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: Automated item management techniques are disclosed. For example, for an item type obtainable from one or more sources and storable as inventory at one of a first site or a second site and based on a first demand forecast, a method computes a discrepancy value for the item type for each of the first site and the second site based on a second demand forecast, wherein the second demand forecast is computed more recently in time than the first demand forecast. The method generates, based on the discrepancy value computed for each of the first site and the second site, a recommendation operation to mitigate the discrepancy value at one or more of the first site and the second site. The method causes the recommendation operation to be executed in order to mitigate the discrepancy value at one or more of the first site and the second site.

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: Techniques are disclosed for data management in an information processing system. For example, a method comprises analyzing one or more data sources, wherein each of the one or more data sources comprise a set of metadata and usage information associated with the set of metadata. The method then determines at least one of data definitions and data classifications for the one or more sets of metadata across the one or more data sources, and stores the at least one of data definitions and data classifications for the one or more sets of metadata in a repository.

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: Techniques are disclosed for domain-driven intent-based feedback data analysis. For example, a method comprises obtaining a feedback data set, and classifying the feedback data set into at least one domain of a plurality of domains. The feedback data set is mapped to a domain data set corresponding to the at least one domain, and a root cause is computed for the feedback data set to enable one or more actions to be taken in response to the feedback data set based on the root cause. By way of further example, computing the root cause for the feedback data set may further comprise utilizing at least one computed intent attribute, at least one computed sentiment attribute, and the domain data set to compute a decision on the root cause for the feedback data set.

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: Automated item management techniques comprising balancing of items across a plurality of sites are disclosed. For example, for a given item type stored as inventory at a plurality of sites, a method predicts a stock factor for each of the plurality of sites based on historical stock factor data. The method also predicts an aging items count for each of the plurality of sites based on historical aging item data. The method computes a plan, based on the predicted stock factor and the predicted aging items count for each of the plurality of sites, to move an amount of the given item type between two or more of the plurality of sites to satisfy a demand forecast at each site.

Abstract: Techniques are disclosed for hyper-segmented personalization using machine learning-based models in an information processing system. For example, a method obtains one or more product experience recommendation data sets respectively from one or more product entities, and one or more purchase experience recommendation data sets respectively from one or more commerce entities. The method applies a federated ensemble-based machine learning algorithm to at least one of the one or more purchase experience recommendation data sets and at least one of the one or more product experience recommendation data sets to generate a personalized model, and causes adaptation of a purchasing interface of at least one of the one or more commerce entities with respect to a given user based on the personalized model.

Abstract: Intelligent prediction techniques for equipment manufacturing management are disclosed. For example, a method comprises obtaining: (i) a structured description of at least one of components and processes associated with manufacturing of equipment in accordance with a given design; (ii) first manufacturing-related data from one or more potential manufacturing entities for the equipment; and (iii) second manufacturing-related data representing at least one of current attributes and historical attributes associated with manufacturing equipment at least similar to the equipment of the given design. The method then applies one or more prediction models based on at least portions of the obtained structured description, the first manufacturing-related data, and the second manufacturing-related data to compute a predicted attribute associated with manufacturing the equipment.

Abstract: Automated supply chain management techniques are disclosed. For example, a method comprises the following steps. The method obtains a demand for a given number of an item associated with a manufacturing order. The method obtains a set of classifications for a set of suppliers based on historical data corresponding to each of the suppliers, and obtains a criticality indicator for the demand. The method generates an allocation for the demand across at least a subset of the set of suppliers based on the set of classifications and the criticality indicator, wherein each of the suppliers in the subset is allocated a given proportion of the demand in accordance with the generated allocation.

Abstract: Techniques are disclosed for data management in an information processing system. For example, a method comprises analyzing one or more data sources, wherein each of the one or more data sources comprise a set of metadata and usage information associated with the set of metadata. The method then determines at least one of data definitions and data classifications for the one or more sets of metadata across the one or more data sources, and stores the at least one of data definitions and data classifications for the one or more sets of metadata in a repository.

Abstract: Automated supply chain management techniques are disclosed. For example, a method comprises defining a plurality of supply segments to represent a forecasted demand for material needed to manufacture equipment via a supply chain, and allocating supplied material across one or more of the plurality of supply segments wherein a first portion of the supplied material is allocated in a non-fixed manner and a second portion of the supplied material is allocated in a fixed manner.

Abstract: Automated supply chain management techniques are disclosed. For example, a method comprises obtaining a plurality of datasets respectively representing a plurality of probability measures for a plurality of variability factors associated with a supply chain for at least one part needed to manufacture equipment, and generating a prediction for a future shortage of the at least one part based on the plurality of datasets. The prediction may then be used to proactively mitigate the future shortage.