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: Methods, apparatus, and processor-readable storage media for automatically prioritizing supply chain-related demand 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: 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: Methods, apparatus, and processor-readable storage media for temporal supply-related forecasting using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining one or more forecasts pertaining to supply of at least one item by processing supply-related data using one or more artificial intelligence techniques; generating, based at least in part on the one or more forecasts, one or more temporal recommendations associated with one or more orders of at least one a portion of the at least one item; and performing one or more automated actions based at least in part on the one or more temporal recommendations.

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: 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: 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: 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 comprises analyzing a plurality of natural language inputs associated with at least one user, and determining a plurality of contexts for the plurality of natural language inputs based, at least in part, on the analysis. In the method, a plurality of relationships linked to the at least one user are identified based, at least in part, on the analysis, and the at least one user is classified in one or more categories based, at least in part, on the plurality of contexts and the plurality of relationships. At least one of the analyzing, determining, identifying and classifying is performed using one or more machine learning models.

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: Methods, apparatus, and processor-readable storage media for automatically prioritizing supply chain-related demand using artificial intelligence techniques are provided herein.