Citibank, N.a.

Nuance Communications, Inc.

John E Ortega, Jr

Jason Ryan Engelbrecht

Alberto Cetoli

Minjeong Cho

Yael Man

Ines Teixeira

Youval Bitner

Joel Branch

James Myers

Ganesh Prasad Bhat

Wei Jie Ng

Ramee S Karthikeyan

Subramanian Sankaran

Alex Michael Eng

Cameron Paul Lim

Matthew Ryan Mitsui

William Franklin Cameron

Ravi Kumar Kondadadi

Brian William Delaney

Neil D Barrett

Oscar Ferrandez-Escamez

Ryan Bergeron

The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Remediation of unstructured data using artificial intelligence

Systems and methods are disclosed comprising instructions to receive a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set, access an authorization schema set available for the development service, identify an applicable authorization schema from the authorization schema set via comparing the content embeddings of the digital artifacts and the reference embeddings of the authorization schemas, retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema, predict an authorization status for the development service using the historical artifact attribute set and the artifact attribute set, configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service.

Robust artifacts mapping and authorization systems and methods for operating the same

The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.

Detecting data anomalies using artificial intelligence

The systems and methods disclosed herein generates responses generated by artificial intelligence (AI) models such as large language models (LLM) using intent-based rankings of retrieved information. The systems and methods disclosed herein receives an output generation request for the generation of an output using a set of AI models. Using a first AI model, a set of documents are retrieved using the received output generation request. The set of documents are partitioned into chunks. The chunks are ranked using a distance between the vector representation of the received output generation request and the vector representation of each chunk. A second AI model classifies the output generation request and chunks using an intent of the respective output generation request or chunk, and generates a second set of rankings using the intents. The set of AI models generate a response using the second set of rankings.

Using intent-based rankings to generate large language model responses

The systems and methods disclosed herein generate context-aware responses using semantically chunked information. The systems and methods disclosed herein partition a set of artifacts responsive to a query (e.g., a prompt for an artificial intelligence model such as a large language model) into a set of continuous chunks and associate each continuous chunk with a knowledge graph. The knowledge graph includes nodes representing chunks and edges indicating common attributes. The systems and methods disclosed herein modify node(s) in the graph by determining values of feature variables and adjusting edges in accordance with the values and generate contextualized chunks by associating or linking continuous chunks of node pairs using shared edges. The systems and methods disclosed herein use the contextualized chunks and query to generate a response using the artificial intelligence model.

Context-aware semantic chunking for information retrieval in large language models

A method, computer program product, and computing system for processing content concerning a plurality of patients using a CAC system to define one or more billing codes concerning a social habit status of one or more patients of the plurality of patients. The one or more billing codes concerning the social habit status of the one or more patients are provided to a user for review. Feedback is received from the user concerning the accuracy of the one or more billing codes. The feedback concerning the one or more billing codes is automatically processed to define one or more confidence scores. The CAC system is trained based, at least in part, upon the one or more confidence scores.

Automated analysis system and method

Growing community of inventors

New York, NY, United States of America

John E Ortega, Jr