A computer-implemented method includes: receiving, by a computing device, a data restore request; determining, by the computing device, at least one object in response to the data restore request; and querying, by the computing device, a client population to determine at least one client which has resources and network cardinality to assist the data restore request. The at least one client creates a distributed and crowd sourced cache of objects for the data restore request.
G06F 12/00 - Accessing, addressing or allocating within memory systems or architectures
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
H04L 43/0876 - Network utilisation, e.g. volume of load or congestion level
2.
RECRUITMENT AUGMENTATION WITH DIGITAL FOOTPRINT UTILIZATION
A method including: identify candidate pool resources by analyzing a job description; posting the job description at the candidate pool resources; identifying candidates from the candidate pool resources; determining a candidate score of each of the candidates based on an identified candidate information source; and selecting a set of the candidates based on the candidate score.
In one general embodiment, a computer-implemented method includes collecting information relating at least to market trends and problem ticketing. The collected information is stored in a knowledge repository. At least some of the collected information is processed to compute weightage scores for requirements specified in a policy configuration. A list comprising at least some of the requirements and indications of the weightage scores corresponding thereto is generated and output.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
G06Q 30/0202 - Market predictions or forecasting for commercial activities
A computer-implemented method, in accordance with one embodiment, includes receiving a taxonomy specifying entities of a business service and levels of said entities. Time series data about the entities is collected and stored. Impactor propagation paths between entities is identified. A territory of a health impact of each entity is also identified. A health score for each of the entities, considering impacts on a health of the entity by at least one other entity, is computed based on the data, the propagation paths, and the territories of the entities. At least one of the health scores is output.
H04L 43/0817 - Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning
Aspects of the present disclosure relate generally to machine inspection of part production and, more particularly, to systems and methods of automated Al inspection of customized part production. For example, a computer-implemented method includes receiving, by a processor, design information for a custom part; extracting, by the processor, feature information of the custom part from the design information; receiving, by the processor, images of the custom part in production from a recording in near real time; and verifying, by the processor, using machine learning that features in the images of the custom part in production are in compliance with the feature information of the custom part.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
6.
ORCHESTRATION OF A PATTERN-BASED CONFIGURABLE PROCESS SEQUENCE
A computer-implemented method, according to one approach, includes causing a predetermined tracker component to create a template tracker file based on a pattern-based configurable process sequence and causing a predetermined logger component to create a log of the pattern-based configurable process sequence. A playbook is generated based on the pattern-based configurable process sequence. The method further includes storing the template tracker file, a file including the log, and the playbook in storage for enabling auditing of the pattern-based configurable process sequence. A computer program product, according to another approach, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform the foregoing method.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
7.
AUTOMATED CUSTOMIZED MACHINE LEARNING MODEL VALIDATION FLOW
In one aspect, a computer-implemented method includes detecting, by one or more processing devices, custom goals of a specified machine learning application; determining, by the one or more processing devices, relative importance of a plurality of performance categories for the specified machine learning application, based on the custom goals of the specified machine learning application; generating, by the one or more processing devices, automated machine learning model tests based on the determined relative importance of the plurality of performance categories for the specified machine learning application; and performing, by the one or more processing devices, validation testing of the machine learning model based on the automated machine learning model tests.
A computer-implemented method includes: monitoring, by a computing device, a data set format of all files in a computing system to determine a list of data sets in a first format which are accessed greater than a predetermined number of times; gathering, by the computing device, the list of data sets in the first format within the computing system; and converting, by the computing device, the list of data sets in the first format within the computing system to a second format which is different from the first format.
A computer implemented system, method, and computer program product are disclosed for managing a machine learning model operation (MLOps) for cost forecasting models. The MLOps receives a request for a subscription machine learning (ML) model by a tenant having a corresponding tenant configuration profile and automatically selects a subscription ML model in a model registry based on the tenant configuration profile. The MLOps deploys the selected subscription ML model to the tenant and monitors usage by the tenant of the currently operating ML model at a pre-determined refresh frequency. The MLOps determines whether the currently operating ML model exceeds a pre-determined accuracy threshold, and automatically deploys a second subscription ML model from the model registry to the tenant in place of the currently operating ML model in response to the pre-determined accuracy threshold being exceeded.
The exemplary embodiments disclose a method, a computer program product, and a computer system for managing user commands. The exemplary embodiments may include a user giving one or more commands to one or more devices, collecting data of the one or more commands, extracting one or more features from the collected data, and determining which one or more of the commands should be executed on which one or more of the devices based on the extracted one or more features and one or more models.
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
G10L 15/02 - Feature extraction for speech recognition; Selection of recognition unit
G10L 15/16 - Speech classification or search using artificial neural networks
G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks
H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
A computer-implemented method for model building with explainability is provided. The method includes receiving, by a hardware processor, a first metric and a second metric of minimum model performance. The first metric relates to data modeling quality and the second metric relates to model to business rule correlations. The method further includes performing, by the hardware processor, auto Artificial Intelligence model generation responsive to training data and a combination of the first and the second metrics of minimum model performance to obtain a model that is trained and meets model prediction accuracy and model prediction explainability requirements represented by the combination of the first and the second metrics of minimum model performance.
An approach for restoring system functionality when a primary maintenance interface is unavailable is provided. In an embodiment, a computer maintenance application that is configured to use a Virtual Telecommunications Access Method (VTAM) interface to connect to a mainframe system is retrieved. A mainframe system is accessed with the computer maintenance application using the VTAM interface. During the access, a maintenance activity is performed on the mainframe system using the computer maintenance application.
A computer-implemented method includes: advertising, by a computing device, at least one service including information from a learned knowledge base by a service provider vehicle within an autonomous vehicle ecosystem; connecting, by the computing device, the service provider vehicle to a service consumer vehicle within the autonomous vehicle ecosystem through a communication network; and sharing, by the computing device, the at least one service including the information from the learned knowledge base through a connection between the service provider vehicle and the service consumer vehicle using the communication network.
Disclosed embodiments provide a computer-implemented method for cloud computing infrastructure cost forecasting. Resource profiles are computed for one or more cloud resources. A scheduled pattern detection process is performed for each of the one or more cloud resources to check for periodic behaviors. A similar consumer detection process is performed for each of the one or more cloud resources to identify other entities that have a similar cloud computing resource usage pattern, which can serve as supervised learning data for neural networks of disclosed embodiments. Data is input to a neural network, where the input data includes the resource profile, an operational maturity score, and/or one or more similar consumer patterns, in order to obtain a cost forecast from the neural network, based on the input data.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Hardware and software on a computing device is analyzed based on a regulatory profile for the computing device and regulatory compliance for an entity associated with the computing device. A determination is made whether at least one of the hardware and software on the computing device includes at least one regulatory non-compliance issue. In response to determining that at least one of the hardware and software on the computing device includes at least one regulatory non-compliance issue, one or more scripts are executed on the hardware and software on the computing device to cause the hardware and software to resolve the at least one regulatory non-compliance issue based on the regulatory profile for the computing device.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06Q 10/0635 - Risk analysis of enterprise or organisation activities
An approach within the embodiments of the present invention includes providing multi-tenancy support on a dynamic host configuration protocol (DHCP) protocol, and further includes receiving the dynamic host configuration protocol (DHCP) packet, inserting a tenant-specific option information within the DHCP packet, and transmitting the dynamic host configuration protocol (DHCP) packet with the tenant-specific option information.
A method is provided that includes configuring a cellular phone to function as a mobile debit card by installing an application for contactless interfacing with ATMs. The method supplements the application with a neural network (NN) based biometric verification process configured to reduce an incorrect user error value over time to increasingly harden the application to undesired intrusion. The NN based biometric verification process comprises: performing an initial face recognition; greeting the user with one of a plurality of questions in a specific language of the user and evaluating a pre-defined answer provided from the user in the specific language; and detecting, in an acoustic utterance having dialogue in support of an ATM session, a voice and a prosody style indicative of the user in combination with lip and face movements made by the user corresponding to and in synchronization with the acoustic utterance.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
G10L 15/18 - Speech classification or search using natural language modelling
18.
GENERATION OF DATA VISUALIZATIONS ON A SINGLE VISUAL REPRESENTATION
A computer-implemented method, in accordance with one embodiment, includes collecting data relating to development of a software product, the collected data including a plurality of different types of data relating to the development of the software product. A portion of the collected data is selected based on a characteristic of an intended user, the portion of the collected data including a plurality of the types of data. The selected portion of the collected data is transformed into data visualizations representing the data, the different types of the data having different data visualizations relative to one another. The data visualizations are output in a single visual representation for display to the intended user.
A computer-implemented method, according to one embodiment, includes logically partitioning a storage system into a plurality of compartment constructs, and mapping hosts in communication with the storage system to the compartment constructs, thereby enabling interoperability among the hosts and the compartment constructs. The interoperability of the hosts and the compartment constructs is analyzed, and the interoperability is based on storage software and/or firmware versions being run by the hosts. The method further includes defining, based on the analysis, risk profiles for applications run on the hosts, and determining, based on the risk profiles, recommendations for assignment and mapping of the hosts with the compartment constructs. Ownership of storage objects is assigned to the compartment constructs based on the recommendations. Each of the storage objects define a logical partition of one of the hosts and a logical partition of a storage volume of the storage system.
Aspects of the present disclosure relate generally to the formation of project teams and, more particularly, to systems and method of forming synergistic teams. For example, a computer-implemented method includes receiving, by a processor, a project profile including project skills; searching, by the processor, employee profiles for employee skills matching the project skills; selecting, by the processor, at least one group of team candidates having employee profiles with employee skills collectively matching the project skills; determining, by the processor, a synergy score for the at least one group of team candidates; and saving, by the processor, the synergy score and the group of team candidates in persistent storage.
An example operation may include one or more of storing code that adheres to predefined coding standards of one or more programming languages within a data store, reading source code from a code file, comparing the source code to the code stored within the data store to determine one or more recommended code changes to the source code, and displaying the one or more recommended code changes via a user interface.
Embodiments include receiving input of a new message for a group of members having end-to-end encryption in which first keys encrypt and second keys decrypt the new message, determining that a subset of the members in the group is excluded from receiving the new message, and selectively encrypting the new message for the members of the group by encrypting the new message by first keys corresponding to ones of the members of the group while choosing not to encrypt the new message with first keys corresponding to the subset of the members. An aspect includes transmitting the new message encrypted by the first keys to the members, and in response to choosing not to encrypt the new message with first keys corresponding to the subset, causing a system message to be transmitted to the subset excluded from receiving the new message, the system message affecting a presentation to the subset.
A computer-implemented method for receiving, at a database, an analytics request associated with a selected data line from a database and reading a set of additional bytes from the database corresponding to the selected data line. The method may further include parsing and formatting the set of additional bytes read from the database to generate, at the database, an analytics reply to the analytics request, where each of a plurality of data lines in the database represents a search query and where the analytics data comprises a number of search query requests on a subject matter and a number of search query requests for private data.
A method for database cloning is provided. The method initiates an outage-free backup of a production database of a source database system using a point-in-time copy technology which copies both data and object structure. The method executes a cloning command to open a dialog panel menu receiving as input a subsystem name used to generate a set of database restore jobs. The method executes the set of database restore jobs to clone data into a transient area. The method uses a copy management service to activate replication of the data from the source database system created using the copy technology to a target database system. The method exports a file catalog associated with the source database system to the target database system. The method executes a script to rename files according to specifications of the target database system and the file catalog.
A method, computer program product, and system include a processor(s) that obtains extracts, from a data source, atomic actions. The processor(s) traverses the atomic actions to identify one or more recommended actions. The processor(s) maps the recommended actions to the atomic actions by associating at least one atomic action with an expected improvement level from the data source. The processor(s) generates at least one directed graph based on the mapping. The processor(s) utilizes the matrix to filter the recommended actions, into a best plan nomination.
A computer-implemented method, according to one embodiment, includes identifying, in a reference video of a production process of a product, a discrete and non-overlapping set of first tasks. The first tasks define at least a first sub-process. The method further includes analyzing a live video of the production process to identify frames of the live video that include second tasks that define a second sub-process, and analyzing the frames of the live video for determining whether a match exists between the first tasks and the second tasks. In response to a determination that the match does not exist, an alert that a deviation is present in the production process depicted in the live video is output.
A computer-implemented method according to one embodiment includes receiving a request to perform a security policy implementation analysis for a first deployment associated with a first client in an IT environment. IT information associated with the first deployment is collected. The method further includes applying trained machine learning models to analyze the IT information of the first client to compute a security policy for the first deployment. The security policy is computed based on a calculated uncertainty of effects that applying the security policy to the first deployment is capable of causing, and a predicted amount of resources of the first deployment that applying the security policy to the first deployment would consume. An indication of the security policy is output for display in a dashboard on a display of a user device of the first client.
Embodiments relate to virtualization of digital crown actions based on actions performed with an electronic pen. A technique includes communicatively coupling a smartwatch to an electronic pen, the smartwatch including a rotatable piece, where the rotatable piece controls a plurality of functionalities of the smartwatch. The technique includes receiving data from the electronic pen, and in response to receiving the data from the electronic pen, controlling at least one of the plurality of functionalities.
G06F 3/0354 - Pointing devices displaced or positioned by the user; Accessories therefor with detection of 2D relative movements between the device, or an operating part thereof, and a plane or surface, e.g. 2D mice, trackballs, pens or pucks
G04G 21/08 - Touch switches specially adapted for time-pieces
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
G06F 3/0362 - Pointing devices displaced or positioned by the user; Accessories therefor with detection of 1D translations or rotations of an operating part of the device, e.g. scroll wheels, sliders, knobs, rollers or belts
G06F 3/038 - Control and interface arrangements therefor, e.g. drivers or device-embedded control circuitry
G06F 3/0346 - Pointing devices displaced or positioned by the user; Accessories therefor with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
A random password policy for a specific user associated with an entity is generated based on a global password requirement. A new password created by the specific user based on the generated random password policy is identified. That the new password complies with a set of requirements specified by the generated random password policy is confirmed.
A computer-implemented method according to one embodiment includes accessing a copy of a ledger that includes information associated with a plurality of potential recovery sites of a multi-site environment. The information of the copy of the ledger is stored on a blockchain by the potential recovery sites. A current threat to a production site of the multi-site environment is identified. The method further includes, analyzing, based on the current threat, the copy of the ledger to determine one of the potential recovery sites to use as a failover for the production site, and in response to a determination that the current threat has caused a disaster event on the production site, causing the production site to failover to the determined recovery site.
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
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
In an approach for multifactor authorization on hardware calls of resources, a processor receives a request for a hardware resource from a plurality of hardware resources being monitored. A processor calculates a risk level associated with the hardware resource of the request based on a respective risk level data repository. A processor, in response to a determination the risk level requires multifactor authorization, determines that a user associated with the request is logged in. A processor identifies a mechanism used by the user to log in. A processor determines whether a challenge associated with the multifactor authorization based on the mechanism is successful. A processor, in response to a determination the challenge associated with the multifactor authorization is successful, enables access to the hardware resource of the request.
G06F 21/52 - 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
G06F 21/40 - User authentication by quorum, i.e. whereby two or more security principals are required
32.
DETERMINING A NEW CANDIDATE FEATURE FOR A PREDETERMINED PRODUCT BASED ON AN IMPLICIT REQUEST OF A USER
A computer-implemented method, according to one embodiment, includes collecting user data associated with a user's behavior with respect to a predetermined product and analyzing the user data for determining a subset of the user data that constitutes an implicit request for a new candidate feature for the predetermined product. A new candidate feature for the predetermined product that satisfies the implicit request is determined based on the subset of the user data. The method further includes outputting an indication of the determined new candidate feature to a device associated with development of the predetermined product. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform the foregoing method.
A method, computer program product, and system include a processor(s) obtains various data relevant to one or more infected individuals (e.g., location data and infection data. The processor(s) identifies relationships and physical proximity between people comprising the one or more infected individuals and additional individuals, based on the various data. The processor(s) generates, based on the various data, a geofence. The processor(s) utilizes the geofence, the relationships, the physical proximity, and the various data, to predict that a portion of the additional individuals are more likely than not to be infected with the infection. The processor(s) generates a scoring model to identify a source of the infection. The processor(s) applies the scoring model to a group consisting of the one or more individuals, the portion of the additional individuals, and the physical locations, to identify the source of the infection (i.e., a person or physical locations).
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/80 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
A computer implemented method for generating a dispatch datagram is disclosed. The computer implemented method includes receiving, at a dispatcher, a request from a client. The method further includes generating an authorization header based on the received request. The authorization header includes one or more rules for handling the request. The method further includes wrapping the received request and the generated authorization header together to generate a dispatch datagram.
A processor is configured to create a log data specific lexicon wherein each word in the log data specific lexicon corresponds to a weighted sentiment score with a binary polarity. The processor is further configured to assign a sentiment value to a log message based on the weighted sentiment scores of words appearing in the log message. The processor may further determine an alert type for the log message based on the sentiment value, a class indicating an issue the log message, and a priority of the log message, where the alert type is preconfigured with a set of alert type values of varying risk levels and wherein a system alert contains a predefined set of key performance indicators corresponding to the alert type.
A method that includes collecting, by a computer, social network posts by social network friends of a user containing images. The method further includes identifying, by the computer, images within the social network posts containing the user through facial image recognition based on an image of the user in a user social network profile and determining, by the computer, information from the identified images containing the user within the social network posts for inclusion in the user social network profile. The method may further include updating, by the computer, the profile information of the user social network profile to include the information from the identified images containing the user within the social network posts.
A computer-implemented method, including: receiving, by a computing device, a post referring to a multimedia content; identifying, by the computing device, a time in the post; generating, by the computing device, a validity score based on analyzing contextual data of the time in the post; determining, by the computing device, a correlation between the time in the post to the multimedia content based on the validity score; and publishing, by the computing device, the post with an interactive link to a corresponding time of the multimedia content based on the determined correlation.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
A method, computer program product, and system include a processor(s) that map a physical environment by utilizing one or more image capture devices to scan aspects of the physical environment. The mapping identifies contamination levels and features associated with objects in the environment. The processor(s) utilizes unsupervised learning and supervised learning to identify activities engaged in by a user in the environment. The processor(s) determines that a trigger event has occurred. The processor(s) identify an activity engaged in by the user and determine if a user interface utilized by the program code to display results is visible to the user. If the processor(s) determines that the interface is not visible, the processor(s) selects an alert mechanism to alert the user to the trigger event and alerts the user to the trigger event with the selected alert mechanism.
A computer-implemented method includes: obtaining, by a computing device, data items from data sources; classifying, by the computing device, the data items into categories using a first machine learning (ML) model; generating, by the computing device, a risk score of a first data center based on the classified data items and using a second machine learning (ML) model; determining, by the computing device, the risk score of the first data center exceeds a threshold; and in response to the determining the risk score of the first data center exceeds the threshold, initiating, by the computing device, a migration of the first data center to a second data center.
Application transition and transformation is provided by observing and analyzing execution of a monolithic application to provide properties of the data communication of the monolithic application, identifying, by an artificial intelligence engine, atomic application element(s) having logical functional block(s) that function independent of other atomic application element, determining, by the artificial intelligence engine, candidate atomic application element(s) for migrating out of the monolithic application and refactoring, based on automated testing and validation, refactoring at least one candidate atomic application element into a respective at least one refactored element, where the refactoring selects a platform technology for the refactored element and implements functionality of the candidate atomic application element as the refactored element of the selected platform technology, and providing the at least one refactored element to system(s) for execution thereon.
A method of mitigating risks in a service level agreement (SLA), including: identifying relationships between an infrastructure and an application associated with the SLA; identifying changes to a technological environment of the SLA based on collected data associated with the SLA and the identified relationships, the collected data including the infrastructure and the application; establishing a baseline for a service level objective (SLO) of the SLA by analyzing the collected data of the infrastructure and the application; determining risk impact to the SLA based on an assessment of requirements to the SLO and the changes to the technological environment of the SLA; generating a solution option for the SLA by applying an explainable artificial intelligence (XAI) model based on processing the risk impact with the baseline in the XAI model; and updating the SLA based on the solution option.
H04L 41/5006 - Creating or negotiating SLA contracts, guarantees or penalties
H04L 41/5009 - Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
42.
AUTOMATIC GENERATION OF USER EXPERIENCE MOCKUPS USING ADAPTIVE GAZE TRACKING
A user experience theme description is obtained, along with a new user experience feature image set. The theme description and new user experience feature image set are input into a generative adversarial network (GAN). The GAN is trained to output multiple user experience designs based on the new feature image set. The multiple designs are displayed on an electronic display device that includes an eye gaze tracking system. User interface elements and their corresponding positions within a user interface are identified based on eye gaze of a user towards the electronic display device. The position and type of user interface elements are compared between a desired user interface design and a generated user interface design. Errors between the desired user interface design and the generated user interface design are input as feedback into the GAN to further enhance the results.
Embodiments of the present invention provide an approach for identifying outliers (e.g., detecting the outliers and generating outlier explainability) in a heterogeneous system. Heterogeneous input data is received from any number of data sources having any number of data types and converted into a single predefined format. Global outliers are detected in a first pass of the data. Contextual outliers are detected in a second pass. Global and contextual outliers are then collectively grouped based on outlier type. Output data is then generated including explainability for each detected outlier.
A computer-implemented method, including identifying distributed computing design requirements; determining system offerings based on a machine learning model trained with the distributed computing design requirements; providing the system offerings for selection by a customer; receiving selections of the system offerings; and generating a bill of materials based on the selections of the system offerings.
A method includes: determining, by a videoconference server, a level of tolerated risk for a videoconference between a presenter and an attendee; obtaining, by the videoconference server, sensor data from at least one sensor at a location where a user device of the attendee displays the videoconference; generating, by the videoconference server, a current risk score based on the sensor data; determining, by the videoconference server, the current risk score exceeds the level of tolerated risk; and presenting, by the videoconference server and in response to the determining the current risk score exceeds the level of tolerated risk, an alert to the presenter of the videoconference.
Workload distribution is automatically optimized. Cost penalty amounts imposed on executing a database operation transaction by current and alternative processing pathway options are determined as a function of execution response times that exceed a service level agreement time limit. Respective computer processing hardware costs are determined for executing the database operation transaction via each of the current and alternative processing pathway options. Respective licensing costs are determined for migrating execution of the database operation transaction to each of the alternative processing pathway options. Accordingly, the current or alternative middleware option that has a lowest total combined cost of licensing costs, computer processing hardware costs and service level agreement penalty costs is chosen as the path for execution of the database operation transaction.
H04L 41/50 - Network service management, e.g. ensuring proper service fulfilment according to agreements
H04L 41/0826 - Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability for reduction of network costs
A voice signal in a headphone is detected. The voice signal includes a person speaking an audible command. Based on detecting the voice signal a first biometric signature of a user is retrieved. The first biometric signature is compared to one or more biometric features of the person. Based on comparing the first biometric signature to the one or more biometric features an authentication of the user is determined. An authentication action is performed based on the detected voice signal. The authentication action is performed in response to verifying the authentication.
A method includes: identifying, by a computing device, an emotional awareness of an individual based on work product factors; analyzing, by the computing device, an impact on a product development process based on the emotional awareness of the individual; and providing, by the computing device, recommendations to improve the product development process based on the impact.
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
Disclosed embodiments provide techniques for log file manipulation detection. Log file terms are identified in a set of known good log files. A frequency metric is computed for the log file terms, and one or more clusters are formed that represent the terms and their corresponding frequency metric values within the set of known good log files. New log files are then obtained from an operational computer system. The frequency metric for those terms in the new log files are computed, and checked against the established clusters. A score is computed based on how similar the new log files are to the set of known good log files by comparing the frequency metric for terms in the new log file to the data in the previously obtained cluster(s). In response to a score exceeding a predetermined threshold, one or more mitigation actions are taken.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06F 11/16 - Error detection or correction of the data by redundancy in hardware
G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
In an approach to deploying containers on a 5G slice network, responsive to receiving a request to obtain and host a container within a 5G network, a slice network is created within the network for hosting of the container, where the slice network is created by a carrier for the network. The container is deployed to one or more worker nodes using the slice network within the 5G network.
A computer-implemented method, including monitoring, by a computing device, usage data of an application on a primary user device; detecting, by the computing device, a mirroring of the application on a secondary user device based on an identification of the application; determining, by the computing device, a synchronization setting of the application including a secondary user device based on a historical synchronization data for the application; and generating, by the computing device, a synchronization configuration for the application on the secondary user device based on the usage data; and synchronizing, by the computing device, the application on the secondary user device with the synchronization configuration.
A system includes: a platform; a payload extending from a platform; an adjustable tether connecting the payload to the platform; and a sail extending from the payload. The system further includes a computer program that is executable to relocate the platform to the area of interest based on a threshold based decision which may include providing a tether and sail configuration.
B63H 9/072 - Control arrangements, e.g. for launching or recovery
B63B 79/10 - Monitoring properties or operating parameters of vessels in operation using sensors, e.g. pressure sensors, strain gauges or accelerometers
53.
DYNAMIC INPUT SYSTEM FOR SMART GLASSES BASED ON USER AVAILABILITY STATES
A dynamic user interface (UI) input system and method for smart glasses based on availability states of a user is provided. In embodiments, a method includes determining, by a computing device of smart glasses, an initial availability state of a user's hands for a user interface (UI) navigation event based on incoming image data; selecting, by the computing device, a UI configuration and a control profile for the UI navigation event from stored UI configurations and control profiles of the user based on the initial availability state of the user's hands; and initiating, by the computing device, the UI navigation event based on the selected UI configuration and the control profile, wherein content is displayed in a virtual UI of the smart glasses according to the UI configuration, and UI navigation by the user is controlled based on the control profile.
2) sequestration in the atmosphere. For example, a computer-implemented method includes receiving, by a computing device, locations of an atmospheric pollutant; determining, by a computing device, a location of a target area of the atmospheric pollutant for sequestration; determining, by the computing device, positioning and flight path data for airborne sequestration devices to sequester the atmospheric pollutant at the location of the target area of the atmospheric pollutant; and deploying, by the computing device, the airborne sequestration devices with reagent according to the positioning and the flight path data to sequester the atmospheric pollutant at the location of the target area of the atmospheric pollutant.
A system and method for bandwidth management are provided. In embodiments, a method includes: training, by a computing device, a predictive machine learning (ML) model based on historic network usage data of software applications in a cloud environment and historic business context data; assigning, by the computing device, priority rankings to software application activities of the cloud environment using the predictive ML model based on predicted resource requirements for the software application activities of the cloud environment and predicted contextual scenarios that impact the predicted resource requirements using an input of real-time network usage data of the cloud environment and real-time business context data; and initiating, by the computing device, scheduling of the software application activities based on the priority rankings.
H04L 47/762 - Admission control; Resource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions triggered by the network
A method, computer program product, and system include a processor(s) obtaining an instruction to perform an inspection of a given type at a geographic site. The processor(s) deploys a robotic drone to the geographic site, wherein based on the deployment, the robotic drone performs a contextual analysis on the geographic site to identify a use case and to collect locational data. The processor(s) obtains the locational data. Based on the locational data, the given type of the inspection, and the use case, the processor(s) generates an inspection plan comprising tasks. The processor(s) identifies robotic drone(s) to complete the tasks and distributes the tasks. The robotic drone( )automatically self-optimize/s to complete the tasks. The processor(s) obtain the collected data from the self-optimized identified one or more robotic drones. The processor(s) analyze the collected data to identify issue(s) at geographic site.
Text is received from a user describing item(s) for migration to a computing environment with cloud feature(s), resulting in item description(s), the text including unstructured text that are processed separately. Text mining is performed on the unstructured text to extract item feature(s). For each listing a portion of the unstructured text is extracted, resulting in an extracted text portion for each listing from which an entity is identified. Each entity or item feature is mapped to cloud feature(s) available from solution(s) with cloud feature(s). Based on the cloud feature(s), recommendation(s) are made to the user regarding cloud feature(s) of the solution(s) for optional consideration by the user. Explanation(s) for the recommended cloud feature(s) from explainability model(s) may be provided to the user.
Methods, computer program products, and systems are presented. The method, computer program products, and systems can include, for instance: receiving personal inputs regarding a first individual, the first individual previously having given informed consent, automatically generating a work profile for the first individual based on the plurality of personal inputs; based on the work profile of the first individual and a preexisting work profile of a second individual, predicting a work affinity indicator for the first individual and the second individual, the predicting including using an affinity model trained via ensemble learning; and providing the work affinity indicator to a user for optional consideration in making a work-related or employment-related decision.
A computer-implemented method, including: analyzing, by a computing device, historical information associated with a business application; determining, by the computing device, a business process based on the analyzed historical information; generating, by the computing device, a virtual reality simulation of the business process; identifying, by the computing device, a business application requirement based on a user interaction with the virtual reality simulation; and generating, by the computing device, the ad-hoc application based on the identified business application requirement.
A method includes: integrating, by the computing device, a combination of user identified parameters in a template-based framework with historical operations data of a plurality of data centers in a multiple data center set-up to produce a feature set of parameters; clustering, by the computing device, the exclusive feature set of parameters into an optimal number of groups, each of which comprise similar physical attributes of chillers associated with any of the plurality of data centers; generating, by the computing device, analytical models for data center infrastructure component energy efficiency optimization of the multiple data center set-up based on the optimal number of groups; and providing recommendations from the analytical models as to which parameters are to be adjusted to have a more efficient energy utilization of the data center infrastructure components.
A computer-implemented method includes: receiving, by a computing device, data which is associated with a user; generating, by the computing device, a personalized recommendation for the data by at least one artificial intelligence (AI) application; training, by the computing device, a machine learning (ML) model using the data from the at least one AI application; and generating, by the computing device, a trained fitness model for predicting safety issues based on the trained ML model using the data from the at least one AI application.
A computer-implemented method, including: analyzing, by a computing device, historical information associated with a business application; determining, by the computing device, a business process based on the analyzed historical information; generating, by the computing device, a virtual reality simulation of the business process; identifying, by the computing device, a business application requirement based on a user interaction with the virtual reality simulation; and generating, by the computing device, the ad-hoc application based on the identified business application requirement.
A method and related apparatus adaptively control snapshot replication of a plurality of server snapshots in a multi-tenant public cloud using snapshot service. A plurality of snapshot requests are received from a plurality of clients in the multi-tenant public cloud and are each associated with a service level agreement (SLA). The SLA includes a corresponding completion deadline and a slippage penalty. A probabilistic model calculates, for active snapshot flows, a completion time for each of the snapshot requests. If any of the predicted completion times exceed their corresponding completion deadlines, a possible MISS event is produced for an associated first snapshot. The snapshot controller component, in response to producing the possible MISS event, minimizes a probability of breaching fulfilment times for the multi-tenant cloud. The slippage penalty is calculated for each of the plurality of snapshot requests. A second snapshot is identified from among the plurality of snapshot requests.
Systems and methods enable optimized infrastructure deployment planning and validation. In embodiments, a method includes: training, by a computing device, a machine learning (ML) predictive model with historic infrastructure deployment data of a plurality of resource providers in a network environment, including resource dependencies; generating, by the computing device, a deployment topology for requested resources of an information technology (IT) deployment request of a user; generating, by the computing device using the ML predictive model, a confidence score regarding a likelihood of successful implementation of the deployment request based on dependencies of the deployment topology; and dynamically implementing, by the computing device, deployment of the IT deployment request to provision the requested resources from multiple providers in the network environment based on the confidence score.
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
65.
Adaptive audio quality amelioration for video conferencing
A computer-implemented method, including: receiving, by the computing device, connecting device data from a connecting device associated with a participant of the video conference, wherein the connecting device data includes an audio quality data and a device connectivity data; determining, by the computing device, a conferencing readiness score based on the connecting device data; determining, by the computing device, the conferencing readiness score does not meet a threshold score; determining, by the computing device, an ameliorative action configured to raise the conferencing readiness score to meet the threshold score; and providing, by the computing device, the ameliorative action to the connecting device.
Aspects of the present invention disclose a method, computer program product, and system for management and usage of shared authentication credentials. The method includes one or more processors updating usage information associated with an authentication credential with a media access control address (MAC address) that corresponds to a computing device that corresponds to using the authentication credential. The method further includes one or more processors receiving a login request that includes the authentication credential from a computing device. The method further includes one or more processors fetching a MAC address of the computing device that sent the login request. The method further includes one or more processors validating the authentication credentials and the MAC address.
Aspects of the present disclosure relate generally to e-health insights of health monitoring of vibration impacts on the human body and, more particularly, to health monitoring of whole body vibration by the Internet of Things. For example, a computer-implemented method includes inputting into a health model, by the computing device, a plurality of health data and sensor data collected for an individual for a predetermined time period; determining, by the computing device, a probability of an onset of at least one symptom of whole body vibration syndrome for the individual from the plurality of health data and sensor data; and sending, by the computing device, a prediction of the onset of the at least one symptom of whole body vibration syndrome to a user device for display on the user device.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
A method includes: predicting a jeopardy associated with an edge server included in the server cluster that communicates with a central server; responsive to predicting the jeopardy, detaching the edge server from the server cluster; determining a patch based on the jeopardy; pushing the patch to the edge server; validating the patch by performing regression testing; and responsive to validating the patch, inducting the edge server into the server cluster.
Systems and methods synchronize content of a virtual environment with a state of a physical environment. In aspects, a method includes obtaining sensor data from a network of remote sensors measuring a physical state of a location at a time; generating context specific parameter data based on the sensor data; obtaining context data from a remote virtual reality (VR) system, wherein the context data reflects a current state of virtual content in a virtual environment displayed by the remote VR system; selecting virtual content to be displayed in the virtual environment by the remote VR system based on the context specific parameter data, the context data, and rules; and sending the virtual content to the remote VR system to be displayed to a user, wherein the virtual content reflects a state of the physical location at the time.
Aspects of the present disclosure relate generally to reminder systems and, more particularly, to a contextual item discovery and pattern inculcated reminder mechanism and methods of use. A computer-implemented method includes: determining, by a computing device, an event in which a user will participate; associating, by the computing device, one or more items with the event; determining a location of the one or more items associated with the event including that the user does not possess any combination of the one or more items; and providing an alert to the user that the any of the one or more items associated with the event is not in the possession of the user.
A computer implemented method and system are disclosed for dynamically enabling a first user device operated by a first user to provide wireless hotspot capability for a wireless hotspot session to a second user device operated by a second user. The wireless hotspot session is accepted based at least on urgency and bandwidth requirement of the second user device. When the first user device accepts a connection to the second user device to provision a wireless hotspot for the wireless hotspot session, the first user restricts use of the second user device in the wireless hotspot session, based on bandwidth and context of use of the second user device. The first user device may refuse a connection by rejecting the wireless hotspot session request. wireless hotspot session transactions information is provided as blockchain ledgers.
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: storing into a storage device a video data file; examining video file data defining the video data file, wherein the examining includes determining a relevancy of a section of the video data file; reducing a size of file data defining the section of the video data file in dependence on the determining the relevancy of the section of the video data file; storing a reduced-size version of the video data file into the storage device, the reduced-size version of the video data file having a reduced size relative to the video data file by performance of the reducing the size of file data defining the section of the video data file in dependence on the determining the relevancy of the section of the video data file.
G11B 27/031 - Electronic editing of digitised analogue information signals, e.g. audio or video signals
G06V 20/40 - Scenes; Scene-specific elements in video content
G10L 25/57 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for processing of video signals
G10L 15/18 - Speech classification or search using natural language modelling
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
A method includes: in response to identifying a primary user and corresponding Primary AI Assistant for a meeting, receiving by the Primary AI Assistant a confirmation to enroll at least one user personal digital assistant (PDA) of a respective one of at least one user; prompting the at least one user to provide descriptive information associated with the respective user PDA; connecting the at least one user PDA to the Primary AI Assistant internally by the Primary AI Assistant using the descriptive information for submitting requests; identifying by the Primary AI Assistant keywords and phrases received from the at least one user or primary user in the meeting; determining by the Primary AI Assistant a scheduling item based on the identified keywords and phrases; and automatically providing by the Primary AI Assistant the scheduling item to at least one user PDA corresponding to the scheduling item using the descriptive information.
Aspects of the present disclosure relate generally to data storage and data replication. For example, a computer-implemented method includes creating, by a computing device, a mapping that associates a plurality of block identifiers with a plurality of binary combinations of data for a block size; generating from the mapping that associates the plurality of block identifiers with the plurality of binary combinations of data for the block size, by the computing device, a list of block identifiers representing a list of data blocks in a storage; sending, by the computing device, the list of block identifiers to a backup storage to replicate the list of data blocks in the storage; and storing on a computer readable storage media the mapping that associates the plurality of block identifiers with the plurality of binary combinations of data for the block size and the list of block identifiers in the storage.
Systems and method of the invention provide an in situ diagnostic system for traumatic brain injury (TBI). In implementations, a method includes: receiving, by a computing device, real-time user parameter data from one or more sensors of the user during a monitoring event; writing, by the computing device, the real-time user parameter data as time series data in a data store; determining, by the computing device, that at least one parameter of the real-time user parameter data meets or exceeds a predetermined parameter threshold value; calculating, by the computing device, a diagnostic score for the user based on the time series data, baseline parameter data of the user, and a determined protective equipment profile of the user; and automatically diagnosing a potential traumatic brain injury (TBI) of the user in situ based on the diagnostic score meeting or exceeding a diagnostic threshold.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A computer-implemented method including: displaying, by a mobile device, a push notification from an application of the mobile device; determining, by the mobile device, an associated notification parameter for the push notification; receiving, by the mobile device, a user input by a user of the mobile device; determining, by the mobile device, the user input indicates interest in the push notification; and maintaining, by the mobile device, presence of the push notification on a display of the mobile device based on determining the user input indicates interest in the push notification and the associated notification parameter.
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
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 automated mainframe database maintenance are provided. In implementations, a method includes obtaining, by a computing device, real-time performance metrics of a mainframe database; automatically generating, by the computing device, a predicted maintenance task as an output of a trained database maintenance task classification machine learning (ML) model based on an input of the real-time performance metrics; automatically generating, by the computing device, a time to execute the predicted maintenance task as an output of a trained database maintenance triggering ML model based on an input of the predicted maintenance task and the real-time performance metrics; automatically generating, by the computing device, maintenance task instructions for the mainframe database based on the predicted maintenance task, the time to execute the predicted maintenance task, and a maintenance profile of the mainframe database; and automatically initiating, by the computing device, the execution of the maintenance task instructions.
A method includes: hosting, by a computing device, a videoconference among plural users; receiving, by the computing device, input from a sharing user of the plural users, wherein the input initiates a screenshare in the videoconference; creating, by the computing device, document twins of a document shown in the screenshare; and providing, by the computing device and to other users of the plural users, access to respective ones of the document twins in an interface of the videoconference; wherein each of the other users may scroll within a respective one of the document twins independently of other ones of the other users and independently of the screenshare.
A system and method for automatic remediation of non-compliance events are provided. In embodiments, a computer-implemented method includes: accessing a compliance profile and a remediation profile, wherein the compliance profile includes compliance data regarding rules for an enterprise and the remediation profile includes remediation data regarding remediation actions to address non-compliance with one or more of the rules; generating mapped data by mapping compliance data in the compliance profile to remediation data in the remediation profile; receiving non-compliance event data from a workload node in a network; extracting information from the non-compliance event data including the workload node associated with the event and a cause of event; determining a remediation action for the event based on the information and the mapped data; and invoking automatic performance of the remediation action at the workload node based on the determined remediation action.
Blockchain-based appointment management is provided by maintaining, in a blockchain network with which a provider and a collection of users engage, a schedule of appointments between the provider and individual users, and managing appointment scheduling (including changing existing appointments) between the provider and user(s) of the collection of users via recordation of blockchain transactions on a blockchain of the blockchain network. The managing includes monitoring travel of a user to a location at which a scheduled appointment between the provider and the user is to occur, the monitoring providing an estimated arrival time of the user to the appointment location, based on the estimated arrival time, an initial anticipated arrival time for the scheduled appointment, and the schedule of appointments, determining change(s) to make to the schedule of appointments, including a modification to the scheduled appointment, and recording the change(s) to the schedule of appointments as transaction(s) on the blockchain.
A computer-implemented method includes: monitoring an operating system of a server that serves content to client computing devices, wherein the server maintains log files; determining, based on the monitoring, whether commands received at the operating system are indicative of tampering with one or more of the log files; responsive to determining the commands are indicative of tampering with one or more of the log files, performing a predefined security action; and responsive to determining the commands of the user are not indicative of tampering with one or more of the log files, sending the commands to a kernel of the operating system for execution.
A server receives encrypted data from a protected-resource-requesting device that includes an encrypted combination of the device and user identification. The first server requests a most recent copy of data of a distributed ledger from a randomly selected logged-in workstation. The first server searches for a match of the encrypted data from the first device in the distributed ledger data received from the randomly selected workstation. In response to determining a match, the first server updates a table of a second server with a one-time-password (OTP) and a copy of the encrypted data received from the device. The first server sends the OTP and an instruction to the device to send the OTP and the encrypted data to the second server, which determines whether a match exists. In response to a confirmed match, the first server grants access to the device.
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
Personalizing sensory feedback based on user sensitivity analysis includes maintaining user-specific parameters for provision of sensory feedback to a user in extended reality. The user-specific parameters apply to specific contextual situations and dictate levels of sensory feedback to provide via stimulus device(s) in the specific contextual situations. Based on an ascertained contextual situation of the user interacting in a target extended reality environment, a set of sensory feedback level parameters is selected for provision of sensory feedback to the user in the target extended reality environment, and stimulus device(s) in the target extended reality environment is/are automatically controlled in the provision of the sensory feedback to the user based on one or more of the selected parameters. The automatically controlling includes electronically communicating with the stimulus device(s) to control at least one stimuli provided to the user by the stimulus device(s).
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
A computer-implemented method includes: monitoring, by a computing device, network communications to a server; determining, by the computing device, a threat to the server based on the monitoring; determining, by the computing device, a risk level of the threat; selecting, by the computing device, a response to the threat based on the determined risk level, wherein the response is selected from a predefined set of responses; initiating, by the computing device, the selected response.
A computer-implemented method includes: generating, by a computing device, a multidimensional hyperspace encompassing a plurality of features based on input data; generating, by the computing device, a plurality of sequential arrays of a fixed length based on the input data; generating, by the computing device, a final sequential array of a predetermined shape based on the plurality of sequential arrays of the fixed length; generating, by the computing device, a training data set for a deep learning model based on the final sequential array of the predetermined shape and the multidimensional hyperspace; and training, by the computing device, a deep learning model using the generated training data set.
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
Aspects of the present disclosure relate generally to three-dimensional (3D) printing and, more particularly, to robotic support for 3D prints. For example, a computer-implemented method includes identifying, by the computing device, at least one structure in the 3D object source file requiring support for printing the at least one structure by a 3D printer; adding, by the computing device, to a 3D print file an instruction to request deployment of a support structure at a particular location to support the at least one structure by the 3D printer; and providing to the 3D printer, by the computing device, the 3D print file with the instruction to request deployment of the support structure at a particular location to support the at least one structure by the 3D printer.
B33Y 30/00 - ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING - Details thereof or accessories therefor
87.
MASKED OVERLAY FOR CUSTOM REPOSITIONING OF INTERACTIVE OBJECTS ON A TOUCHSCREEN OF A COMPUTING DEVICE
A method includes: receiving, by a computing device, user input defining a first location of a touchscreen of the computing device, the touchscreen displaying a user interface (UI) of an application; receiving, by the computing device, user input defining a second location of the touchscreen of the computing device; identifying, by the computing device, an interactive object of the UI at the first location; creating, by the computing device, an overlay including a copy of the interactive object and a masking object, the copy of the interactive object being at the second location and the masking object being at the first location; and causing, by the computing device, the touchscreen to display an output including the overlay superimposed over the UI.
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
A computer-implemented method includes: setting up, by a computing device, an identification profile by capturing a first image using a camera of the computing device; requesting, by the computing device, an authentication action by an authentication device; comparing, by the computing device, recognized elements of the first image and a second image, the recognized elements of the first image and the second image including at least one of ephemeral features and transient features; determining, by the computing device, whether a confidence level is above a predetermined threshold based on a comparison of the recognized elements of the first image and the second image; and completing, by the computing device, authentication of the computing device with the authentication device in response to a determination that the confidence level is above the predetermined threshold.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
G06V 10/75 - Image or video pattern matching; Proximity measures in feature spaces using context analysis; Selection of dictionaries
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
89.
LEDGER-BASED IMAGE DISTRIBUTION PERMISSION AND OBFUSCATION
Using an image analysis model within an image intended for distribution online, an image portion depicting personally identifiable information is identified, the personally identifiable information comprising image data usable to identify a specific individual. Using an online profile, a person depicted in the image portion is identified. A transaction is posted in a publicly-accessible distributed encrypted ledger, the transaction comprising an encrypted request to allow the image to be distributed online. According to a response to the request, the image portion is obfuscated, the obfuscating comprising altering data of the image portion, the altering making the image portion unusable to identify the person.
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: iteratively obtaining utilization parameter values from first to Nth edge computing environments, training one or more predictive model by machine learning using parameter values of the utilization parameter values obtained by the iteratively obtaining, wherein the training includes training a first computing environment predictive model with use of parameter values of the utilization parameters obtained from the first computing environment by the iteratively obtaining.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
91.
SELECTING THREE-DIMENSIONAL (3D) PRINTING TECHNIQUE AND LOCATION OF 3D-PRINTED SENSORS
Aspects of the present disclosure relate generally to selecting a 3D printing technique and location of a sensor as part of a 3D object. For example, a computer-implemented method includes: receiving, by a computing device, a 3D print file specifying a 3D object for printing on a 3D printer; identifying, by the computing device, a technique for printing a sensor as part of the 3D object from a plurality of techniques for printing the sensor as part of the 3D object; determining, by the computing device, a location for printing the sensor as part of the 3D object; adding, by the computing device, the technique and the location for printing the sensor as part of the 3D object to the 3D print file; and sending to the 3D printer the 3D print file with the technique and the location for printing the sensor as part of the 3D object.
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining clothing article data stream data from one or more internet of things (IoT) device disposed in a computing environment, wherein the computing environment is collocated with a residence of a user, the clothing article data stream data representing one or more clothing article of the user; examining data of the clothing article data stream data to determine at least one clothing article parameter value of the one or more clothing article; in dependence at least one clothing article parameter value of the one or more clothing article, and providing user profile data that specifies predicted behavior of the user.
D06F 34/28 - Arrangements for program selection, e.g. control panels therefor; Arrangements for indicating program parameters, e.g. the selected program or its progress
A method includes executing, by a computing device, a reorganization command within an environment; monitoring, by the computing device, unprocessed replication transactions within the environment; determining, by the computing device, whether the unprocessed replication transactions exceed a threshold; and pausing, by the computing device, the executing the reorganization command in response to determining the unprocessed replication transactions exceed the threshold.
G06F 16/17 - File systems; File servers - Details of further file system functions
G06F 16/16 - File or folder operations, e.g. details of user interfaces specifically adapted to file systems
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
G06F 16/178 - Techniques for file synchronisation in file systems
A cognitive assignment engine (CAE) system attempts to infer semantic meaning from textual content of an incoming message in order to use the inferred meaning to assign the message to an appropriate responder. If the message contains insufficient textual content, the system identifies ontological structures comprised by the message's graphical content and classifies each structure as a function of the structure's location within the graphical content or of an intrinsic characteristic of the structure. The system then generates a message identifier by performing a computation on these classifications and uses the identifier to retrieve a previously stored graphical template that comprises ontological structures similar to those of the incoming message. The system associates the incoming message with a semantic meaning previously associated with the template, enabling the system to classify the message and to assign the message to the correct responder.
A computer-implemented method includes: receiving, by a computing device, text extracted from a webpage in a browser and a Uniform Resource Locator (URL) of a linked webpage associated with the text; generating, by the computing device, questions based on the text; retrieving, by the computing device, content of the linked webpage using the URL; generating, by the computing device, answers to the questions using the retrieved content; and returning, by the computing device, the questions and the answers to the browser such that the browser displays the questions and the answers in the webpage.
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: predicting, with use of one or more predictive model, subsequent future state data of a computing node having an associated working memory, wherein the subsequent future state data specifies that a certain transaction currently uninvoked will be invoked by the computing node; prior to the invoking of the certain transaction, proactively establishing one or more control parameter value for controlling the working memory in dependence on the future state data; invoking the certain transaction in response to receipt, by the computing node, of transaction invoking request data for invoking the certain transaction; and executing the certain transaction in dependence on at least one control parameter value of the one or more control parameter value for controlling the working memory.
A system and method for managing secure shared office space is provided. In embodiments, a method includes: receiving, by a computing device, a workspace request from a user, the workspace request including scheduling requirements for an activity; determining, by the computing device, security requirements associated with the workspace request utilizing a trained convoluted neural network (CNN) based on participant data including information regarding security requirements of the user and shared workspace information for a pool of shared workspaces; determining, by the computing device, that one or more of the shared workspaces in the pool of shared workspaces meets the security requirements associated with the workspace request; determining, by the computing device, that the one or more of the shared workspaces meets the scheduling requirements of the request; and automatically generating and sending, by the computing device, a notification to the user including the one or more of the shared workspaces.
A method includes detecting, by a computing device, a plurality of transactions on one or more data storage system. The method further includes sequentially recording, by the computing device, the plurality of transactions occurring on the one or more data storage system in sequentially linked and immutable blocks.
An intelligent user centric design platform is provided. In implementations, a method includes: receiving, by a computing device, software design input from a user, the software design input including software domain information; sending, by the computing device, questions to the user selected from a database of predetermined questions based on the domain information; receiving, by the computing device, answers to the questions from the user, the answers including text information regarding design requirements of the user; determining, by the computing device, a proposed user-centric design (UCD) diagram by matching the answers to a stored UCD diagram in a repository using a supervised machine learning model; and presenting, by the computing device, the proposed UCD diagram in a user interface, wherein the user interface enables acceptance of the proposed UCD diagram or rejection of the proposed UCD diagram.
A method includes receiving training inputs related to technology use cases and associated services, training, by a cognitive integration engine a cognitive model from the received training inputs, receiving a demand for composite services from a customer including functional and/or non-functional requirements, determining, by the cognitive integration engine, a selection of composite services for the customer based on the cognitive model, and recommending the selection of composite services to the customer.
patents
