Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving audio data corresponding to a spoken utterance by a first user and determining an accent of the audio data. The method also includes, by the computing device, neutralizing the accent of the audio data to a preconfigured accent and transmitting a modified audio data in the preconfigured accent to another computing device. The modified audio data includes the spoken utterance by the first user.

Abstract: Methods, apparatus, and processor-readable storage media for a neural network-based message communication framework with summarization and on-demand audio output generation are provided herein.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving activity data corresponding to an activity of an employee and deriving context and information from the activity data. The method also includes, by the computing device, associating the context and information to a job position and storing the context and information within a repository. The method further includes, by the computing device, responsive to a request to generate a job profile for the job position, retrieving, from the repository, the context and information associated with the job position and generating the job profile based on the retrieved context and information.

Abstract: A method comprises receiving a set of raw worker data records associated with one or more worker activities of a worker, each raw worker data record comprising one or more of an image, a video, information from a sensor, information from interaction with a tool, and information from interaction with a device. A first analysis, configured to analyze the set of raw worker data records for one or more indications regarding whether any worker activities include one or more undesired behaviors, is performed on the set of raw worker data records. Based on the first analysis, a second analysis, configured to categorize and document worker behavior as a function of time, is performed. Based on the second analysis, a third analysis, configured to analyze worker behavior for undesired conditions, is performed. Based on at least one of the second and third analysis, a summary of worker activity is generated.

Abstract: In a distributed ledger (DLT) network, a transaction, related to movement of a monitored entity from a first party's location to a second party's location, is monitored. The transaction is associated with a respective real-time status and with at least a first predetermined milestone. Sensor outputs from an IoT enabled sensor that is operably coupled to the monitored entity, are received while the monitored entity is located remotely from the first and second parties during its movement from the first location to the second location. Updates to a real-time status of the monitored entity, relating to progress towards a milestone, are determined based on the received sensor outputs. The transaction is updated, in real time, on the DLT network when the milestone is reached. Information is communicated relating to the update of the transaction and the first predetermined milestone, in real-time, to the first party and the second party.

Abstract: Techniques are disclosed for a multi-layer micro model analytics framework for analyzing or otherwise processing data. For example, a method comprises building two or more micro models respectively for two or more stages of a given process, wherein each micro model of the two or more micro models comprises a user interaction layer and a predictive learning layer that coordinate to train the micro model based on input to the user interaction layer and data accessible by the predictive learning layer for the corresponding stage of the two or more stages of the given process. The method then assembles the two or more micro models to perform analysis for the given process. In one non-limiting example, the given process is a new product introduction process and each micro model is built and trained to perform analytics for a specific lifecycle stage of the process.

Abstract: A computer-implemented method is provided. Raw data records associated with one or more user interactions of a user with one or more entities, each respective raw data record comprising one or more of a textual interaction, an audio interaction, and a video interaction, are received. A first analysis is performed on the set of raw data records to analyze them for at least one of sentiments, emotions, and intent. A second analysis is performed on the set of raw data records to segment the set of raw user data. A third analysis of the raw set of data records performs at least one of interpreting, summarizing and classifying, of the information associated with the first and second analyses to determine, at least one recommended action to assist the user. An output signal, comprising the at least one recommended action is generated to the user based on the third analysis.

Abstract: In a distributed ledger (DLT) network, a transaction, related to movement of a monitored entity from a first party's location to a second party's location, is monitored. The transaction is associated with a respective real-time status and with at least a first predetermined milestone. Sensor outputs from an IoT enabled sensor that is operably coupled to the monitored entity, are received while the monitored entity is located remotely from the first and second parties during its movement from the first location to the second location. Updates to a real-time status of the monitored entity, relating to progress towards a milestone, are determined based on the received sensor outputs. The transaction is updated, in real time, on the DLT network when the milestone is reached. Information is communicated relating to the update of the transaction and the first predetermined milestone, in real-time, to the first party and the second party.

Abstract: A method comprising: receiving, at a computing device, a first input specifying a first time window for a meeting and a first set of recipients who are invited to attend the meeting, the first set being a non-empty set; receiving, at the computing device, a second input specifying a plurality of segments within the meeting, for each of the plurality of segments, the second input specifying a respective second time window for the segment, and a respective second set of recipients who are invited to attend the segment, each second set of recipients being a subset of the first set, and each second time window being a time window that falls entirely within the first time window; and generating one or more invites based on the first and second input and transmitting the one or more invites to the recipients in the first set.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving a set of requirements for a database and generating a feature vector representative of the set of requirements for the database. The method also includes, by the computing device, predicting, using a machine learning (ML) model, a database for the set of requirements based on the feature vector and sending information indicative of the predicted database to a client. The predicted database may be a database that is optimal for the received set of requirements. The ML model may be a multiclass classification model.

Abstract: A method comprising: storing, in a ledger of a blockchain system, a transaction record containing information associated with a first purchase that is made in a supply chain, the supply chain including a plurality of suppliers, the first purchase being made by a first one of the plurality of suppliers from a second one of the plurality of suppliers, the transaction record containing a plurality of data items associated with the first purchase; and storing, in the ledger of the blockchain system, a logic for enforcing one or more data access policies, the logic being configured to control access to at least one of the plurality of data items in the transaction record by any given one of the plurality of suppliers based on a respective tier in the supply chain to which the given supplier belongs.

Abstract: Methods, apparatus, and processor-readable storage media for automated resource prioritization using artificial intelligence techniques are provided herein.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a product subscription service, receiving information regarding a hardware asset being returned at an end of a subscription and predicting, using a first machine learning (ML) model, whether the hardware asset has reached EOL. The method also includes, responsive to predicting that the hardware asset has reached EOL, creating, by the product subscription service, a work order to dispatch an eco-partner. The method may further include, by the product subscription service, responsive to predicting that the hardware asset has not reached EOL, predicting, using a second ML model, one or more new subscription orders matching the hardware asset and recommending the one or more matching new subscription orders as possible fits for the hardware asset.

Abstract: A method including: generating a conversation flow signature based on a set of communication transcripts, each of the communication transcripts being associated with a support request for a product, each of the communication transcripts being a text transcript of a communication between a respective customer and a respective customer support agent; classifying the conversation flow signature into one of a plurality of categories, the conversation flow signature being classified by using a machine learning classifier that is trained based on customer support records, each of the plurality of categories corresponding to a respective set of steps for configuring or repairing the product; and outputting an indication of the respective set of steps that is associated with the category in which the conversation flow signature is classified.

Abstract: A method comprising: generating first sentiment identifier of a user, the first sentiment identifier of the user being a text sentiment identifier; generating a second sentiment identifier of the user, the second sentiment identifier including one of a voice sentiment identifier of the user and an image sentiment identifier of the user, the image sentiment identifier being an indication of an emotional state of the user that is manifested in a facial expression of the user during the telephone call; generating a composite signature of the user based on the first sentiment identifier and the second sentiment identifier; and classifying the composite signature of the user into one of a plurality of categories.

Abstract: A method for use in a computing device, comprising: receiving an invite for a communications session; obtaining context information associated with the invite; generating a signature for the invite based on the context information; generating an attendance score for the invite by evaluating a neural network based on the signature for the invite, the attendance score being an estimate of a degree of importance of attending the communications session; generating a response to the invite based on the attendance score, the response indicating whether a user of the computing device accepts or rejects the invite; and transmitting the response to a sender of the invite.

Abstract: A method for use in a computing device, comprising: receiving an invite for a communications session; obtaining context information associated with the invite; generating a signature for the invite based on the context information; generating an attendance score for the invite by evaluating a neural network based on the signature for the invite, the attendance score being an estimate of a degree of importance of attending the communications session; generating a response to the invite based on the attendance score, the response indicating whether a user of the computing device accepts or rejects the invite; and transmitting the response to a sender of the invite.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, responsive to a determination, by a computing device, of a scheduling of an online conference between a plurality of conference participants, the online conference including food service, determining a food service application that is appropriate for at least one conference participant in the online conference. The method also includes sending to the at least one conference participant a notification regarding the scheduling of the online conference that includes food service, wherein the notification includes a link to the food service application. The method may also include, responsive to a receipt of a bill for a food order placed with the food service application by the at least one conference participant, collecting an amount from a conference organizer to pay for the food order and making an appropriate payment to the food service application to pay for the food order.

Abstract: Systems and methods for automated invocation of accessibility validations in accessibility scripts are disclosed. According to one embodiment, in an information processing apparatus comprising at least one computer processor, an automated accessibility test program performing the following: (1) invoking an automated test program; (2) invoking the automated accessibility test program in the automated test program; (3) loading a webpage to be validated; (4) identifying at least one interactive webpage element on the webpage; (5) causing the automated accessibility program to validate the interactive webpage element with the automated accessibility program; (6) storing a result of the validation; and (7) performing an action validation on the interactive webpage element.

Abstract: Systems and methods for automated invocation of accessibility validations in accessibility scripts are disclosed. According to one embodiment, in an information processing apparatus comprising at least one computer processor, an automated accessibility test program performing the following: (1) invoking an automated test program; (2) invoking the automated accessibility test program in the automated test program; (3) loading a webpage to be validated; (4) identifying at least one interactive webpage element on the webpage; (5) causing the automated accessibility program to validate the interactive webpage element with the automated accessibility program; (6) storing a result of the validation; and (7) performing an action validation on the interactive webpage element.