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: An example methodology includes analyzing a data set onboarded to a data marketplace service for quality, deriving a static price depreciation coefficient based on the quality of the data set, and applying the static price depreciation coefficient to a price of the data set to determine a first new price of the data set, wherein the price of the data set is specified by a data provider that provided the data set to the data marketplace service. The method may also include analyzing error reports and feedbacks about the data set to determine a density of errors in the data in the data set, deriving a dynamic price depreciation coefficient based on the density of errors in the data in the data set, and applying the dynamic price depreciation coefficient to the price of the data set to determine a second new price of the data set.

Abstract: A method includes capturing shipment milestone data of cost, insurance, and freight (CIF) shipments, recording the shipment milestone data within a distributed ledger, generating a training dataset using past shipment milestone data recorded within the distributed ledger, and generating a machine learning (ML) model configured with explainable artificial intelligence (XAI) based on the training dataset. The method also includes receiving information regarding a CIF shipment from a computing device, determining one or more relevant features from the information regarding the CIF shipment, the relevant features influencing prediction of a lead time for ownership transfer, and generating, using the ML model configured with XAI, a prediction of a lead time for ownership transfer with an explanation of the prediction for the CIF shipment based on the determined relevant features. The method may also include sending the prediction of the lead time for ownership transfer with the explanation to the computing device.

Abstract: An example methodology includes, by a computing device, receiving a recording of a virtual interview, the recording including an audio of the virtual interview and a video of the virtual interview, retrieving a profile image of a candidate, and determining an appearance of an interviewee in the video of the virtual interview. The method also includes, responsive to a determination that the appearance of the interviewee and profile image do not match, including, by the computing device, an indication that the candidate did not participate in the virtual interview in an assessment of authenticity of the candidate. The method further includes sending, by the computing device, a report of the assessment of the authenticity of the candidate to another computing device.

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 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: An example methodology implementing the disclosed techniques includes, by a computing device, receiving a first video stream captured by a camera, analyzing images within the first video stream to recognize a first regional sign language, and determining a caption that portrays a meaning conveyed by the recognized first regional sign language. The method also includes, by the computing device, translating the first caption to a neutral language to generate a neutral language caption, translating the neutral language caption to generate a regional language caption, the regional language associated with an intended recipient of the first video stream, and generating a second video stream composed of a second regional sign language images representing the regional language caption. The method may also include sending the second video stream to the intended recipient of the first video stream. The first video stream may be received during a sign language conversation session.

Abstract: An example methodology includes receiving a request to analyze a timeslot of a meeting and determining meeting details, wherein the meeting details include an organizer of the meeting, a date of the meeting, a time of the meeting, a subject of the meeting, and one or more attendees of the meeting. The method also includes predicting, using a machine learning model, an attendee meeting acceptance level for each attendee of the one or more attendees. The method further includes determining an acceptance level of the meeting based on likelihood of the one or more attendees joining the meeting, wherein the likelihood of the one or more attendees joining the meeting is based at least on the predicted attendee meeting acceptance levels for the one or more attendees, and sending information about the acceptance level of the meeting in a response to the request to analyze the timeslot of the meeting.

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: An example method includes: identifying one or more existing parts that match a part to be procured; determining a part quality and readiness score for each of the existing parts; identifying one or more suppliers of the existing parts; determining a supplier trust and opportunity score for each of the suppliers; determining one or more potential procurement decisions for procuring the quantity of the part, wherein the one or more potential procurement decisions include procurement decisions to procure the specified quantity of the one or more existing parts from the one or more suppliers based on the part quality and readiness score for each of the one or more existing parts and the supplier trust and opportunity score for each of the one or more suppliers; and recommending one of the one or more potential procurement decisions as an optimal procurement decision for procuring the quantity of the part.

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: 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: 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 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: 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.