Citibank, N.a.

Ganesh Prasad Bhat

Joshua Adam Goldman

James Myers

Wei Jie Ng

Subramanian Sankaran

Ramee S Karthikeyan

Jason Ryan Engelbrecht

John E Ortega, Jr

Alberto Cetoli

Ines Teixeira

Yael Man

Minjeong Cho

Alex Michael Eng

Cameron Paul Lim

Matthew Ryan Mitsui

Ramkumar Ayyadurai

William Franklin Cameron

Chamindra Desilva

Vishal Mysore

Venkata Uttam Kumar Chunduri

Ryan Bergeron

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

Encrypted autonomous agent verification in multi-tiered distributed systems across global or cloud networks

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

Systems and methods disclosed herein automatically generate and executing computer programs using artificial intelligence (AI) models. The system obtains an output generation request (e.g., from a graphical user interface (GUI)) to generate an output using an unstructured dataset. The system determines a feature set of the unstructured dataset using a first AI model set, and dynamically generates a programmatic workflow set using a second AI model set (same as or different from the first AI model set) based on the feature set to be stored in a distributed database. The system automatically executes the programmatic workflow set using a third AI model set (same as or different from the first and/or second AI model sets) to generate an artifact responsive to the output generation request. The system displays (e.g., on the GUI) representations of the output generation request, programmatic workflow set, and/or generated artifact.

Automatic generation and execution of computer-executable commands using artificial intelligence models

The systems and methods disclosed herein generate responses using data retrieved by validating vector embeddings using hash signatures. An output generation request is received via a computing device and includes an input that includes a content set and a command set. The content set includes data chunks, which refers to text, audio, image, and/or video data. A subset of the data chunks that fail to associate with existing hash signatures are selected. Each selected data chunk is validated against predefined constraints that define the operative boundaries of a guideline set and subsequently assigned a unique hash signature to indicate a degree of satisfaction of the data chunk with the guidelines. Using an artificial intelligence (AI) model, a response to the output generation request is generated in accordance with the data chunks, where each data chunk is associated with a corresponding unique hash signature.

Validating artificial intelligence model outputs using hash signatures and chunk-level access controls

Systems and methods disclosed herein generate validated responses using artificial intelligence (AI)-based models. The system obtains/receives an output generation request (e.g., from a graphical user interface (GUI)) that can include a document set and a query set. The system classifies the query set by partitioning it into multiple query subsets and assigning a complexity score. Based on the classification, the system generates a computational workflow set using a first AI model set to retrieve a resource set responsive to the query set. The system executes the workflow using a second AI model set (the same as or different from the first AI model set) and validates the retrieved resources against predefined criteria (e.g., rules, guidelines). If the resources satisfy the criteria, the system generates a response using a third AI model set. The system can display a graphical layout on the GUI showing the request, retrieved resources, and/or generated response.

Using a multi-model architecture for retrieval-augmented generation (RAG)

Growing community of inventors

West Orange, NJ, United States of America

Ganesh Prasad Bhat