International Business Machines Corporation

Dell Products Inc.

Optum Technology, Inc.

Unitedhealth Group Incorporated

Atul Kumar

Love Hasija

Ruchi Mahindru

Daniela Rosu

Amit Krishna

Saurabh Jha

Rahul Dutta

Seema Nagar

Damian Kelly

Atri Mandal

Ninad D Sathaye

Sahil Bansal

Anamika Chatterjee

Ajay Kumar Kuruba

Kimberly A Vorse

Shubhendu Shekhar

Adya Jha

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

Machine learning techniques for parasomnia episode management

Document retrieval with domain adaptation and out of distribution identification is disclosed. Custom layers are added to a model without impacting base weights. The custom layers are trained using a domain specific dataset and topic identifiers. When executing a query, the custom layers use weights associated with a topic identifier. If a potential result is sufficiently similar, the result is returned in response to the query. Otherwise, the result is out of distribution and no result is returned.

System and method for document retrieval with domain adaption and out of distribution identification

A computing system initializes a score for each chatbot profile of a plurality of chatbot profiles. The chatbot profiles correspond to different personas. For each chatbot profile, the computing system collects biometric response data for a user while the user has an interaction session with the chatbot profile. The computing system updates the score for the chatbot profile based on the biometric response data for the user collected while the user has the interaction session with the chatbot profile. The computing system ranks the chatbot profiles based on the scores and selects a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with the user based on the ranking of the chatbot profiles.

Ranking chatbot profiles

Methods, apparatus, and processor-readable storage media for localized anomaly detection in digital documents are provided herein. An example computer-implemented method includes identifying at least one image in a digital document; processing the identified at least one image using at least an image compression algorithm; applying a first machine learning model to the at least one processed image, wherein the first machine learning model is trained to detect whether the at least one image comprises one or more modifications; in response to detecting that the at least one image comprises at least one modification, applying a second machine learning model to identify a location in the at least one image corresponding to the at least one modification; and generating an indication that identifies the location of the at least one modification in the at least one image.

Localized anomaly detection in digital documents using machine learning techniques

Systems, methods and/or computer program products maximizing answer coherence of virtual agents responding to multi-part user queries, using relationships among extracted chunks corresponding to support document(s). Relationships are extracted by mining entities from text and images of support document(s) and mapping entities to an otology. Induced subgraphs for chunks, based on extracted entities are created and compared. Depending on amounts of overlap and/or relationships between subgraphs, relationships among chunks are identified. Chunks relationships can be complementary, contained within one another, instances of one another, or not related at all. For a given multi-part query, a query answer graph is created comprising answer nodes corresponding to potential answer candidates associated with each chunk. Using the edges between nodes and relationships identified between chunks, coherence scores are calculated along answer pathways, whereby answer pathways with the highest coherence score are selected to respond to the query.

Multi-chunk relationship extraction and maximization of query answer coherence

Systems, computer-implemented methods, and/or computer program products facilitating a process to monitor and evaluate the effects of an artificial intelligence (AI) model on enterprise performance metrics are provided. According to an embodiment, a computer implemented method can comprise determining a technical issue of candidate technical issues associated with an artificial intelligence model that correlates to a change associated with a performance metric, wherein the determination is based on using a first data model that defines first relationships between the key performance metrics and candidate technical issues and second relationships between the candidate technical issues and candidate solutions. The method further comprises determining a solution for the technical issue using the data model and recommending or automatically implementing the solution. The method further provides for updating/refining the data model over time using continuous learning based on evaluating whether and how implemented solutions impact the relevant performance metrics.

Evaluating effects of an artificial intelligence model on enterprise performance objectives

Various embodiments provide for decentralized crowd sourced generation of recommendation data objects. An example apparatus receives, originating from an external computing device, a recommendation data object request, the recommendation data object request comprising a user identifier and one or more user attributes. The example apparatus may further retrieve, based on a predictive recommendation model, one or more therapy identifiers associated with a therapy efficacy score exceeding a therapy efficacy score threshold for attributes of a first attributes set associated with a first cluster identifier, the first attributes set comprising one or more of the one or more user attributes. The predictive recommendation model is trained based at least in part on trusted efficacy blocks of a distributed ledger, where the trusted efficacy blocks are added to the distributed ledger when a number of therapy efficacy transaction blocks associated with a given therapy identifier and having a therapy efficacy score exceeding a therapy efficacy score threshold meets or exceeds a therapy efficacy transaction block threshold. The example apparatus may further transmit, to the external computing device, a recommendation data object configured for rendering for display via a display device of the external computing device, where the recommendation data object comprises one or more of the one or more therapy identifiers.

Generating multi-dimensional recommendation data objects based on decentralized crowd sourcing

Methods, apparatus, and processor-readable storage media for character-based representation learning for table data extraction using artificial intelligence techniques are provided herein. An example computer-implemented method includes identifying, from unstructured documents comprising tabular data, items of text and corresponding document position information using artificial intelligence-based text extraction techniques; generating an intermediate output by implementing character embedding with respect to the unstructured documents using an artificial intelligence-based encoder; determining structure-related information for the unstructured documents using one or more artificial intelligence-based graph-related techniques by inferring columns from the tabular data; generating a character-based representation of the unstructured documents using an artificial intelligence-based decoder by converting the inferred columns into one or more line items; classifying portions of the character-based representation using artificial intelligence-based statistical modeling techniques; and performing one or more automated actions based on the classifying.

Character-based representation learning for table data extraction using artificial intelligence techniques

Embodiments are provided that relate to a computer system, a computer program product, and a computer-implemented method for improving performance of a virtual dialog agent system employing an automated virtual dialog agent. Embodiments involve generating ground truth (GT) from a user's knowledge base, and leveraging the GT to evaluate how the virtual dialog agent performs with the GT. The evaluation measures quality of a multi-turn virtual dialog, and generates a remediation plan directed at an algorithmic improvement of the virtual dialog agent.

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Bengaluru, India

Atul Kumar