs of the MLM. The MLM takes as input instances of each of the modified inputs. The method also includes sampling the model outputs using a statistical sampling strategy to generate sampled model outputs. The method also includes clustering the sampled model outputs into clusters. Each cluster of the clusters represents a distinct semantic meaning of the sampled model outputs. The method also includes generating a confidence metric for the user input. The confidence metric includes a predictive entropy of the clusters. The method also includes routing the user input based on whether the confidence metric satisfies or fails to satisfy a threshold value.
Systems and methods are disclosed for switching between batch processing and real-time processing of time series data, with a system being configured to switch between a batch processing module and a real-time processing module to process time series data. The system includes an orchestration service to indicate when to switch, which may be based on a switching event identified by the orchestration service. In some implementations, the orchestration service identifies a switching event in incoming time series data to be processed. When a batch processing module is to be used to batch process time series data, the real-time processing module may be disabled, with the real-time processing module being enabled when it is used to process the time series data. In some implementations, the real-time processing module includes the same processing models as the batch processing module such that the two modules' outputs have a similar accuracy.
Certain aspects of the present disclosure provide techniques for predicting activity within a software application using a machine learning model. An example method generally includes generating a multidimensional time-series data set from time-series data associated with activity within a software application. The multidimensional time-series data set generally includes the time-series data organized based on a plurality of time granularities. Using a machine learning model and the generated multidimensional time-series data set, activity within the software application is predicted for one or more time granularities of the plurality of time granularities. Computing resources are allocated to execute operations using the software application based on the predicted activity within the software application.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Software for artificial intelligence processing and machine learning; software for use in processing and generating natural language queries; software for knowledge engineering; software for generative artificial intelligence applications; software using artificial intelligence for the production of speech, text, images, video, sound, haptics, and numerical calculations; software using artificial intelligence for rendering and navigating virtual reality environments; software for multi-modal machine-learning based language-, text-, speech-, image-, video-, sound-, haptic- and numerical-calculation-processing software; artificial intelligence software for facilitating interaction and communication between humans and machines, humans and software, humans and other humans, machines and other machines, software and other software, and between and among machines and software applications and operating systems; software for facilitating multi-modal natural language, speech, text, numerical, image, video, sound and haptic input; software featuring artificial intelligence for use in processing voice and physical commands, searching databases, and creating audio, images, textual and numeric responses to user-specific inquiries, including via chatbot interfaces; software for use in predictive analytics, recommendations, personalization, automatic data understanding, automatic rules generation, and automatic software code generation; software for enabling sentiment analysis; software tools for use in software development; software for automation of business and personal tasks; software which takes automated actions on behalf of a user, a machine, or application or operating system software; software for automated analysis of data and for producing automated insights; software for automated analysis of large datasets to enable decision-making; software for producing automated reminders and recommendations; software for automatically categorizing data; software for automatically analyzing data to enable reporting, behavioral analysis and trend analysis; downloadable computer software for accounting, bookkeeping, tax planning, tax calculation, tax return preparation and filing, personal finance, business finance, business management, financial and business process management, inventory management, operation management, retail management, financial planning, budgeting, forecasting, enterprise resource planning (ERP), project cost management, tax management, human resource management, payroll services, management of benefit plans, insurance plans, retirement plans, unemployment insurance plans, and health care plans, tracking time worked by employees and subcontractors, customer relationship management (CRM), transaction processing, point of sale transactions, invoicing, credit card payment processing, online banking, email marketing and automation, and marketing services, namely, supporting and managing a marketing platform for the creation, sending, optimizing and targeting of bulk electronic mail, advertising and promotional campaigns and surveys, and for promotion of ecommerce web sites (1) Providing online non-downloadable software for artificial intelligence processing and machine learning; providing online non-downloadable software for use in processing and generating natural language queries; providing online non-downloadable software for knowledge engineering; providing online non-downloadable software for generative artificial intelligence applications; providing online non-downloadable software using artificial intelligence for the production of speech, text, images, video, sound, haptics, and numerical calculations; providing online non-downloadable software using artificial intelligence for rendering and navigating virtual reality environments; providing online non-downloadable software for multi-modal machine learning based language-, text-, speech-, image-, video-, sound-, haptic- and numerical-calculation processing software; providing online non-downloadable artificial intelligence software for facilitating interaction and communication between humans and machines, humans and software, humans and other humans, machines and other machines, software and other software, and between and among machines and software applications and operating systems; providing online non-downloadable software for facilitating multi-modal natural language, speech, text, numerical, image, video, sound and haptic input; application service provider (ASP) services featuring application programming interface (API) software featuring artificial intelligence and machine learning features and capabilities; providing online non-downloadable software featuring artificial intelligence for use in processing voice and physical commands, searching databases, and creating audio, images, textual and numeric responses to user-specific inquiries, including via chatbot interfaces; providing online non-downloadable software for use in predictive analytics, recommendations, personalization, automatic data understanding, automatic rules generation, and automatic software code generation; providing online non-downloadable software for enabling sentiment analysis; providing online non-downloadable software tools for use in software development; providing online non-downloadable software for automation of business and personal tasks; providing online non-downloadable software which takes automated actions on behalf of a user, a machine, or application or operating system software; providing online non-downloadable software for automated analysis of data and for producing automated insights; providing online non-downloadable software for automated analysis of large datasets to enable decision-making; providing online non-downloadable software for producing automated reminders and recommendations; providing online non-downloadable software for automatically categorizing data; providing online non-downloadable software for automatically analyzing data to enable reporting, behavioral analysis and trend analysis; providing temporary use of online non-downloadable software for accounting, bookkeeping, tax planning, tax calculation, tax return preparation and filing, personal finance, business finance, business management, financial and business process management, inventory management, operation management, retail management, financial planning, budgeting, forecasting, enterprise resource planning (ERP), project cost management, tax management, human resource management, payroll services, management of benefit plans, insurance plans, retirement plans, unemployment insurance plans, and health care plans, tracking time worked by employees and subcontractors, customer relationship management (CRM), transaction processing, point of sale transactions, invoicing, credit card payment processing, online banking, email marketing, and marketing services, namely, supporting and managing a marketing platform for the creation, sending, optimizing and targeting of bulk electronic mail, advertising and promotional campaigns and surveys, and for promotion of e-commerce web sites
A method comprising generating, during multiple user sessions of a first user with a software application, first clickstream data from the multiple user sessions, and extracting, from the first clickstream data, a first plurality of task instances of the first user performing a first plurality of tasks. The method also includes decomposing, from the first clickstream data, each task instance of the first plurality of task instances into a first plurality of steps to obtain a first plurality of decomposed task instances. The first plurality of steps in the first plurality of decomposed task instances are each associated with a timestamp. The method further includes training a first user model with the first plurality of decomposed task instances to learn a user optimal order to perform the first plurality of tasks and presenting, to the first user, the user optimal order to perform the first plurality of tasks.
Certain aspects of the present disclosure provide techniques for medoid-based data compression. One example method generally includes receiving item data indicative of one or more items, determining one or more medoids based on the item data, determining, for each item of the one or more items, a corresponding medoid based on the one or more medoids, identifying, for each item of the one or more items, a difference between the item and the corresponding medoid for the item, storing the one or more medoids, and storing, for each item of the one or more items, the identified difference between the item and the corresponding medoid.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software for artificial intelligence processing and machine learning; software for use in processing and generating natural language queries; software for knowledge engineering; software for generative artificial intelligence applications; software using artificial intelligence for the production of speech, text, images, video, sound, haptics, and numerical calculations; software using artificial intelligence for rendering and navigating virtual reality environments; software for multi-modal machine-learning based language-, text-, speech-, image-, video-, sound-, haptic- and numerical-calculation-processing software; artificial intelligence software for facilitating interaction and communication between humans and machines, humans and software, humans and other humans, machines and other machines, software and other software, and between and among machines and software applications and operating systems; software for facilitating multi-modal natural language, speech, text, numerical, image, video, sound and haptic input; software featuring artificial intelligence for use in processing voice and physical commands, searching databases, and creating audio, images, textual and numeric responses to user-specific inquiries, including via chatbot interfaces; software for use in predictive analytics, recommendations, personalization, automatic data understanding, automatic rules generation, and automatic software code generation; software for enabling sentiment analysis; software tools for use in software development; software for automation of business and personal tasks; software which takes automated actions on behalf of a user, a machine, or application or operating system software; software for automated analysis of data and for producing automated insights; software for automated analysis of large datasets to enable decision-making; software for producing automated reminders and recommendations; software for automatically categorizing data; software for automatically analyzing data to enable reporting, behavioral analysis and trend analysis; downloadable computer software for accounting, bookkeeping, tax planning, tax calculation, tax return preparation and filing, personal finance, business finance, business management, financial and business process management, inventory management, operation management, retail management, financial planning, budgeting, forecasting, enterprise resource planning (ERP), project cost management, tax management, human resource management, payroll services, management of benefit plans, insurance plans, retirement plans, unemployment insurance plans, and health care plans, tracking time worked by employees and subcontractors, customer relationship management (CRM), transaction processing, point of sale transactions, invoicing, credit card payment processing, online banking, email marketing and automation, and marketing services, namely, supporting and managing a marketing platform for the creation, sending, optimizing and targeting of bulk electronic mail, advertising and promotional campaigns and surveys, and for promotion of ecommerce web sites. Providing online non-downloadable software for artificial intelligence processing and machine learning; providing online non-downloadable software for use in processing and generating natural language queries; providing online non-downloadable software for knowledge engineering; providing online non-downloadable software for generative artificial intelligence applications; providing online non-downloadable software using artificial intelligence for the production of speech, text, images, video, sound, haptics, and numerical calculations; providing online non-downloadable software using artificial intelligence for rendering and navigating virtual reality environments; providing online non-downloadable software for multi-modal machine learning based language-, text-, speech-, image-, video-, sound-, haptic- and numerical-calculation processing software; providing online non-downloadable artificial intelligence software for facilitating interaction and communication between humans and machines, humans and software, humans and other humans, machines and other machines, software and other software, and between and among machines and software applications and operating systems; providing online non-downloadable software for facilitating multi-modal natural language, speech, text, numerical, image, video, sound and haptic input; application service provider (ASP) services featuring application programming interface (API) software featuring artificial intelligence and machine learning features and capabilities; providing online non-downloadable software featuring artificial intelligence for use in processing voice and physical commands, searching databases, and creating audio, images, textual and numeric responses to user-specific inquiries, including via chatbot interfaces; providing online non-downloadable software for use in predictive analytics, recommendations, personalization, automatic data understanding, automatic rules generation, and automatic software code generation; providing online non-downloadable software for enabling sentiment analysis; providing online non-downloadable software tools for use in software development; providing online non-downloadable software for automation of business and personal tasks; providing online non-downloadable software which takes automated actions on behalf of a user, a machine, or application or operating system software; providing online non-downloadable software for automated analysis of data and for producing automated insights; providing online non-downloadable software for automated analysis of large datasets to enable decision-making; providing online non-downloadable software for producing automated reminders and recommendations; providing online non-downloadable software for automatically categorizing data; providing online non-downloadable software for automatically analyzing data to enable reporting, behavioral analysis and trend analysis; providing temporary use of online non-downloadable software for accounting, bookkeeping, tax planning, tax calculation, tax return preparation and filing, personal finance, business finance, business management, financial and business process management, inventory management, operation management, retail management, financial planning, budgeting, forecasting, enterprise resource planning (ERP), project cost management, tax management, human resource management, payroll services, management of benefit plans, insurance plans, retirement plans, unemployment insurance plans, and health care plans, tracking time worked by employees and subcontractors, customer relationship management (CRM), transaction processing, point of sale transactions, invoicing, credit card payment processing, online banking, email marketing, and marketing services, namely, supporting and managing a marketing platform for the creation, sending, optimizing and targeting of bulk electronic mail, advertising and promotional campaigns and surveys, and for promotion of e-commerce web sites
34.
System and method for feature aggregation for tracking anonymous visitors
Systems and methods for tracking anonymous visitors of an online website or mobile app are disclosed. The browsing activity by an anonymous visitor of the online website or mobile app is converted into features and a visitor-identifier associated with the browsing activity generated by the anonymous visitor is determined. The features are stored with the visitor-identifier in a super-visitor-state before the visitor-identifier is associated with a super-visitor-identifier. After the visitor-identifier is associated with the super-visitor-identifier, the features are stored with the super-visitor-identifier in the super-visitor-state. After the visitor-identifier is associated with the super-visitor-identifier, the features associated with the visitor-identifier in the super-visitor-state may be combined, e.g., aggregated, with the features associated with the super-visitor-identifier and the visitor-identifier may be removed from the super-visitor-state.
Certain aspects of the present disclosure provide techniques for intelligent grouping of travel data for review through a user interface. In one example, a method for providing grouped travel data to a user interface of an application includes receiving travel data from an application running on a remote device; generating one or more travel data-based features from the travel data thereby creating featurized travel data; applying a pattern mining technique to the featurized travel data to detect a plurality of patterns in the featurized travel data; for each trip record in the featurized travel data: determining a plurality of trip record groups in which the trip record falls based on the plurality of patterns; and adding the trip record to a trip record group of the plurality of trip record groups according to a prioritization scheme; and transmitting the trip record group to the application running on the remote device.
The present disclosure provides techniques for detecting and correcting outliers in categories of transactions. One example method includes receiving electronic transaction data indicative of one or more current transactions, wherein the one or more current transactions are associated with a user of a software application, identifying, for each transaction of the one or more transactions, a category using a first machine learning model, computing a distribution for each category of a plurality of categories of the user, identifying, a particular category of the user as an anomalous category, based on the distribution for the particular category of the user and corresponding distributions for the particular category of other users, and updating a category assigned to one or more transactions such that a delta between a value relating to the anomalous category of the user and corresponding values relating to the particular category of the other users is reduced.
Certain aspects of the present disclosure provide techniques for managing a search engine based on search performance metrics. An example method generally includes dividing a set of search history data into a first subset of search history data and a second subset of search history data. The first subset of data is associated with interaction with search results, and the second subset of data is associated with non-interaction with search results. A first quality score is generated for searches in the first subset of data. A second quality score is generated for searches in the second subset of data based on different search intents identified for each temporally related group in the second subset of data. An overall quality score is generated for a search engine, and one or more actions with respect to the search engine are taken based on the overall quality score.
Systems and methods dynamically extracting n-grams for automated vocabulary updates. Text is received. An n-gram extracted from the text is matched to a canonical n-gram from a vocabulary to identify a tag for the text. An n-gram weight is computed for the n-gram extracted from the text. The n-gram weight may be computed by adjusting a term frequency of the n-gram. A relevancy score is computed for the tag using the n-gram weight and using an n-gram frequency of the canonical n-gram. The relevancy score is computed by dividing the n-gram weight by a value proportional to the n-gram frequency of the canonical n-gram. The relevancy score of the n-gram is presented.
A server computer hosting an extended reality world receives a first transmission over a communication network from a computing device associated with a user, the first transmission including a request for the user to access the extended reality world. The server computer transmits a presentation of the extended reality world to the communication device over the communications network based at least in part on the request, and displays the presentation of the extended reality world on the computing device, where the presentation includes at least an avatar associated with the user. The server computer receives a command for the avatar to store a phrase selected by the user in a location associated with a virtual object within the extended reality world. The server computer displays, within the presentation of the extended reality world on the computing device, the avatar storing the phrase at the location in the extended reality world.
Incoming data requests from the perspective of the data lake, are gathered and analyzed to determine the usage of the data. Using the perspective of the data lake avoids the technical challenge of analyzing data usage by different computation points, which are at different locations, perform hard-to-track different operations, and are often reachable only through complicated access protocols. Another technical challenge of mapping between an object path and a table path is solved by generating object path datasets and table path datasets at different levels of abstractions. A comparison is performed, iteratively, from a lower level of granularity and the granularity is increased in the progressive steps. Matches from iterations are unionized to generate a final matching data. Observability metrics are generated using final matching data and are used to perform downstream operations such as controlling data table access, moving data tables to cold storage, decommissioning unused pipelines, etc.
Certain aspects of the present disclosure provide techniques for efficient data parity. Embodiments include receiving, by a first data consuming component, from a publication service, a plurality of database change records indicating changes to an underlying data source. Embodiments include updating, by the first data consuming component, a secondary data store based on the plurality of database change records. Embodiments include selecting a sample subset of the plurality of database change records based on one or more conditions. Embodiments include, for each respective database change record in the sample subset that has already been consumed by a second data consuming component that updated a primary data store based on one or more database change records: comparing the respective database change record to first data from the primary data store that corresponds to the respective database change record and making a parity determination based on the comparing.
Certain aspects of the present disclosure provide techniques for generating a user interface to prompt users of a software application to perform an action in the software application. The method generally includes generating historical transaction time gap data for transactions in the account. A probability distribution is generated based on the historical time gap data. The probability distribution represents a probability that a transaction related to the account has been performed after an elapsed time from a previous transaction. A probability that an unrecorded transaction exists for an account based on the probability distribution and a time difference between a most recent transaction and a current time. The probability that an unrecorded transaction exists is determined to exceed a threshold probability, and a user interface is generated and displayed to a user of the software application including a prompt for the user to enter new transactions for the account.
A system and method for extracting data from a piece of content using spatial information about the piece of content. The system and method may use a conditional random fields process or a bidirectional long short term memory and conditional random fields process to extract structured data using the spatial information.
The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes receiving electronic transaction data indicative of one or more transactions, identifying, from the one or more transactions, a subset of transactions that are associated with for known attribute values with respect to one or more unique recipients, computing, for each unique provider of the one or more unique providers, a provider feature based on the known attribute values with respect to a subset of the one or more associated unique recipients, computing, for a given recipient indicated in one or more given transactions that are not included in the subset of transactions, a recipient feature based on the provider feature of each unique provider of the one or more associated unique providers, and predicting, based on the recipient feature, a value for the attribute with respect to the given recipient.
A computer-implemented method includes receiving training data including a plurality of API requests from a plurality of client devices. The method includes generating a plurality of permissible API sessions based on the training data. The method includes applying a sequence embedding technique to the plurality of permissible API sessions to generate a plurality of embeddings. The method includes applying a dimensionality reduction technique to the plurality of embeddings to generate a plurality of compact embeddings. The method includes applying a clustering technique to the plurality of compact embeddings to determine a plurality of different clusters of the compact embeddings. The method includes generating a plurality of patterns based on the plurality of different clusters. Each of the plurality of patterns is descriptive of permissible API sessions associated with a corresponding cluster of the plurality of different clusters.
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Education services for primary and secondary school students, namely, providing games and challenges in the field of personal finance; Education and entertainment services, namely providing online video games for primary and secondary school students in the field of personal finance; Providing an on-line computer game for primary and secondary school students in the field of personal finance. Providing temporary use of online non-downloadable software in the nature of games for primary and secondary school students in the field of finance.
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing classes and software-based instruction for primary and secondary school students in the nature of personal finance Temporary use of online non-downloadable software in the nature of online classes and software-based instruction for primary and secondary school students about personal finance; Software as a Service (SaaS) featuring software for accessing, viewing, and learning from interactive content in the nature of articles, videos, and online activities in the field of personal finance for primary and secondary school students; Software as a Service (SaaS) featuring software for accessing, viewing, and learning from streamable and on-demand lectures and workshops in the field of personal finance for primary and secondary school students
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Downloadable computer software for accounting, bookkeeping, personal finance, business finance, business management, financial and business process management, inventory management, operation management, retail management, tax preparation and tax planning, financial planning, budgeting, forecasting, enterprise resource planning (ERP), project cost management, tax management, customer relationship management (CRM), transaction processing, point of sale transactions, credit card payment processing, online banking, business and financial transaction management, and e-commerce software for allowing users to perform electronic business transactions; downloadable computer software for creating, customizing and managing invoices; downloadable computer software for recording payments and issuing receipts; downloadable computer software to analyze the financial status of businesses and industries; downloadable computer software to manage customer lists, e-mail and print sales forms, and track running balances; downloadable computer software for human resource management, payroll services, and management of benefit plans, insurance plans, retirement plans, unemployment insurance plans, and health care plans; downloadable computer software for calculating and charging sales tax, creating reports to pay sales tax to tax agencies, and creating, printing, exporting, tracking, and emailing purchase orders and financial reports; downloadable computer software for tracking income, expenses, sales, and profitability; downloadable computer software for tracking time worked by employees and subcontractors; downloadable computer software for importing contacts and financial data from other electronic services and software; downloadable computer software for synchronizing data among computers and mobile devices; downloadable computer software for database management, data aggregation, data reporting, and data transmission; downloadable computer software for online backup of electronic files (1) Online business management services in the field business finance; online accounting and bookkeeping services; providing payroll preparation, payroll tax assessment, and payroll tax filing services
(2) Online credit card transaction processing services; payroll tax debiting services; online bill payment services
(3) Providing temporary use of online non-downloadable software for accounting, bookkeeping, personal finance, business finance, business management, financial and business process management, inventory management, operation management, retail management, tax preparation and tax planning, financial planning, budgeting, forecasting, enterprise resource planning (ERP), project cost management, tax management, customer relationship management (CRM), transaction processing, point of sale transactions, credit card payment processing, online banking, business and financial transaction management, and e-commerce software for allowing users to perform electronic business transactions; providing temporary use of online non-downloadable software for creating, customizing and managing invoices; providing temporary use of online non-downloadable software for recording payments and issuing receipts; providing temporary use of online nondownloadable software to analyze the financial status of businesses and industries; providing temporary use of online non-downloadable software to manage customer lists, e-mail and print sales forms, and track running balances; providing temporary use of online non-downloadable software for human resource management, payroll services, and management of benefit plans, insurance plans, retirement plans, unemployment insurance plans, and health care plans; providing temporary use of online non-downloadable software for calculating and charging sales tax, creating reports to pay sales tax to tax agencies, and creating, printing, exporting, tracking, and emailing purchase orders and financial reports; software for tracking income, expenses, sales, and profitability; providing temporary use of online non-downloadable software for tracking time worked by employees and subcontractors; providing temporary use of online non-downloadable software for importing contacts and financial data from other electronic services and software; providing temporary use of online non-downloadable software for synchronizing data among computers and mobile devices; providing temporary use of online non-downloadable software for database management, data aggregation, data reporting, and data transmission; providing temporary use of online non-downloadable software for online backup of electronic files; technical support services, namely, troubleshooting of computer software problems, web sites, online services, web and online application problems, mobile application problems, and network problems; technical support services, namely, help desk services; computer services, namely, synchronizing data among computers and mobile devices; computer technology consultation services; data hosting services; hosting software for use by others for use in managing, organizing and sharing data on computer server on a global computer network
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for accounting, bookkeeping, personal finance, business finance, business management, financial and business process management, inventory management, operation management, retail management, tax preparation and tax planning, financial planning, budgeting, forecasting, enterprise resource planning (ERP), project cost management, tax management, customer relationship management (CRM), transaction processing, point of sale transactions, credit card payment processing, online banking, business and financial transaction management, and e-commerce software for allowing users to perform electronic business transactions; downloadable computer software for creating, customizing and managing invoices; downloadable computer software for recording payments and issuing receipts; downloadable computer software to analyze the financial status of businesses and industries; downloadable computer software to manage customer lists, e-mail and print sales forms, and track running balances; downloadable computer software for human resource management, payroll services, and management of benefit plans, insurance plans, retirement plans, unemployment insurance plans, and health care plans; downloadable computer software for calculating and charging sales tax, creating reports to pay sales tax to tax agencies, and creating, printing, exporting, tracking, and emailing purchase orders and financial reports; downloadable computer software for tracking income, expenses, sales, and profitability; downloadable computer software for tracking time worked by employees and subcontractors; downloadable computer software for importing contacts and financial data from other electronic services and software; downloadable computer software for synchronizing data among computers and mobile devices; downloadable computer software for database management, data aggregation, data reporting, and data transmission; downloadable computer software for online backup of electronic files Online business management services in the field business finance; online accounting and bookkeeping services; providing payroll preparation, payroll tax assessment, and payroll tax filing services Online credit card transaction processing services; payroll tax debiting services; online bill payment services Providing temporary use of online non-downloadable software for accounting, bookkeeping, personal finance, business finance, business management, financial and business process management, inventory management, operation management, retail management, tax preparation and tax planning, financial planning, budgeting, forecasting, enterprise resource planning (ERP), project cost management, tax management, customer relationship management (CRM), transaction processing, point of sale transactions, credit card payment processing, online banking, business and financial transaction management, and e-commerce software for allowing users to perform electronic business transactions; providing temporary use of online non-downloadable software for creating, customizing and managing invoices; providing temporary use of online non-downloadable software for recording payments and issuing receipts; providing temporary use of online non-downloadable software to analyze the financial status of businesses and industries; providing temporary use of online non-downloadable software to manage customer lists, e-mail and print sales forms, and track running balances; providing temporary use of online non-downloadable software for human resource management, payroll services, and management of benefit plans, insurance plans, retirement plans, unemployment insurance plans, and health care plans; providing temporary use of online non-downloadable software for calculating and charging sales tax, creating reports to pay sales tax to tax agencies, and creating, printing, exporting, tracking, and emailing purchase orders and financial reports; software for tracking income, expenses, sales, and profitability; providing temporary use of online non-downloadable software for tracking time worked by employees and subcontractors; providing temporary use of online non-downloadable software for importing contacts and financial data from other electronic services and software; providing temporary use of online non-downloadable software for synchronizing data among computers and mobile devices; providing temporary use of online non-downloadable software for database management, data aggregation, data reporting, and data transmission; providing temporary use of online non-downloadable software for online backup of electronic files technical support services, namely, troubleshooting of computer software problems, web sites, online services, web and online application problems, mobile application problems, and network problems; technical support services, namely, help desk services; computer services, namely, synchronizing data among computers and mobile devices; computer technology consultation services; data hosting services; hosting software for use by others for use in managing, organizing and sharing data on computer server on a global computer network
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Education services for primary and secondary school students, namely, providing games and challenges in the field of personal finance; Education and entertainment services, namely providing online video games for primary and secondary school students in the field of personal finance; Providing an on-line computer game for primary and secondary school students in the field of personal finance
(2) Providing temporary use of online non-downloadable software in the nature of games for primary and secondary school students in the field of finance
51.
METHOD AND SYSTEM FOR SCALABLE PERFORMANCE TESTING IN CLOUD COMPUTING ENVIRONMENTS
Certain embodiments of the present disclosure provide techniques for performing performance tests against services in a computing environment. The method generally includes deploying application code to an application namespace hosted on a first set of resources in the computing environment. Testing code is deployed to an infrastructure namespace hosted on a second set of resources in the computing environment. A request to test the application code is received. The request generally includes information identifying a load to be generated in testing the application code. A plurality of container instances implementing the test code are instantiated based on the identified load to be generated to test the application code. A test is executed against the application code through the instantiated plurality of container instances.
Systems and methods for user authentication are disclosed. An example method includes receiving a request for access to a first secured service, the request corresponding to a first user, determining whether or not the request for access is valid, in response to determining that the request for access is valid, determining whether or not the first user has successfully performed a secondary authentication within a predetermined time period of the request for access, and in response to determining that the first user has successfully performed the secondary authentication within the predetermined time period of the valid request for access, providing the first user with access to the secured service.
An apparatus may include a database and a processor in communication with at least one network. The processor may be configured to instantiate an application stack comprising a data missing detector, a data source router, and a response handler. The data missing detector may be configured to receive a request for data and determine whether the data is available from the database. The data source router may be configured to identify an alternate database from which to obtain the data when the data is not available from the database and route the request for data through the at least one network to an alternate processor associated with the alternate database. The response handler may be configured identify responsive data to a routed response received from another processor in the database and send the responsive data to a requesting device. Multiple apparatuses may form a distributed system.
G06F 11/20 - Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements
Augmented Denoising Diffusion Implicit Models (“DDIMs”) using a latent trajectory optimization process can be used for image generation and manipulation using text input and one or more source images to create an output image. Noise bias and textual bias inherent in the model representing the image and text input is corrected by correcting trajectories previously determined by the model at each step of a diffusion inversion process by iterating multiple starts the trajectories to find determine augmented trajectories that minimizes loss at each step. The trajectories can be used to determine an augmented noise vector, enabling use of an augmented DDIM and resulting in more accurate, stable, and responsive text-based image manipulation.
Certain aspects of the present disclosure provide techniques and systems for screening chat attachments. A chat attachment screening system monitors a chat window of a first computing device associated with a first user during an interaction session between the first user and a second user. An upload of an attachment is detected based on the monitoring. Access to the attachment from a second computing device associated with the second user is blocked, in response to detecting the upload. Content from the attachment is identified and extracted. A type of the attachment is determined based on the content. A determination is made as to whether the second user is authorized to access the type of the attachment. An indication of the determination is presented on at least one of the first computing device or the second computing device during the interaction session.
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
H04L 51/08 - Annexed information, e.g. attachments
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
Systems and methods may predict whether a user will abandon an application. Initially, different features are extracted from a time series of numerical values rendered by the application. A machine learning model is trained using a supervised approach on the extracted features to map the known and labeled outputs. In this supervised approach, the output may be binary with a “0”-label for a user that has left the application in the middle of a task and a “1”-label for the user who has used the application to finish the task. During the deployment, the trained model may be called to predict whether the user will abandon the application based on time series of numerical values retrieved in real time. If an abandonment is predicted, a customized message is generated and presented on the user's device.
A method implements anonymous uncensorable cryptographic chains. The method includes receiving, from a first application, verifiable data for a current record and unverified data for the current record. The unverified data for the current record was received by the first application from a second application. The method further includes verifying the verifiable data for the current record with unverified data from a previous record. The method further includes recording the verifiable data for the current record and the unverified data for the current record to the current record responsive to verifying the verifiable data for the current record. The method further includes presenting the current record to one or more of the first application and to the second application.
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
A method, computer program product, and system are provided. A first natural language text is received via a user interface. A generative pretrained transformer machine learning model processes the first natural language text and a context to identify a first intent. The processing is based in part on a syntax determined from a set of natural language completion paradigms. The generative transformer machine learning model maps the first set of parameters to a first query. The mapping is associated with a first confidence. The generative transformer machine learning model processes the first set of parameters and the first query to generate a set of execution steps. The processing is performed when the first confidence satisfies a threshold. The set of execution steps is parsed into a query object that is forwarded to a reporting service.
A visual-based classification model influenced by text features as a result of the outputs of a text-based classification model is disclosed. A system receives one or more documents to be classified based on one or more visual features and provides the one or more documents to a student classification model, which is a visual-based classification model. The system also classifies, by the student classification model, the one or more documents into one or more document types based on one or more visual features. The one or more visual features are generated by the student classification model that is trained based on important text identified by a teacher classification model for the one or more document types, with the teacher classification model being a text-based classification model. Generating training data and training the student classification model based on the training data are also described.
Aspects of the present disclosure provide techniques for machine learning and rules integration. Embodiments include receiving input values corresponding to a subset of a set of input variables associated with an automated determination. Embodiments include generating a directed acyclic graph (DAG) representing a set of constraints corresponding to the set of input variables. The set of constraints relate to one or more machine learning models and one or more rules. Embodiments include receiving one or more outputs from the one or more machine learning models based on one or more of the input values. Embodiments include determining outcomes for the one or more rules based on at least one of the input values. Embodiments include populating the DAG based on the input values, the one or more outputs, and the outcomes. Embodiments include making the automated determination based on logic represented by the DAG.
Certain aspects of the present disclosure provide techniques and systems for automatically detecting, tracking, and processing certain information content, based on voice input from a user. A voice enabled content tracking system receives natural language content corresponding to audio input from a user. A determination is made as to whether the natural language content includes a first type of information, based on evaluating the natural language content with a first machine learning model. In response to determining the natural language content comprises the first type of information, a temporal association of the first type of information is determined, based on evaluating the natural language content with a second machine learning model, and a message including an indication of the temporal association of the first type of information is transmitted to the user.
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.
Systems and methods for automated techniques that generate queryable database table ownership attribution information in real-time. In addition to generating ownership attribution information, system and methods provide a novel framework for creating bi-partite graphs and generating insightful graph data.
Systems and methods for transforming a data visualization are disclosed. An example method includes presenting the data visualization on a first page of a display of the computing device, the data visualization representing at least a first portion of a data set, receiving a visualization transformation command from a user, in response to receiving the visualization transformation command, navigating to a second page of the display, and, during the navigation, transforming the data visualization based at least in part on the first portion of the data set, wherein the transformation includes at least enlarging the data visualization and rotating the data visualization from a first orientation to a second orientation.
A visual-based classification model influenced by text features as a result of the outputs of a text-based classification model is disclosed. A system receives one or more documents to be classified based on one or more visual features and provides the one or more documents to a student classification model, which is a visual-based classification model. The system also classifies, by the student classification model, the one or more documents into one or more document types based on one or more visual features. The one or more visual features are generated by the student classification model that is trained based on important text identified by a teacher classification model for the one or more document types, with the teacher classification model being a text-based classification model. Generating training data and training the student classification model based on the training data are also described.
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
A method includes detecting, in a written electronic communication, an input sentence satisfying a readability metric threshold, and processing, by a sentence transformer model responsive to the input sentence satisfying the readability metric threshold, the input sentence to output a suggested set of sentences. The method further includes evaluating the first suggested set of sentences along a set of acceptability criteria, and determining, based on the evaluating, that the set of acceptability criteria is satisfied. The method further includes modifying, based on determining that the set of acceptability criteria is satisfied, the written electronic communication with the suggested set of sentences to obtain a modified written electronic communication, and storing the modified written electronic communication.
Systems and methods for generating a contextually adaptable classifier model are disclosed. An example method is performed by one or more processors of a system and includes obtaining a dataset, feature values, and labels, transforming each datapoint into a natural language statement (NLS) associating the datapoint's feature values and label with feature identifiers and a label identifier, generating a feature matrix for each NLS, transforming the feature matrix into a global feature vector, generating a target vector for each NLS, transforming the target vector into a global target vector having a same shape, and generating, using the vectors, a similarity measurement operation, and a loss function, a classifier model trained to generate a compatibility score predictive of an accuracy at which the classifier model can classify given data based on at least one of a different feature characterizing the given data or a different label for classifying the given data.
This disclosure relates to generating a comprehensive set of synthetic utterances. An example system is configured to provide an input utterance to a plurality of synthetic utterance generation pipelines in parallel. Each of the plurality of synthetic utterance generation pipelines include one or more utterance synthesizers. For example, one or more pipelines may use a synthesizer chain that includes a plurality of synthesizers in parallel. The plurality of synthetic utterance generation pipelines generates synthetic utterances, which may be stored in a database after evaluating the similarity between the original input utterance and each resulting synthetic utterance. For example, a synthetic utterance may be retained if the cosine similarity between the input and synthetic utterances is less than a predetermined threshold. Additionally, the synthetic utterances may be fed back at input utterances based on the similarity evaluation and the feedback loop repeated until a desired number of utterances are generated.
Certain aspects of the present disclosure provide techniques and systems for screening chat attachments. A chat attachment screening system monitors a chat window of a first computing device associated with a first user during an interaction session between the first user and a second user. An upload of an attachment is detected based on the monitoring. Access to the attachment from a second computing device associated with the second user is blocked, in response to detecting the upload. Content from the attachment is identified and extracted. A type of the attachment is determined based on the content. A determination is made as to whether the second user is authorized to access the type of the attachment. An indication of the determination is presented on at least one of the first computing device or the second computing device during the interaction session .
Certain aspects of the present disclosure provide techniques and systems for automatically detecting, tracking, and processing certain information content, based on voice input from a user. A voice enabled content tracking system receives natural language content corresponding to audio input from a user. A determination is made as to whether the natural language content includes a first type of information, based on evaluating the natural language content with a first machine learning model. In response to determining the natural language content comprises the first type of information, a temporal association of the first type of information is determined, based on evaluating the natural language content with a second machine learning model, and a message including an indication of the temporal association of the first type of information is transmitted to the user.
G10L 15/183 - Speech classification or search using natural language modelling using context dependencies, e.g. language models
H04W 4/18 - Information format or content conversion, e.g. adaptation by the network of the transmitted or received information for the purpose of wireless delivery to users or terminals
Aspects of the present disclosure provide techniques for machine learning and rules integration. Embodiments include receiving input values corresponding to a subset of a set of input variables associated with an automated determination. Embodiments include generating a directed acyclic graph (DAG) representing a set of constraints corresponding to the set of input variables. The set of constraints relate to one or more machine learning models and one or more rules. Embodiments include receiving one or more outputs from the one or more machine learning models based on one or more of the input values. Embodiments include determining outcomes for the one or more rules based on at least one of the input values. Embodiments include populating the DAG based on the input values, the one or more outputs, and the outcomes. Embodiments include making the automated determination based on logic represented by the DAG.
Systems and methods that process, classify, and provide intelligent insights related to received documents such as notice documents in real-time. The system and methods leverage a novel framework of artificial intelligence and machine learning techniques to identify a requirement in the document (e.g., a government notice) and generate actionable suggestions thereto.
A method including training, using training data including a first ontological hierarchical level, trained machine learning models (MLMs) to predict a first output type including a second ontological hierarchical level different than the first ontological hierarchical level. The method also includes generating instances of the first output type by executing the trained MLMs on unknown data including the first ontological hierarchical level. Outputs of the trained MLMs include the instances at the second ontological hierarchical level. The method also includes training, using the instances, a voting classifier MLM to predict a selected instance from the instances. The voting classifier MLM is trained to predict the selected instance to satisfy a criterion including a third ontological hierarchical level different than the first ontological hierarchal level and the second ontological hierarchical level.
The one or more embodiments provide for a method. The method includes receiving a digital image stored in an object notation data format. The method also includes converting the digital image into hypertext markup language (HTML) data format. The method also includes caching the HTML data format to generate cached HTML data. The method also includes receiving a first request to reload the digital image. The method also includes rendering, responsive to receiving the first request to reload, the digital image using the cached HTML data to generate a rendered digital image.
A transcript of an audio conversation between multiple users (e.g., two users) is generated. The transcript is displayed in real time within a VR environment as the conversation takes place. A virtual selection tool is displayed within the VR environment to allow for a selection of different portions of the transcript. In addition, a virtual keyboard and or virtual panels with characters may be displayed and the virtual selection tool may be used to make selections from these displays as well. These selections are used to generate new text. The new text may form part of a user's notes of the conversation or an entry for a text field within the VR environment.
G06F 3/04886 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus
G06F 3/04842 - Selection of displayed objects or displayed text elements
G06F 3/04815 - Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software for artificial intelligence processing and machine learning; software for use in processing and generating natural language queries;software for knowledge engineering; software for generative artificial intelligence applications; software using artificial intelligence for the production of speech, text, images, video, sound, haptics, and numerical calculations; software using artificial intelligence for rendering and navigating virtual reality environments;software for multi-modal machine-learning based language-, text-, speech-, image-, video-, sound-, haptic- and numerical-calculation-processing software; artificial intelligence software for facilitating interaction and communication between humans and machines, humans and software, humans and other humans, machines and other machines, software and other software, and between and among machines and software applications and operating systems; software for facilitating multi-modal natural language, speech, text, numerical, image, video, sound and haptic input; software featuring artificial intelligence for use in processing voice and physical commands, searching databases, and creating audio, images, textual and numeric responses to user-specific inquiries, including via chatbot interfaces; software for use in predictive analytics, recommendations, personalization, automatic data understanding, automatic rules generation, and automatic software code generation; software for enabling sentiment analysis;software tools for use in software development; software for automation of business and personal tasks; software which takes automated actions on behalf of a user, a machine, or application or operating system software; software for automated analysis of data and for producing automated insights; software for automated analysis of large datasets to enable decision-making; software for producing automated reminders and recommendations; software for automatically categorizing data; software for automatically analyzing data to enable reporting, behavioral analysis and trend analysis Providing online non-downloadable software for artificial intelligence processing and machine learning; providing online non-downloadable software for use in processing and generating natural language queries; providing online non-downloadable software for knowledge engineering; providing online non-downloadable software for generative artificial intelligence applications; providing online non-downloadable software using artificial intelligence for the production of speech, text, images, video, sound, haptics, and numerical calculations; providing online non-downloadable software using artificial intelligence for rendering and navigating virtual reality environments; providing online non-downloadable software for multi-modal machine-learning based language-, text-, speech-, image-, video-, sound-, haptic- and numerical-calculation-processing software; providing online non-downloadable artificial intelligence software for facilitating interaction and communication between humans and machines, humans and software, humans and other humans, machines and other machines, software and other software, and between and among machines and software applications and operating systems;providing online non-downloadable software for facilitating multi-modal natural language, speech, text, numerical, image, video, sound and haptic input; application service provider (ASP) services featuring application programming interface (API) software featuring artificial intelligence and machine learning features and capabilities; providing online non-downloadable software featuring artificial intelligence for use in processing voice and physical commands, searching databases, and creating audio, images, textual and numeric responses to user-specific inquiries, including via chatbot interfaces; providing online non-downloadable software for use in predictive analytics, recommendations, personalization, automatic data understanding, automatic rules generation, and automatic software code generation; providing online non-downloadable software for enabling sentiment analysis; providing online non-downloadable software tools for use in software development; providing online non-downloadable software for automation of business and personal tasks; providing online non-downloadable software which takes automated actions on behalf of a user, a machine, or application or operating system software;providing online non-downloadable software for automated analysis of data and for producing automated insights; providing online non-downloadable software for automated analysis of large datasets to enable decision-making; providing online non-downloadable software for producing automated reminders and recommendations; providing online non-downloadable software for automatically categorizing data; providing online non-downloadable software for automatically analyzing data to enable reporting, behavioral analysis and trend analysis
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software for artificial intelligence processing and machine learning; software for use in processing and generating natural language queries; software for knowledge engineering; software for generative artificial intelligence applications; software using artificial intelligence for the production of speech, text, images, video, sound, haptics, and numerical calculations; software using artificial intelligence for rendering and navigating virtual reality environments; software for multi-modal machine-learning based language-, text-, speech-, image-, video-, sound-, haptic- and numerical-calculation-processing software; artificial intelligence software for facilitating interaction and communication between humans and machines, humans and software, humans and other humans, machines and other machines, software and other software, and between and among machines and software applications and operating systems; software for facilitating multi-modal natural language, speech, text, numerical, image, video, sound and haptic input; software featuring artificial intelligence for use in processing voice and physical commands, searching databases, and creating audio, images, textual and numeric responses to user-specific inquiries, including via chatbot interfaces; software for use in predictive analytics, recommendations, personalization, automatic data understanding, automatic rules generation, and automatic software code generation; software for enabling sentiment analysis; software tools for use in software development; software for automation of business and personal tasks; software which takes automated actions on behalf of a user, a machine, or application or operating system software; software for automated analysis of data and for producing automated insights; software for automated analysis of large datasets to enable decision-making; software for producing automated reminders and recommendations; software for automatically categorizing data; software for automatically analyzing data to enable reporting, behavioral analysis and trend analysis. Providing online non-downloadable software for artificial intelligence processing and machine learning; providing online non-downloadable software for use in processing and generating natural language queries; providing online non-downloadable software for knowledge engineering; providing online non-downloadable software for generative artificial intelligence applications; providing online non-downloadable software using artificial intelligence for the production of speech, text, images, video, sound, haptics, and numerical calculations; providing online non-downloadable software using artificial intelligence for rendering and navigating virtual reality environments; providing online non-downloadable software for multi-modal machine-learning based language-, text-, speech-, image-, video-, sound-, haptic- and numerical-calculation-processing software; providing online non-downloadable artificial intelligence software for facilitating interaction and communication between humans and machines, humans and software, humans and other humans, machines and other machines, software and other software, and between and among machines and software applications and operating systems; providing online non-downloadable software for facilitating multi-modal natural language, speech, text, numerical, image, video, sound and haptic input; application service provider (ASP) services featuring application programming interface (API) software featuring artificial intelligence and machine learning features and capabilities; providing online non-downloadable software featuring artificial intelligence for use in processing voice and physical commands, searching databases, and creating audio, images, textual and numeric responses to user-specific inquiries, including via chatbot interfaces; providing online non-downloadable software for use in predictive analytics, recommendations, personalization, automatic data understanding, automatic rules generation, and automatic software code generation; providing online non-downloadable software for enabling sentiment analysis; providing online non-downloadable software tools for use in software development; providing online non-downloadable software for automation of business and personal tasks; providing online non-downloadable software which takes automated actions on behalf of a user, a machine, or application or operating system software; providing online non-downloadable software for automated analysis of data and for producing automated insights; providing online non-downloadable software for automated analysis of large datasets to enable decision-making; providing online non-downloadable software for producing automated reminders and recommendations; providing online non-downloadable software for automatically categorizing data; providing online non-downloadable software for automatically analyzing data to enable reporting, behavioral analysis and trend analysis.
78.
MACHINE LEARNING MODEL BASED ELECTRONIC DOCUMENT COMPLETION
Systems and methods for machine learning (ML) based electronic document completion are described. A system is configured to receive one or more electronic documents to be completed for a user and provide the one or more electronic documents to an ML model. The ML model is trained to categorize the one or more electronic documents based on previously categorized electronic documents. The system is also configured to: categorize, for each electronic document of the one or more electronic documents, the electronic document into an electronic document category by the ML model; identify one or more fields to be entered by the user based on categorizing the one or more electronic documents; generate a dynamic form including the one or more fields to be entered; and provide the dynamic form for display to the user. Identifying the one or more fields to be entered may be based on a statistical model.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Software for artificial intelligence processing and machine learning; software for use in processing and generating natural language queries; software for knowledge engineering; software for generative artificial intelligence applications; software using artificial intelligence for the production of speech, text, images, video, sound, haptics, and numerical calculations; software using artificial intelligence for rendering and navigating virtual reality environments; software for multi-modal machine-learning based language-, text-, speech-, image-, video-, sound-, haptic- and numerical-calculation-processing software; artificial intelligence software for facilitating interaction and communication between humans and machines, humans and software, humans and other humans, machines and other machines, software and other software, and between and among machines and software applications and operating systems; software for facilitating multi-modal natural language, speech, text, numerical, image, video, sound and haptic input; software featuring artificial intelligence for use in processing voice and physical commands, searching databases, and creating audio, images, textual and numeric responses to user-specific inquiries, including via chatbot interfaces; software for use in predictive analytics, recommendations, personalization, automatic data understanding, automatic rules generation, and automatic software code generation; software for enabling sentiment analysis; software tools for use in software development; software for automation of business and personal tasks; software which takes automated actions on behalf of a user, a machine, or application or operating system software; software for automated analysis of data and for producing automated insights; software for automated analysis of large datasets to enable decision-making; software for producing automated reminders and recommendations; software for automatically categorizing data; software for automatically analyzing data to enable reporting, behavioral analysis and trend analysis (1) Providing online non-downloadable software for artificial intelligence processing and machine learning; providing online non-downloadable software for use in processing and generating natural language queries; providing online non-downloadable software for knowledge engineering; providing online non-downloadable software for generative artificial intelligence applications; providing online non-downloadable software using artificial intelligence for the production of speech, text, images, video, sound, haptics, and numerical calculations; providing online non-downloadable software using artificial intelligence for rendering and navigating virtual reality environments; providing online non-downloadable software for multi-modal machine-learning based language-, text-, speech-, image-, video-, sound-, haptic- and numerical-calculation-processing software; providing online non-downloadable artificial intelligence software for facilitating interaction and communication between humans and machines, humans and software, humans and other humans, machines and other machines, software and other software, and between and among machines and software applications and operating systems; providing online non-downloadable software for facilitating multi-modal natural language, speech, text, numerical, image, video, sound and haptic input; application service provider (ASP) services featuring application programming interface (API) software featuring artificial intelligence and machine learning features and capabilities; providing online non-downloadable software featuring artificial intelligence for use in processing voice and physical commands, searching databases, and creating audio, images, textual and numeric responses to user-specific inquiries, including via chatbot interfaces; providing online non-downloadable software for use in predictive analytics, recommendations, personalization, automatic data understanding, automatic rules generation, and automatic software code generation; providing online non-downloadable software for enabling sentiment analysis; providing online non-downloadable software tools for use in software development; providing online non-downloadable software for automation of business and personal tasks; providing online non-downloadable software which takes automated actions on behalf of a user, a machine, or application or operating system software; providing online non-downloadable software for automated analysis of data and for producing automated insights; providing online non-downloadable software for automated analysis of large datasets to enable decision-making; providing online non-downloadable software for producing automated reminders and recommendations; providing online non-downloadable software for automatically categorizing data; providing online non-downloadable software for automatically analyzing data to enable reporting, behavioral analysis and trend analysis
A method including receiving, from a server, a backoff data packet including first information and second information. The first information includes a backoff command to cease transmitting at least some requests to a service executing on the server, and The second information includes metadata. The method also includes adding the backoff data packet to cached data stored in a cache. The method also includes receiving, from a client, a request data packet including a request for the service. The method also includes comparing request information, associated with the request data packet, to the cached data, including at least comparing the request information to the metadata. The method also includes blocking, responsive to a match between the request information and the cached data, the request data packet from being transmitted to the service.
The one or more embodiments provide for a method, system, and computer program product, an intent, generated by a large language model from a text, is received from a user device as a first input to an advice planner. A state of an account is received as a second input to the advice planner. The advice planner classifieds the intent into a domain corresponding to the intent, and generates, as output, a plan comprising a first set of action logic associated with the domain. Each action logic is a discrete step in an ordered sequence for achieving a desired state of the account. The advice planner forwards the plan to the large language model (LLM). The large language model receives the plan as input and generates advice in a natural language format as output. The advice is then forwarded to the user device.
Systems and methods are disclosed for managing a generative artificial intelligence (AI) model. Managing the generative AI model may include training or tuning the generative AI model before use or managing the operation of the generative AI model during use. Training or tuning a generative AI model typically requires manual review of outputs from the model based on the queries provided to the model to reduce hallucinations generated by the generative AI model. Once the model is in use, though, hallucinations still occur. Use of a confidence (whose generation is described herein) to train or tune the generative AI model and/or manage operation of the model reduces hallucinations, and thus improves performance, of the generative AI model.
Systems and methods are disclosed for tuning a generative artificial intelligence (AI) model based on a knowledge base. Instead of manually generating questions relevant to the knowledge base, providing those questions to the generative AI model, and manually reviewing the answers generated by the generative AI model in order to tune the generative AI model over many iterations, a natural language processing model may be configured to leverage the knowledge base to automatically generate questions and answers based on the knowledge base. In this manner, the natural language processing model is able to generate tuning data that may be used to automatically tune the generative AI model. The systems and methods also disclose automatic tuning of the generative AI model, including testing and feedback that may be used to improve tuning of the generative AI model.
A tax data collection system includes a navigation module configured to obtain user data. The system also includes a data graph including information relating to the user data. The system further includes a knowledge engine configured to map the user data onto a data model using the information from the data graph. Moreover, the system includes an inference engine configured to suggest a system action by analyzing at least the data model after the user data has been mapped thereon.
A method includes training, using first real data objects, a generative adversarial network having a generator model and a discriminator model to create a trained generator model that generates realistic data, and training, using adversarial data objects and second real data objects, the discriminator model to output an authenticity binary class for the adversarial data objects and the second real data objects. The method further includes deploying the discriminator model to a production system. In the production system, the discriminator model outputs the authenticity binary class to a system classifier model.
A method implements influencer segmentation detection. The method includes selecting transaction data for a time window and processing the transaction data for the time window to generate a graph for the time window. The method further includes extracting, from the graph, a feature set for a node of the graph for the time window and processing the feature set to generate a predicted rank for the node for a subsequent time window using a machine learning model. The method further includes selecting, using the predicted rank, an entity identifier corresponding to the node and presenting the entity identifier.
Aspects of the present disclosure relate to electronic document creation assistance. Embodiments include determining a current time related to creation of a document by a user and providing inputs to a machine learning model based on the current time. Embodiments include receiving output from the machine learning model based on the inputs and selecting, based on the output, a first recommended item from a plurality of items for inclusion in the document. Embodiments include determining a likelihood of each additional item of the plurality of items co-occurring with the first recommended item based on historical item co-occurrence data. Embodiments include selecting, based on the output and the likelihood of each additional item of the plurality of items co-occurring with the first recommended item, a second recommended item for inclusion in the document and providing, via a user interface, the first recommended item and the second recommended item to the user.
Systems and methods for throttling requests to web services are disclosed. A system is configured to receive, at a host, one or more requests during a first time period. Each request is for a web service hosted on a backend. The host is one of a plurality of hosts of an application programming interface (API) gateway to receive a plurality of requests for the web services. The system is further configured to: process at least a portion of the one or more requests for the one or more web services; count, by a local counter in a local cache of the host, the one or more requests received at the host during the first time period; compare a local count of the local counter to a local bucket size associated with the host; and provide an instruction to update a remote count of a remote counter based on the comparison.
H04L 67/60 - Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
H04L 67/1021 - Server selection for load balancing based on client or server locations
Certain aspects of the present disclosure provide techniques for improving a user experience of an application. Embodiments include receiving, from a user and via a user interface, a request for informational content related to a step in a workflow within the application. Embodiments include determining an identifier associated with the step. Embodiments include retrieving a reference document based on the request. Embodiments include accessing metadata associated with the reference document to identify context information associated with the identifier. Embodiments include displaying a portion of the reference document to the user within the user interface based on the context information, wherein the portion of the reference document comprises the informational content.
A system deploying a machine learning technique that utilizes known code graph and abstract syntax tree pairs for known JSON objects to learn a function for predicting a corresponding abstract syntax tree from a new JSON object. The predicted abstract syntax tree is used to generate code for formatting the new JSON object into a standardized data structure.
Techniques for detecting fraud may include obtaining a merchant's financial data; determining, via a machine learning model, a first prediction of the merchant's industry; generating a first probability matrix based on the first prediction and the declared information regarding the merchant's industry; determining, via the machine learning model, a second prediction of the merchant's industry; generating a second probability matrix based on the second prediction and the declared information regarding the merchant's industry; obtaining a declared industry of a subject merchant in a runtime environment; determining, via the machine learning model, a predicted industry for the subject merchant; obtaining, based on the declared industry and the predicted industry of the subject merchant, a first value from the first probability matrix and a second value from the second probability matrix; and labeling the subject merchant for further investigation.
Certain aspects of the disclosure provide systems and methods for training an information extraction transformer model architecture directed to pre-training a first multimodal transformer model on an unlabeled dataset, training a second multimodal transformer model on a first labeled dataset to perform a key information extraction task processing the unlabeled dataset with the second multimodal transformer model to generate pseudo-labels for the unlabeled dataset, training the first multimodal transformer model based on a second labeled dataset comprising one or more labels, the pseudo-labels generated, or combinations thereof to generate a third multimodal transformer model, generating updated pseudo-labels based on label completion predictions from the third multimodal transformer model, and training the third multimodal transformer model using a noise-aware loss function and the updated pseudo-labels to generate an updated third multimodal transformer model.
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G06V 30/413 - Classification of content, e.g. text, photographs or tables
Systems and methods are provided for refreshing encryption and decryption keys. The disclosed techniques can improve refreshing encryption keys by allowing for the process to be automated, preventing downtime in each system and reducing developer labor in preparing and facilitating the exchange. In addition, the embodiments of the present disclosure can enable organizations to store keys (both old keys and newly generated keys) along with metadata in a known location accessible to the other organization.
Certain aspects of the present disclosure provide techniques for identifying fraudulent user identifiers in a software application. An example method generally includes generating a vector representation of a user identifier. Using a first machine learning model and the vector representation of the user identifier, a fingerprint representative of the user identifier is generated. Using the first machine learning model and the generated fingerprint, a score is generated. The score generally describes a likelihood that the user identifier corresponds to a fraudulent user identifier. One or more similar user identifiers are identified based on the generated fingerprint and a second machine learning model. One or more actions are taken within a computing system relative to a user associated with the user identifier based on the generated score and the identified one or more similar user identifiers.
Methods, systems and articles of manufacture for efficiently calculating an electronic tax return, such as within a tax return preparation system. A computerized tax return preparation system accesses taxpayer-specific tax data from a shared data store. The system executes a tax calculation engine configured to perform a plurality of tax calculations based on a tax calculation graph and the taxpayer-specific tax data from the shared data store. The system is configured to perform only the calculations in the tax calculation graph which are changed by new taxpayer-specific tax data received since the preceding tax calculation executed by the tax calculation engine. The system may also determine whether the new taxpayer-specific tax data does, or does not change the calculated tax return and the reason why.
Certain aspects of the present disclosure provide techniques for training and using machine learning models to extract key-value sets from a document. An example method generally includes identifying regions of a document including key-value sets corresponding to inputs to a data processing application based on a first machine learning model and an electronic version of the document. One or more keys and one or more values are identified in the document based on a second machine learning model. One or more key-value sets are generated based on matching keys of the one or more keys and values of the one or more values in the region of the document. The one or more key-value sets are provided to a data processing application for processing.
Methods and systems for assisting entities with improving the effectiveness of their profiles are disclosed. An example method is performed by one or more processors of a system and includes storing profile data including profiles identifying attributes associated with respective entities, obtaining a selection data vector including values each indicating a selection rate for a respective entity, generating, using a trained analysis model, selection prediction data predicting, for each respective change of a set of possible changes to a selected entity's profile, how the selection rate for the selected entity will change if the selected entity's profile is adjusted in accordance with the respective change, selecting, from the selection prediction data, one or more recommended changes likely to result in an increase in the selection rate for the selected entity, and outputting a prompt recommending that the selected entity make one or more recommended changes to the selected entity's profile.
Systems and methods for assessment of user price sensitivity using a predictive model are disclosed. An example method may be performed by one or more processors of a retention system and include retrieving traversal sequences including lists of pages users accessed prior to price notifications, labeling the traversal sequences based on whether users terminated sessions upon the notifications, transforming the traversal sequences into graphs based on identifiers assigned to the pages and instances in which users successively accessed pages, defining predictive features suggesting an extent to which identified structural attributes of given graphs affect user price sensitivity, generating model training data based on the labeled traversal sequences and predictive features, determining optimal weights for the predictive features, and generating a model incorporating the optimal weights and trained to predict a likelihood that a user will terminate a session when notified of a price to continue given the user's traversal sequence.
Disclosed dynamic schema mapping systems and methods monitor network traffic between different microservices and train mapping models based on the monitored network traffic using unsupervised training. This training of the mapping models generates a probability distribution tensor that shows the probabilistic associations of different key-value pairs of the schemas of different microservices. The trained mapping models are used to map a schema from a source microservice to another schema at a destination microservice. Should the translated schema be incompatible with the destination microservice, a semi-supervised approach is taken to make the translated schema compatible. The trained models may be reinforced (e.g., the probability distribution tensor may be updated) as more network traffic is collected and analyzed. The dynamic mapping therefore allows a system to be schema-agnostic, and developers may be able to define application interfaces or interaction schemas without the necessity of accounting for compatibility constraint between the different schemas.
A processor may receive clear text data. The processor may represent at least a portion of the clear text data as at least one array encoding a description of at least one feature of the clear text data. The processor may process the at least one array using a clustering algorithm to determine whether the at least one array is grouped with a benign cluster or a sensitive cluster of a model. In response to determining that the at least one array is grouped with the sensitive cluster, the processor may generate an alert indicating that the clear text data includes sensitive information.
