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 - Classement ou recherche de la parole utilisant une modélisation du langage naturel selon les contextes, p.ex. modèles de langage
H04W 4/18 - Conversion de format ou de contenu d'informations, p.ex. adaptation, par le réseau, des informations reçues ou transmises pour une distribution sans fil aux utilisateurs ou aux terminaux
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 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.
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.
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 30/413 - Classification de contenu, p.ex. de textes, de photographies ou de tableaux
G06V 30/414 - Extraction de la structure géométrique, p.ex. arborescence; Découpage en blocs, p.ex. boîtes englobantes pour les éléments graphiques ou textuels
A method including receiving a command to display a modal dialog. The modal dialog is displayed using both first and second scrolling frames. The first scrolling frame permits scrolling when a modal dialog height exceeds a first scrolling frame constraint. The second scrolling frame permits scrolling of a content section when a content section height exceeds a second scrolling frame constraint. The first scrolling frame constraint has a first and second priorities. The second scrolling frame constraint has a third priority. An orientation of the display screen is determined as being either in a portrait orientation or a landscape orientation. Responsive to determining the physical orientation, an applicable priority that is applicable to the first scrolling frame constraint is assigned. The applicable priority is the first priority in the portrait orientation, and is the second priority in the landscape orientation. After assigning the applicable priority, the modal dialog is displayed.
7.
ENTITY EXTRACTION WITH ENCODER DECODER MACHINE LEARNING MODEL
A method includes executing an encoder machine learning model on multiple token values contained in a document to create an encoder hidden state vector. A decoder machine learning model executing on the encoder hidden state vector generates raw text comprising an entity value and an entity label for each of multiple entities. The method further includes generating a stmctural representation of the entities directly from the raw text and outputting the structural representation of the entities of the document.
A method including receiving an incoming call from a calling device of a caller and determining identification information for the calling device. The method also includes receiving voice audio data of the caller from the calling device, converting the voice audio data to caller phones, and identifying a customer account associated with the identification information. The method further includes obtaining user phones for multiple candidate users associated with the identified customer account, comparing the caller phones to the user phones for the multiple candidate users, and determining the identity of the caller based on the comparison.
9.
SUPERVISED MACHINE LEARNING METHOD FOR MATCHING UNSUPERVISED DATA
A method including receiving first and second natural language texts. A distance metric is generated from the first and second natural language texts. A first machine learning system is executed, the first machine learning system taking, as a first input, the distance metric and generating, as a first output, a first probability that the first natural language text matches the second natural language text. A second machine learning system is executed, the second machine learning system taking as a second input the first natural language text and as a third input the second natural language text, and generating, as a second output, a second probability that the first natural language text matches the second natural language text. A third probability that the first natural language text matches the second natural language text is generated. Generating includes combining the first probability and the second probability.
Certain aspects of the present disclosure provide techniques for training and using a machine learning model to extract relevant textual content for custom fields in a software application from freeform text samples. An example method generally includes generating, via a natural language processing pipeline, a training data set from a data set of freeform text samples and field entries for a plurality of custom fields defined in a software application. A first machine learning model is trained to identify custom fields for which relevant data is included in freeform text. A second machine learning model is trained to extract content from the freeform text into one or more custom fields of the plurality of custom fields defined in the software application and identified by the first machine learning model as custom fields for which relevant data is included in the freeform text.
Systems and methods for securely verifying integrity of application responses are disclosed. One example method includes receiving, from a client, an application encrypted in accordance with a fully homomorphic encryption (FHE) algorithm, generating, with a trained machine learning model associated with the FHE algorithm, a plurality of first application labels, each first application label indicating a true or false response associated with the application, inverting a randomly selected portion of the plurality of first application labels, generating a first randomly sorted list including the plurality of first application labels, transmitting the first randomly sorted list to the client, receiving a first decrypted list from the client, performing a validation of at least the first decrypted list, the validation based at least in part on the plurality of first application labels, and in response to the validation being successful, providing the client with a response to the application.
A processor may receive a request for access to a first resource from a client. The processor may retrieve a decision token indicating a plurality of resource decisions for the client, each of the plurality of resource decisions including a decision permitting or forbidding access to at least one resource. The processor may identify, among the plurality of resource decisions, a first decision for the first resource. On the basis of the first decision for the first resource, the processor may enable or block access to the first resource by the client. The decision token may have been generated by the processor generating a plurality of resource decisions for the client, the plurality of resource decisions including a first decision permitting or forbidding access to the first resource and at least one additional decision permitting or forbidding access to at least one additional resource.
Systems and methods for generating regressors based on data sparsity using a machine learning (ML) model are described. A system is configured to provide a plurality of time series datasets to a recurrent neural network (RNN) of a machine learning (ML) model. The RNN generates one or more outputs associated with one or more time series datasets, and the system provides a first portion and a second portion of the one or more outputs to a regressor layer and a classification layer of the ML model, respectively. The regressor layer generates one or more regressors for the one or more time series datasets, and the classification layer generates one or more classifications associated with the one or more regressors (with each indicating whether an associated regressor is valid). Whether a classification indicates a regressor is valid may be based on time series data sparsity.
A method implements hybrid artificial intelligence generated actionable recommendations. The method includes processing an event to identify an action of an event action set. The event includes an event value. The method further includes processing the event action set to generate an objective value, corresponding to the action, and a probability, corresponding to the action, and to form a model action set from the event action set. The method further includes filtering the model action set using action rule data and rule user data to generate a filtered action set. The method further includes processing, using the objective value and the probability, the filtered action set with an optimization controller to generate suggested action sets from which a selected action set is selected. The selected action set corresponds to a combined action value that satisfies the event value.
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
A method implements chum prevention using graphs. The method includes receiving clickstream data, which includes an event, of a user session with an application. The method further includes identifying the event as corresponding to a chum user account and mapping the event to a pair of nodes of a graph. The method further includes updating a chum user count of the pair of nodes in response to identifying the event as corresponding to the chum user account. The method further includes identifying an edge of the graph, corresponding to the pair of nodes. The method further includes updating a value of the edge using an active user count and the chum user count presenting an update responsive to the value.
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
A method implements churn prediction using clickstream data. The method includes receiving clickstream data of a user and converting the clickstream data to a token list. The method further includes processing the token list with a first recurrent layer, a second recurrent layer, and an attention layer of a machine learning model to generate a churn risk. The method further includes executing a reactivation action in response to the churn risk.
H04L 41/5061 - Gestion des services réseau, p.ex. en assurant une bonne réalisation du service conformément aux accords caractérisée par l’interaction entre les fournisseurs de services et leurs clients réseau, p.ex. la gestion de la relation client
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p.ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
A processor may receive an image and identify a plurality of characters in the image using a machine learning (ML) model. The processor may generate at least one word- level bounding box indicating one or more words including at least a subset of the plurality of characters and/or may generate at least one field-level bounding box indicating at least one field including at least a subset of the one or more words. The processor may overlay the at least one word-level bounding box and the at least one field-level bounding box on the image to form a masked image including a plurality of optically-recognized characters and one or more predicted fields for at least a subset of the plurality of optically-recognized characters.
G06V 30/18 - Extraction d’éléments ou de caractéristiques de l’image
G06V 30/414 - Extraction de la structure géométrique, p.ex. arborescence; Découpage en blocs, p.ex. boîtes englobantes pour les éléments graphiques ou textuels
Certain aspects of the present disclosure provide techniques for managing the lifecycle of user data. A user data lifecycle management system can collect user data from multiple sources including the user, the organization implementing the lifecycle management system, and third parties. The user data is used to create a user profile with a global user identifier. The user profile is scored based on the attributes within the user profile. The user data lifecycle management system can route the user profile to a destination source based on the score of the user profile. The destination source can be a tool of the organization for interacting with the user. The destination sources can also provide feedback to that is incorporated to the user profile and can assist the user data lifecycle management system in managing the user profile and aid in decision making.
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
19.
MULTIMODAL, MULTITASK MACHINE LEARNING SYSTEM FOR DOCUMENT INTELLIGENCE TASKS
Multimodal multitask machine learning system for document intelligence tasks includes a feature extractor processing token values obtained from a document to obtain features, and a token extraction head classif)fing, using the features, the token values to obtain classified tokens. The classified tokens are aggregated into entities. A document classification model is executed on the features to classify the document and obtain a document label prediction. Further a confidence head model applying the document label prediction processes the entities to obtain a result.
A method includes establishing a connection with a spreadsheet at a source location, receiving at least one selected field from an entry in the spreadsheet at the source location, and generating an assured reference record for the entry using at least one value in the at least one selected field. The assured reference record includes an assurance value and a reference to the entry at the source location. The method further includes storing, at a second location, the assured reference record in an assured reference file associated with the spreadsheet. The method further includes validating, after storing the assured reference record, the reference to the first entry in the spreadsheet at the source location using the first assurance value in the assured reference record.
The present application provides a graphical user interface (GUI) for conversational task completion and a related method. The method includes obtaining a help file or clickstream file, based on clickstream data including a sequence of steps for a task, and generating a knowledge graph including instructions corresponding to the steps. The method further includes extracting, from a user input of a user, an intent to complete the task. Responsive to extracting the intent to complete the task, obtaining the knowledge graph is obtained. Using the knowledge graph, an instruction of the knowledge graph is presented to the user to perform an action in a workflow to complete the task.
A method that includes obtaining, for a task, a help file including steps, and generating, from the help file, a knowledge graph for the task, the knowledge graph includes nodes connected by directed edges. Generating the knowledge graph includes, for a step of the set of steps obtaining, from the step, a first step attribute value defining an action type of an action specified by the step, generating a natural language instruction based on the action type and a second step attribute value, in the step, corresponding to a parameter of the action, and storing the natural language instruction in a node. The method further includes storing the knowledge graph.
G06N 5/02 - Représentation de la connaissance; Représentation symbolique
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
23.
INTELLIGENT QUERY AUTO-COMPLETION SYSTEMS AND METHODS
Systems and methods are described for training a large language model with query auto-completion training data and automatically generating query auto- completion training data in an interactive GUI. A computing system continuously trains and refines a large language model utilizing masking techniques to on complex software- related queries. The computing system is further configured to utilize the large language model to provide complex software-related query suggestions to users operating a graphical user interface real-time.
A method extracts explainable corpora embeddings. The method includes constructing a graph with nodes representing terms from a text sequence and edges that include pointwise values generated between pairs of terms. The method further includes generating a rank vector from the graph. Elements of the rank vector correspond to the edges of the graph. A rank value, of the rank values, corresponds to a term from the text sequence. The method further includes selecting the term by comparing the rank vector to a previous rank vector generated for a previous text sequence.
A method implements last mile churn prediction. The method includes retrieving data during a user session in response to a trigger. The data includes a list of screen identifiers and a corresponding list of timestamps. The method further includes converting the list of timestamps to a list of time deltas. The list of time deltas includes a time delta that identifies an amount of time between two timestamps of the list of timestamps. The method further includes generating a churn risk from the list of screen identifiers and the list of time deltas. The churn risk identifies a probability that the user session will be terminated. The method further includes transmitting an intervention to intervene in the user session based on the churn risk.
A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.
G06V 30/18 - Extraction d’éléments ou de caractéristiques de l’image
G06V 30/414 - Extraction de la structure géométrique, p.ex. arborescence; Découpage en blocs, p.ex. boîtes englobantes pour les éléments graphiques ou textuels
27.
RULE-BASED TARGETED EXTRACTION AND ENCRYPTION OF SENSITIVE DOCUMENT FEATURES
Aspects of the present disclosure provide techniques for rule-based document security. Embodiments include receiving, from a computing device: an amended document; an encrypted sensitive component; and information relating to reconstructing a document based on the amended document and the encrypted sensitive component. Embodiments include decrypting the encrypted sensitive component to produce a decrypted sensitive component. Embodiments include determining, based on the information relating to reconstructing the document, a document location that corresponds to the decrypted sensitive component. Embodiments include reconstructing the document by inserting the decrypted sensitive component into the amended document at the document location.
A method that includes extracting image features of a document image, executing an optical character recognition (OCR) engine on the document image to obtain OCR output, and extracting OCR features from the OCR output. The method further includes executing an anomaly detection model using features including the OCR features and the image features to generate anomaly score, and presenting anomaly score.
A method and system are provided for quantifying saved time, such as during data entry applications in tax, accounting, and other similar fiscal tools. The method comprises th e steps of causing an interface to be displayed on a user device, the interface comprising a first option fora user to manually enter data into a form and a second option to import or upload data into the form; receiving user metrics information from a database; receiving import and extraction statistical information from a data lake; receiving identifying information for the form from the user device; calculating, via a machine learning algorithm, a time savings for using the second option instead of the first option based on the user metrics information and on a plurality of features. The method and system allow for consistent updating and re-tuning of the model via the machine learning platform.
Certain aspects of the present disclosure provide techniques for extracting information, including receiving a document comprising a plurality of tokens, wherein each token is associated with position coordinates; determining a classification for at least one token; generating a plurality of key-value pairs based on the positional coordinates; analyzing each respective key-value pair of the plurality of key-value pairs based on whether a token of the two tokens of each respective key-value pair matches a type associated with the respective key-value pair; determining a correct key-value pair of the plurality of key-value pairs based on the correct key- value pair comprising a matched token that matches the type associated with the correct key-value pair; and providing the classification and the correct key-value pair to a component of an application associated with the document.
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
Processing compliance documents based on an artificial intelligence (AI) model is described herein. A system is configured to obtain a compliance document and obtain seed data associated with the compliance document. The seed data includes a plurality of sample text inputs and a plurality of sample computer readable operations associated with the plurality of sample text inputs. The system is also configured to parse text in the compliance document into one or more text segments, provide the one or more text segments and the seed data to the AI model, and obtain, from the AI model, one or more computer readable operations associated with the one or more text segments. The one or more computer readable operations are generated by the AI model based on few-shot learning using the seed data. The system is also configured to store the one or more computer readable operations for completing the compliance document.
Certain aspects of the present disclosure provide techniques for executing a function in a software application through a conversational user interface based on a knowledge graph associated with the function. An example method generally includes receiving a request to execute a function in a software application through a conversational user interface. A graph definition of the function is retrieved from a knowledge engine. Input is iteratively requested through the conversational user interface for each parameter of the parameters identified in the graph definition of the function based on a traversal of the graph definition of the function. Based on a completeness graph associated with the function, it is determined that the requested inputs corresponding to the parameters identified in the graph definition of the function have been provided through the conversational user interface. The function is executed using the requested inputs as parameters for executing the function.
A method including generating a captured facial object and a captured pose from a captured image. The method also includes obtaining a base facial object and a base pose from a base image. The method also includes generating base pose angles using the captured pose, and captured pose angles using the captured pose. The method also includes obtaining selected base images using the base pose angles and the base facial object. The method also includes generating selected captured images using the captured pose angles and the captured facial object. The method also includes comparing the selected base images to the selected captured images to establish a comparison. The method also includes outputting a match output using the comparison.
A computer implemented method includes obtaining an original graph data structure including multiple stored nodes connected by multiple edges. The stored nodes include multiple operation stored nodes and multiple data stored nodes. The method further includes generating an auxiliary graph data structure from the original graph data structure. The auxiliary graph data structure includes the operation stored nodes. The method further includes executing a pattern mining tool on the auxiliary graph data structure to obtain a pattern list, traversing the auxiliary graph data structure to identify multiple instances of identified patterns in the pattern list, and presenting the instances.
A method optimizes questions to retain engagement. The method includes generating, using a machine learning model, a churn risk from user interaction data. The method includes selecting, when the churn risk satisfies a threshold, a field, from multiple fields, using multiple prediction confidences corresponding to multiple prediction values generated for the multiple fields. The method includes obtaining a prediction value for the field and obtaining a question, corresponding to the field, using the prediction value. The method includes presenting the question and receiving a user input in response to the question.
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
A method including transcribing, into digital tokens, utterances from a conversation between an agent and a person. The method also includes embedding the digital tokens into an utterances tensor including sequences of the digital tokens. The method also includes obtaining a metadata tensor by encoding metadata related to the utterances into the metadata tensor. The method also includes executing a machine learning model which takes, as input, the utterances tensor and the metadata tensor, and which outputs a predicted source article predicted to be related to the utterances. The method also includes generating an interactive link to the predicted source article.
G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
37.
CONVERTING FROM COMPRESSED LANGUAGE TO NATURAL LANGUAGE
A method converts from compressed language to natural language. The method includes receiving an element string. The element string is in a compressed language format and is extracted from a document in a structured document language. The method includes tokenizing the element string to form multiple element tokens, generating a token set from the element tokens, and generating a name string from multiple token sets. The name string is in a natural language format.
Aspects of the present disclosure relate to proactive intervention in a software application. Embodiments include selecting, by a proactive intervention system related to an application, an event of a plurality of events related to use of the application for processing based on event priorities associated with the plurality of events. Embodiments further include receiving, by the proactive intervention system, contextual information related to the event. Embodiments further include determining, by the proactive intervention system, a proactive intervention based on the event and the contextual information. Embodiments further include determining, by the proactive intervention system, that the proactive intervention can presently be provided based on intervention availability data. Embodiments further include providing, by the proactive intervention system, the proactive intervention via a user interface associated with the application.
Systems and methods of subscriber retention analysis are disclosed. A system is configured to obtain an instance of a current subscriber data for the first current subscriber subscribed to a product for a first amount of time and configured to provide the first instance of the current subscriber data to a machine learning (ML) classification model. Training the ML classification model is based on a plurality of data sets as training data. Each data set includes an instance of historic subscriber data over the first amount of time of a subscription for a historic subscriber. The system is also configured to generate, using the ML classification model, a predicted likelihood in retaining the first current subscriber based on the first instance of the current subscriber data.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
A processor may receive a request to perform an operation. The processor may generate a seed derived from data required to perform the operation. The processor may generate a perturbation based on inputting the seed into a pseudorandom number generator. The processor may generate the actual result based on performing the operation. The processor may generate a perturbed result, wherein generating the perturbed result may comprise performing a second operation based on the actual result and the perturbation. The processor may return the perturbed result in response to the request.
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
A method including generating, by a state engine from data describing behaviors of users in an environment external to the state engine, an executable process. An agent executes the executable process by determining, from the data describing the behaviors of the users, a problem of at least some of the users, and selects, based on the problem, a chosen action to alter the problem. At a first time, a first electronic communication describing the chosen action to the at least some of the users is transmitted. Ongoing data describing ongoing behaviors of the users is monitored. A reward is generated based on the ongoing data to change a parameter of the agent. The parameter of the agent is changed to generate a modified agent. The modified agent executes the executable process to select a modified action. At a second time, a second electronic communication describing the modified action is transmitted.
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
Certain aspects of the present disclosure provide techniques for configuring a software application through a remote configuration service. An example method generally includes receiving, from a remote configuration service, a declarative construct. Generally, the declarative construct includes a definition of a workflow in an application to be executed within a player application deployed on a client device. Information associated with the definition of the workflow is extracted by parsing the declarative construct according to a schema defining a format of the declarative construct. The workflow is executed in the player application based on the extracted information defining functionality of the workflow.
Aspects of the present disclosure relate to object-based image capture. Embodiments include identifying a reference point corresponding to an object in an image of a series of images. Embodiments include comparing a position of the reference point in the image to positions of one or more corresponding reference points in one or more previous images in the series of images. Embodiments include determining a total number of images in the series of images. Embodiments include selecting, based on the comparing and the total number of images in the series of images, between: capturing the image; or declining to capture the image.
H04N 23/60 - Commande des caméras ou des modules de caméras
H04N 23/68 - Commande des caméras ou des modules de caméras pour une prise de vue stable de la scène, p. ex. en compensant les vibrations du boîtier de l'appareil photo
44.
METHODS AND SYSTEMS FOR PERSISTENT COMMUNICATIONS BETWEEN CLIENT APPLICATIONS AND APPLICATION SERVERS
Certain aspects of the present disclosure provide techniques for communicating between an application executing on a client device and a server using a persistent connection. An example method generally includes initializing a persistent connection between an application executing on a client device and a server. Information about an event within the application is received. Communications between the application and the server are performed via streaming data related to the information about the event carried on the persistent connection. Generally, the streaming data may be translated from an application-native format to a platform- agnostic format and may include application-specific information. One or more actions are taken within the application based on the streaming data related to the event and carried on the persistent connection.
H04L 67/60 - Ordonnancement ou organisation du service des demandes d'application, p.ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises
G06F 9/448 - Paradigmes d’exécution, p.ex. implémentation de paradigmes de programmation
A method may include generating a vector from unstructured data included in an untransformed transaction, and determining, for the vector, a cluster ID of cluster IDs by matching the vector with a matching cluster vector of cluster vectors. The method may further include generating a query using the cluster ID and the untransformed transaction, and transforming, using the cluster IDs, untransformed transactions to transformed transactions. The transformed transactions may each include a cluster ID. The method may further include generating, using the query, a query result from features of the transformed transactions, generating a fraud score using the query result, and presenting the fraud score and the cluster ID.
G06F 16/30 - Recherche d’informations; Structures de bases de données à cet effet; Structures de systèmes de fichiers à cet effet de données textuelles non structurées
A method uses private information with a shared single source of truth. A message is received responsive to adding a first shared block to a shared object ledger of a shared object. The message includes message data. A private block is added to a private object ledger to update a private object. The private block includes private data from the message data. A second shared block is added to the shared object ledger to update the shared object. The second shared block includes shared data from the message data.
H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p.ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
A method may including obtaining a voice transcript corpus and a chat transcript corpus, extracting voice transcript sentences from the voice transcript corpus and chat transcript sentences from the chat transcript corpus, encoding, by a series ofneural network layers, the voice transcript sentences to generate voice sentence vectors, encoding, by the series of neural network layers, the chat transcript sentences to generate chat sentence vectors, determining, for each voice sentence vector, a matching chat sentence vector to obtain matching voice-chat vector pairs, and adding, to a parallel corpus, matching voice- chat sentence pairs using the matching voice-chat vector pairs. Each of the matching voice- chat sentence pairs may include a voice transcript sentence and a matching chat transcript sentence. The method may further include training a disfluency remover model using the parallel corpus.
G10L 15/10 - Classement ou recherche de la parole utilisant des mesures de distance ou de distorsion entre la parole inconnue et les gabarits de référence
A method implements a customer recognition system. A request with an identifier of an unidentified user is received. Sparse data is generated from string information corresponding to the identifier. Preexisting identifiers are filtered to generate a list of candidate identifiers using the sparse data. The plurality of preexisting identifiers correspond to a plurality of preexisting users. A core identifier is selected by determining a match between the identifier and a preexisting identifier from the preexisting identifiers using distance information generated using the list of candidate identifiers. The core identifier is matched to the identifier using the match to identify the unidentified user as a preexisting user from the plurality of preexisting users.
Aspects of the present disclosure provide techniques for application navigation recommendations using machine learning. Embodiments include determining one or more pages accessed by a user within an application. Embodiments include providing one or more inputs to a machine learning model based on the one or more pages accessed by the user. Embodiments include receiving, from the machine learning model based on the one or more inputs, one or more predicted pages. Embodiments include displaying, in a user interface, one or more elements recommending the one or more predicted pages to the user. Embodiments include receiving a selection of a given element of the one or more elements. Embodiments include navigating within the user interface, based on the selection, to a given page of the one or more predicted pages that corresponds to the given element.
A computing system generates a plurality of training data sets for generating the NLP model. The computing system trains a teacher network to extract and classify tokens from a document. The training includes a pre-training stage where the teacher network is trained to classify generic data in the plurality of training data sets and a fine-tuning stage where the teacher network is trained to classify targeted data in the plurality of training data sets. The computing system trains a student network to extract and classify tokens from a document by distilling knowledge learned by the teacher network during the fine-tuning stage from the teacher network to the student network. The computing system outputs the NLP model based on the training. The computing system causes the NLP model to be deployed in a remote computing environment.
A computing system receives, from a client device, an image of a content item uploaded by a user of the client devices. The computing system divides the image into one or more overlapping patches. The computing system identifies, via a first machine learning model, one or more distortions present in the image based on the image and the one or more overlapping patches. The computing system determines that the image meets a threshold level of quality. Responsive to the determining, the computing system corrects, via a second machine learning model, the one or more distortions present in the image based on the image and the one or more overlapping patches. Each patch of the one or more overlapping patches are corrected. The computing system reconstructs the image of the content item based on the one or more corrected overlapping patches.
A method may include obtaining a knowledge graph including entities and relationships between pairs of entities, determining, for the knowledge graph, a first state including a first subset of the entities that are selectable by a user, receiving, from the user, a selection of an entity from the first subset, responsive to receiving the selection, determining, for the knowledge graph, a second state including a second subset of the entities that are selectable by the user, and generating a report using the knowledge graph and the second state.
A method utilizes a framework for transaction categorization personalization. A transaction record is received. a baseline model is selected from a plurality of machine learning models. An account identifier, corresponding to the transaction record using the baseline model, is selected. The account identifier for the transaction record is presented.
A method categorizes transaction records. A transaction record is received by a server application. The transaction record is encoded with a first machine learning model to obtain a transaction vector, wherein the transaction vector is in a same vector space as multiple account vectors. A second machine learning model executing in the server application, selects an account vector, from the multiple account vectors, corresponding to the transaction vector. An account identifier, corresponding to the account vector, is presented for the transaction record.
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
A method secures hash chains via hybrid consensus. A proximate payload for a proximate chain block for a proximate chain is obtained. A first identifier of the proximate chain and the proximate payload are hashed with a hash function to generate a second identifier of the proximate chain. The proximate chain block is added to the proximate chain. The proximate chain block includes the first identifier, the second identifier, and the proximate payload. A request to add the second identifier to a reporting chain is transmitted. A response hldicating that the second identifier is incorporated into the reporting chain using the consensus mechanism is received.
This disclosure provides systems, methods and apparatuses for scheduling tasks in an electronic system. The scheduling system allows performance-based access to a task schedule. The method comprises: receiving a number of tasks to be completed; generating a schedule including timeslots accessible by a first set of resources, each timeslot representing a period of time allocated for the resources in the first set to complete a first subset of the tasks; determining an amount of time required by each resource to complete a respective one of the tasks; assigning a proficiency score to each resource based at least in part on the amount of time required by the resource to complete the task; and iteratively adding each resource to the first set of resources based at least in part on the proficiency score assigned to each resource
A method performs personalized transaction categorization. A transaction record is received, by a server application. In a first stage, sparse raw features are extracted from a transaction record of a transaction and converted into a transaction vector including dense features. In a second stage, the transaction vector is classified into a customized chart of accounts using the dense features to generate adapter model output. The method further includes selecting, an account identifier, corresponding to the transaction record and to an account of the customized chart of accounts, using the adapter model output, and presenting the account identifier for the transaction record.
A method may include executing a baseline classifier on unreviewed transaction features of an unreviewed transaction record to obtain a baseline account identifier, and executing a comparison model on (i) an unreviewed transaction vector of the unreviewed transaction record and (ii) reviewed transaction vectors to obtain comparison scores. The reviewed transaction vectors may correspond to reviewed transaction records each having a user-approved account identifier. The method may further include selecting, using the comparison scores, a reviewed transaction record. The reviewed transaction record may correspond to a comparison score. The comparison score may correspond to a user- approved account identifier of the reviewed transaction record. The method may further include selecting, using the comparison score, one of the baseline account identifier and the user-approved account identifier to obtain a selected account identifier, and presenting the selected account identifier for the unreviewed transaction record.
Aspects of the present disclosure provide techniques for expert matching though workload intelligence. Embodiments include receiving a request for a support engagement. Embodiments include receiving workload data of a plurality of experts. Embodiments include determining a workload capacity of each respective expert based on the respective workload data for the respective expert. Embodiments include determining a respective estimated completion time for the support engagement for each of the plurality of experts using a machine learning model. Embodiments include determining match scores for the support engagement and each of the plurality of experts based on the estimated completion times and the workload capacities. Embodiments include selecting a given expert of the plurality of experts to handle the support engagement based on the match scores.
This disclosure relates to predictions based on a Bernoulli uncertainty characterization used in selecting between different prediction models. An example system is configured to perform operations including determining a prediction by a first prediction model. The first prediction model is associated with a loss function. The system is also configured to determine whether the prediction is associated with the first prediction model or a second prediction model based on a joint loss function. The second prediction model is associated with a likelihood function, and the joint loss function is based on the loss function and the likelihood function. The system is further configured to indicate the prediction to the user in response to determining that the prediction is associated with the first prediction model. If the prediction is associated with the second prediction model, the system may prevent indicating the prediction to the user.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
A computer-implemented system and method for generating heterogeneous graph feature embeddings for feature learning and prediction. An application server may receive and process a plurality of feature datasets to generate a graph data structure comprising a plurality of interconnected transaction pairs. The application server processes the graph data structure to determine a first-order transaction pair corresponding to a maximum transaction frequency based on a user identifier; executes a jumping probability algorithm to process the graph data structure to determine a second-order transaction pair jumping from a first-order transaction pair; and generates a transaction sequence associated with the user identifier.
Systems and methods for providing contextual information for computerized document understanding. The systems and methods can be used to assist users in filling out documents by providing contextual information based on anomalies identified in a provided document. The methods and systems may identify the deficiency in the document and automatically generate a query related to the anomaly. The query can be fed as an input to a question-answering (QA) model that can provide an answer as the contextual information.
After a host client establishes a multimedia stream with a guest client, host data is received from a host application. A state machine is updated using the host data. The host application executes on the host client. Guest data is received from a guest application. The state machine is updated using the guest data. The guest application executes on the guest client. Transaction data is propagated between the host application and the guest application. The transaction data is presented with the multimedia stream. The transaction data includes the host data and the guest data. Provider data is generated responsive to updating the state machine with the host data and the guest data. The provider data is sent to the guest client. The provider data is presented with the multimedia stream by the guest application on the guest client.
H04L 65/00 - Dispositions, protocoles ou services dans les réseaux de communication de paquets de données pour prendre en charge les applications en temps réel
H04N 21/254 - Gestion au sein du serveur de données additionnelles, p.ex. serveur d'achat ou serveur de gestion de droits
H04N 21/262 - Ordonnancement de la distribution de contenus ou de données additionnelles, p.ex. envoi de données additionnelles en dehors des périodes de pointe, mise à jour de modules de logiciel, calcul de la fréquence de transmission de carrousel, retardement d
H04N 21/458 - Ordonnancement de contenu pour créer un flux personnalisé, p.ex. en combinant une publicité stockée localement avec un flux d'entrée; Opérations de mise à jour, p.ex. pour modules de système d'exploitation
H04N 21/478 - Services additionnels, p.ex. affichage de l'identification d'un appelant téléphonique ou application d'achat
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
65.
CARD READER BASED PAYMENT TRANSACTIONS FROM A WEB BROWSER
This disclosure relates to systems and methods for processing electronic payments for customer purchases. In some implementations, a mobile computing device receives a payment request identifying a sales transaction between a merchant and a customer. The payment request indicates a purchase amount owed by the customer to the merchant, and carries a set of instructions. The mobile computing device activates a card reader based at least in part on the set of instructions, receives customer authorization for electronic payment of the purchase amount, instructs the card reader to process a credit card for the purchase amount based on receiving the customer authorization, and receives confirmation of payment of the purchase amount from the card reader.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p.ex. cartes à puces ou cartes magnétiques
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
66.
CARD READER BASED PAYMENT TRANSACTIONS FROM A WEB BROWSER
This disclosure relates to systems and methods for processing electronic payments for customer purchases. In some implementations, a system receives a payment request from a merchant, and transmits payment information to a mobile computing device associated with the merchant. The payment information includes a transaction ID to identify the purchase, a purchase amount, and instructions that cause the mobile computing device to activate a card reader, present a notification of the purchase amount to the customer, receive an acceptance of the purchase amount from the customer, and authorize the card reader to process an electronic payment for the purchase amount. The system receives payment confirmation from the mobile computing device, and provides the parameter confirmation to the merchant.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p.ex. cartes à puces ou cartes magnétiques
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
67.
COMBINING RULES-BASED KNOWLEDGE ENGINEERING WITH MACHINE LEARNING PREDICTION
The present invention provides a method of predicting values for one or more fields of a knowledge engineering (KE) data model, performed by one or more computing device associated with one or more machine learning models comprising the steps of identifying a first field in the KE data model, determining whether each of the one or more dependent fields has a respective value in the KE data model, executing multiple first machine learning models to predict one or more values for the first field, selecting one of the one or more predicted values as a representative value of the first field, identifying one or more further fields in the KE data model, and calculating values for each of the one or more further fields based at least in part on the representative value of the first field. Such rule-based systems are useful for document understanding, business management and other applications.
A method may include clustering form elements into line objects and columns of a table of a structured representation by applying a trained multi -dimensional clustering model to spatial coordinates of the form elements, and assigning a table header line type to a table header line object of the line objects based on a spatial coordinate of the table header line object relative to a spatial coordinate of a topmost table data line object of the line objects, and a determination that a number of columns of the table header line object is within a threshold of a number of columns of the topmost table data line object. The topmost table data line object may be assigned a table data line type. The method may further include presenting the stmctured representation to a user.
This disclosure relates to systems and methods for constructing a customized debt reduction plan for a user. In some implementations, a customized debt reduction system obtains a plurality of financial attributes of the user and a plurality of other users, where the plurality of financial attributes are indicative of credit card debt, and identifies users from the plurality of other users who successfully repaid their credit card debt based on their respective financial attributes and one or more repayment techniques that resulted in successful repayment of their credit card debt. The customized debt reduction system correlates the plurality of financial attributes of the user with the plurality of financial attributes of a number of the identified users and determines a personalized score for the user, using a trained machine learning model, based on the correlation to determine a customized debt reduction plan for the user based on the personalized score.
Systems and models are disclosed for determining a value over replacement feature (VORF) for one or more features of a machine learning model. An example method includes selecting one or more features used in the machine learning model, determining a comparison set of unused features not used in the machine learning model, for each unused feature in the comparison set, determining a difference in a specified metric when the selected one or more features are replaced by a corresponding unused feature from the comparison set, and determining the VORF to be the smallest difference in the specified metric.
Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include receiving a historical support record comprising time-stamped actions, a support initiation time, and an account indication. Embodiments include determining features of the historical support record based at least on differences between times of the time- stamped actions and the support initiation time. Embodiments include determining a label for the features based on the account indication. Embodiments include training an ensemble model, using training data comprising the features and the label, to determine an indication of an account in response to input features, wherein the ensemble model comprises a plurality of tree-based models and a ranking model.
Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include determining a set of unlabeled user transaction records associated with a user. Embodiments include selecting a first unlabeled user transaction record associated with a first vendor from the set of unlabeled user transaction records based on a transaction record prioritization scheme. Embodiments include presenting the first unlabeled user transaction record to the user in a label query. Embodiments include receiving, from the user in response to the label query, a label of a first account for the first unlabeled user transaction record. Embodiments include selecting a second unlabeled user transaction record associated with a second vendor from the set of unlabeled user transaction records based on: the transaction record prioritization scheme, and a determination that the second vendor is least likely to be categorized by the user in the first account.
Systems and methods that may be used to provide personalized financial nudges to users of a financial service that may be used to further the users' savings intentions (e.g., a savings goal, an emergency fund, etc.). The disclosed systems and methods may increase user interactivity with the financial service and the services it offers by providing personalized nudges that are based on, among other things, an evaluation of various behavioral economics principles. A machine learning recommendation system may be used to fit and output different nudges to users in a personalized way to maximize their savings' intentions.
A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.
A computer-implemented method and system are provided to perform a machine learning pipeline process to produce an explainable machine learning model. A computing device may be configured to train a plurality of machine learning models with a set of respective feature datasets to generate an accuracy and explainability property for each trained model. The computing device may evaluate a plurality of the trained machine learning models and select a model as an explainable machine learning model based on at least one of the accuracy and the explainability property.
Certain aspects of the present disclosure provide techniques for processing natural language utterances in a knowledge graph. An example method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application. Operands and operators are extracted from the natural language utterance using a natural language model. Operands may be mapped to nodes in a knowledge graph, the nodes representing values calculated from data input into the application, and operators may be mapped to operations to be performed on data extracted from the knowledge graph. The functions associated with the operators are executed using data extracted from the nodes in the knowledge graph associated with the operands to generate a query result. The query result is returned as a response to the received long-tail query.
A method for using piecewise forecasts involves obtaining, by a model discovery service, a plurality of models and generating, by a demand prediction service, a plurality of values for a time series variable. The plurality of values corresponding to a plurality of days to be predicted. The method further involves inputting the plurality of values for the time series variable as part of a piecewise forecast to a headcount estimation service and generating, by the headcount estimation service with the piecewise forecast, an estimated headcount from the time series variable.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisation; Planification des actions en fonction des objectifs; Analyse ou évaluation de l’efficacité des objectifs
H04M 3/50 - Dispositions centralisées pour répondre aux appels; Dispositions centralisées pour enregistrer des messages pour abonnés absents ou occupés
Aspects of the present disclosure provide techniques for dynamic location tracking. Embodiments include receiving a plurality of location records associated with a site, wherein each respective location record of the plurality of location records comprises respective location coordinates of a respective device associated with the respective location record. Embodiments include determining respective distances from a center point of the site to the respective location coordinates in each respective location record of the plurality of location records. Embodiments include determining a radius of a region definition for the site based on the respective distances. Embodiments include receiving a device location from a device associated with a user. Embodiments include performing, based on the device location and the region definition, one or more location-based operations.
Systems and methods that may implement an Oracle-aided protocol for producing and using FHE encrypted data. The systems and methods may initially encrypt and store input data in one encrypted form that is not performed using FHE, which does not substantially increase the size of the data and storage resources required to store the encrypted data. In accordance with the Oracle-aided protocol, the encrypted data is re-encrypted as FHE encrypted data when FHE encrypted data is required.
Systems and methods providing dynamic deep web page navigation using keyboard navigation. The method comprising: outputting a page of a GUI having a first region comprising first selectable links to first sub-pages to a display; outputting a first menu including first options corresponding to the first links at a second region on the page; receiving selection of a first option; determining if a first sub-page associated with a first link corresponding to the selected first option comprises second selectable links to second sub-pages; outputting a second menu at the second region when the first sub-page associated with the first link comprises second links to second sub-pages, the second menu comprising second options corresponding to the second links; loading the first subpage associated with the first link corresponding to the selected first option when the first sub-page associated with the first link does not comprise second links to second sub-pages.
A machine learning system executed by a processor may generate predictions for a variety of natural language processing (NLP) tasks. The machine learning system may include a single deployment implementing a parameter efficient transfer learning architecture. The machine learning system may use adapter layers to dynamically modify a base model to generate a plurality of fine-tuned models. Each fine-tuned model may generate predictions for a specific NLP task. By transferring knowledge from the base model to each fine-tuned model, the ML system achieves a significant reduction in the number of tunable parameters required to generate a fine- tuned NLP model and decreases the fine-tuned model artifact size. Additionally, the ML system reduces training times for fine-tuned NLP models, promotes transfer learning across NLP tasks with lower labeled data volumes, and enables easier and more computationally efficient deployments for multi-task NLP.
ABSTRACT Aspects of the present disclosure provide techniques for providing a graphical user interface. Embodiments include displaying a text input field. Embodiments include receiving an input of at least a portion of a tag via the text input field. Embodiments include displaying, in response to the input and proximate to the text input field, a graphical representation of an existing tag that relates to the input. The graphical representation includes a type of the existing tag, the existing tag, and a colored section on a right side or a left side of the graphical representation having a color that is associated with the type of the existing tag in the computing application. Embodiments include receiving a selection of the graphical representation and displaying an instance of the graphical representation inside of the text input field. Date Recue/Date Received 2021-04-30
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p.ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comport
G06F 3/04847 - Techniques d’interaction pour la commande des valeurs des paramètres, p.ex. interaction avec des règles ou des cadrans
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
A method for using shareable and nested transaction on hash chains includes storing transaction data of a transaction of a hash chain. A lock block is appended to the hash chain. Appending the lock block includes setting a tail block identifier of the hash chain from a preceding tail block of a preceding transaction to the lock block. A data block is appended to the hash chain. Appending the data block includes setting the tail block identifier of the hash chain to the data block. The method further includes removing the transaction data from the transaction without invalidating the hash chain. The method further includes appending an updated data block to the hash chain to update the transaction with updated transaction data.
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
G06F 7/00 - Procédés ou dispositions pour le traitement de données en agissant sur l'ordre ou le contenu des données maniées
G06Q 20/00 - Architectures, schémas ou protocoles de paiement
84.
METHOD AND SYSTEM FOR GENERATING SYNTHETHIC DATA USING A REGRESSION MODEL WHILE PRESERVING STATISTICAL PROPERTIES OF UNDERLYING DATA
A method for generating a synthetic dataset involves generating discretized synthetic data based on driving a model of a cumulative distribution function (CDF) with random numbers. The CDF is based on a source dataset. The method further includes generating the synthetic dataset from the discretized synthetic data by selecting, for inclusion into the synthetic dataset, values from a multitude of entries of the source dataset, based on the discretized synthetic data, and providing the synthetic dataset to a downstream application that is configured to operate on the source dataset.
G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
85.
ENHANCED INTENT MATCHING USING KEYWORD-BASED WORD MOVER'S DISTANCE
Aspects of the present disclosure provide techniques for intent matching. Embodiments include receiving input of text by a user via a user interface. Embodiments include determining weights for portions of the text based on a plurality of keywords. Embodiment include generating an embedding of the text. Embodiments include determining an intent of the text by weighting, based on the weights, word mover's distances from the embedding of the text to a known embedding of known text associated with the intent in order to determine a similarity measure between the text and the known text. Embodiments include providing content to the user via the user interface based on the intent.
Certain aspects of the present disclosure provide techniques for node matching with accuracy by combining statistical methods with a knowledge graph to assist in responding (e.g., providing content) to a user query in a user support system. In order to provide content, a keyword matching algorithm, statistical method (e.g., a trained BERT model), and data retrieval are each implemented to identify node(s) in a knowledge graph with encoded content relevant to the user's query. The implementation of the keyword matching algorithm, statistical method, and data retrieval results in a matching metric score, semantic score, and graph metric data, respectively. Each score associated with a node is combined to generate an overall score that can be used to rank nodes. Once the nodes are ranked, the top ranking nodes are displayed to the user for selection. Based on the selection, content encoded in the node is displayed to the user.
At least one processor may obtain a document comprising text tokens. The at least one processor may determine, based on a pre-trained language model, word embeddings corresponding to the text tokens. The at least one processor may determine, based on the word embeddings, named entities corresponding to the text tokens; and one or more accuracy predictions corresponding to the named entities. The at least one processor may compare the one or more accuracy predictions with at least one threshold. The at least one processor may associate, based on the comparing, the named entities with one or more confidence levels. The at last one processor may deliver the named entities and the one or more confidence levels.
1NTU1811355W0 ABSTRACT Systems and methods for forecasting future values of data streams are disclosed. One example method may include receiving information characterizing each of a plurality of forecasting models, retrieving historical data for each of a plurality of data streams, determining one or more constraints, dynamically selecting one of the plurality of forecasting models for each of the data streams based on accuracy metrics for the forecasting models, estimating cost metrics associated with each forecasting model, dynamically selecting the forecasting model based at least in part on the accuracy metrics, the cost metrics, and the determined constraints, and forecasting a first subsequent value of each data stream using the corresponding selected forecasting model. 26 Date Recue/Date Received 2021-04-19
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
INTU18 11354WO ABSTRACT Systems and methods for forecasting cashflows across one or more accounts of a user disclosed. One example method may include retrieving a data set for each of a plurality of accounts from a database, constructing a graph including a plurality of nodes linked together by a multitude of edges, wherein each node identifies a time series value corresponding to one of the accounts, and each edge indicates a time series value of a corresponding set of transactions occurring between a corresponding pair of accounts, determining a plurality of constraints, determining a specified loss function based on the plurality of constraints, back-propagating a derivative of the specified loss function into a deep neural network (DNN) to determine a set of neural network parameters, forecasting, using the DNN, a time sequence for one or more of the nodes and one or more of the edges, and providing the forecasted time sequences to the user. Date Recue/Date Received 2021-04-21
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
G06Q 40/02 - Opérations bancaires, p.ex. calcul d'intérêts ou tenue de compte
Aspects of the present disclosure provide techniques for explainable payroll calculations. Embodiments include receiving a request from a client for a rule-based calculation. Embodiments include receiving one or more user values related to the request. Embodiments include using a calculation graph to determine the rule-based calculation based on the one or more user values. The calculation graph may comprise at least one node that performs an operation using the one or more user values. Embodiments include identifying an explanation template associated with the at least one node. Embodiments include generating an explanation of the rule-based calculation based on the explanation template and the one or more user values. Embodiments include providing the rule-based calculation and the explanation of the rule-based calculation to the client in response to the request.
Certain aspects of the present disclosure provide techniques for mapping natural language to stored information. The method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application associated with a set of topics and providing the natural language utterance to a natural language model configured to identify nodes of a knowledge graph. The method further includes, based on output of the natural language model, identifying a node of a knowledge graph associated with the natural language utterance, wherein the output of the natural language model includes a node identifier for the node of the knowledge graph and providing the node identifier to the knowledge engine. The method further includes receiving a response associated with the node of the knowledge graph from the knowledge engine and transmitting the response to the user in response to the long-tail query.
Certain aspects of the present disclosure provide techniques for providing assistance to users by integrating social computing system with conversational user interface. In some cases, a user interacting with a virtual assistant of a conversational user interface provides input that the virtual assistant is not able identify a matching intent. As a result, the virtual assistant can leverage the social computing system to generate a new question based on the user input and post the question to the social computing system. Users of the social computing system can provide an answer, which the virtual assistant provides to the user in the conversational user interface. The social computing system can also generate a new intent for the virtual assistant to increase efficiency of the virtual assistant.
A document extraction system executed by a processor, may process documents using manual and automated systems. The document extraction system may efficiently route tasks to the manual and automated systems based on a predicted probability that the results generated by the automated system meet some baseline level of accuracy. To increase document processing speed, documents having a high likelihood of accurate automated processing may be routed to an automated system. To ensure a baseline level of accuracy, documents having a smaller likelihood of accurate automated processing may be routed to a manual system.
Systems and methods that approximate and use branching operations on data encrypted by fully homomorphic encryption (FHE). The systems and methods may use polynomial approximation to convert "if" statements into "soft if" statements that may be applied to the FHE encrypted data in a manner that preserves the security of the systems and methods.
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
G06F 9/30 - Dispositions pour exécuter des instructions machines, p.ex. décodage d'instructions
H04L 9/06 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p.ex. système DES
95.
EXTENDING FINITE RANK DEEP KERNEL LEARNING TO FORECASTING OVER LONG TIME HORIZONS
In one embodiment a finite rank deep kernel learning method includes: receiving a training dataset; forming a plurality of training data subsets from the training dataset; for each respective training data subset of the plurality of training data subsets: calculating a subset- specific loss based on a loss function and the respective training data subset; and optimizing a model based on the subset-specific loss; determining a set of embeddings based on the optimized model; determining, based on the set of embeddings, a plurality of dot kernels; combining the plurality of dot kernels to form a composite kernel for a Gaussian process; receiving live data from an application; and predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.
Certain aspects of the present disclosure provide techniques for performing finite rank deep kernel learning. In one example, a method for performing finite rank deep kernel learning includes receiving a training dataset; forming a set of embeddings by subjecting the training dataset to a deep neural network; forming, from the set of embeddings, a plurality of dot kernels; linearly combining the plurality of dot kernels to form a composite kernel for a Gaussian process; receiving live data from an application; and predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.
Certain aspects of the present disclosure provide techniques for entering user credentials through a proxy. One example method generally includes receiving, at a user device, a push request for user data from a cloud server and receiving a request file from an aggregation system. The method further includes injecting user credentials stored on the user device into the request file, wherein when injected the user credentials replace at least one dummy entry of the request file, and transmitting the request file to a data source associated with the request file. The method further includes receiving user data from the data source and transmitting the user data to the aggregation system.
A method and system for automatically and dynamically creating routes between message dropboxes in separate data center infrastructures. The method and system establishes a first message dropbox, including first property data defining properties of the first message dropbox, in a first data center infrastructure; and establishes a second message dropbox, including second property data defining properties of the second message dropbox, in a second data center infrastructure separate from the first data center infrastructure. The system and method identifies that a message has been dropped in the first message dropbox; reads the first property data of the first message dropbox; detects, based on the first property data, that the first message dropbox is routable to the second message dropbox and automatically writes, in a routing service that carries messages between the first and second datacenter infrastructures, a route definition defining a route between the first and second message dropboxes.
Arbitrary image data may be transformed into data suitable for optical character recognition (OCR) processing. A processor may generate a plurality of intermediate feature layers of an image using convolutional neural network (CNN) processing. For each intermediate feature layer, the processor may generate at least one text proposal using a region proposal network (RPN). The at least one text proposal may comprise a portion of the intermediate feature layer that is predicted to contain text. The processor may merge the text proposals with one another to form a patch of the image that is predicted to contain text. The processor may determine outer coordinates of the patch. The outer coordinates may comprise at least leftmost, rightmost, topmost, and bottommost coordinates. The processor may generate a quadrilateral of the image that is a smallest quadrilateral including the leftmost, rightmost, topmost, and bottommost coordinates.
Certain aspects of the present disclosure provide techniques for providing a diagnostics framework for large scale hierarchical time series forecasting models. In one embodiment, a method includes providing a plurality of hierarchical time-series, each of the plurality of hierarchical time-series comprising node data; concurrently providing node data from the plurality of hierarchical time-series to a forecasting model; using the forecasting model, concurrently calculating a plurality of forecasting data corresponding to each one of the node data of the plurality of hierarchical time-series; concurrently calculating a plurality of performance metrics of the forecasting model using the plurality of forecasting data; and generate an updated forecasting model by modifying the forecasting model based upon the plurality of performance metrics; concurrently calculating a plurality of updated forecasting data corresponding to each one of the node data using tire updated forecasting model; and provide the updated forecasting data to a user.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"