公开(公告)号：US20250013869A1

公开(公告)日：2025-01-09

申请号：US18762333

申请日：2024-07-02

Applicant: SRI International

Inventor： John Cadigan ,  Dayne Brian Freitag ,  John Joseph Niekrasz ,  Chih-hung Yeh

IPC: G06N3/084

Abstract: In an example, a method for a method for training a Machine Learning (ML) model using arbitrarily sized training data files, to selectively identify informative portions of one or more training data files for improving the ML model includes automatically selectively identifying, by a computing system, one or more informative portions of one or more training data files; calculating, by the computing system, gradients for the identified one or more informative portions; and updating, by the computing system, weights of a ML model using the calculated gradients.

公开(公告)号：US20240193366A1

公开(公告)日：2024-06-13

申请号：US18534210

申请日：2023-12-08

Applicant: SRI International

Inventor： Dayne Brian Freitag ,  John Cadigan ,  John Joseph Niekrasz ,  Robert Vincent Sasseen

IPC: G06F40/289 ,  G06F40/284

CPC classification number: G06F40/289 ,  G06F40/284

Abstract: A computing system is configured to process a first document using an anchor rule, wherein the anchor rule identifies tokens for a domain. The computing system is further configured to identify, using the anchor rule, a first set of phrases from the first document that match the tokens. The computing system is further configured to receive a first selection from a first subset of the first set of phrases. The computing system is further configured to determine, based on the first selection, a word list, wherein the word list is a list of words ranked by rate of appearance in the first document. The computing system is further configured to process, based on the word list, a second document to extract one or more points of information from the second document.

公开(公告)号：US20240147025A1

公开(公告)日：2024-05-02

申请号：US18471171

申请日：2023-09-20

Applicant: SRI International

Inventor： Martin Graciarena ,  John Cadigan ,  Alan Taitz

IPC: H04N21/83 ,  H04N21/81

CPC classification number: H04N21/83 ,  H04N21/814

Abstract: In general, the disclosure describes techniques for obtaining, by a computing system, a content item and a purported source for the content item, wherein the content item may include multimodal data. The techniques may further include generating, by the computing system, a plurality of modality feature vectors representative of the multimodal data, wherein each of the generated modality feature vectors has a different, corresponding modality feature. The techniques may further include mapping, by the computing system, the generated modality feature vectors based on a statistical distribution associated with the purported source. The techniques may further include determining, by the computing system, a score based on the mapping. The techniques may further include outputting, by the computing system and based on the score, an indication of whether the content item originated from the purported source.

公开(公告)号：US20240378198A1

公开(公告)日：2024-11-14

申请号：US18391339

申请日：2023-12-20

Applicant: SRI International

Inventor： Mario Latendresse ,  John Cadigan

IPC: G06F16/2452 ,  G06F16/242 ,  G06F40/253

Abstract: In an example, a method includes, generating, by a machine learning system, one or more formal queries based on data contained in a database repository; generating, by the machine learning system, a natural language query for each formal query of the one or more formal queries to generate pairs of formal queries and corresponding natural language queries by applying a general grammar for a language of each formal query; and training, by the machine learning system, a neural network configured to translate natural language queries into formal queries using the pairs of the formal queries and corresponding natural language queries generated by the machine learning system.

公开(公告)号：US20240152540A1

公开(公告)日：2024-05-09

申请号：US18472761

申请日：2023-09-22

Applicant: SRI International

Inventor： John Cadigan ,  Martin Graciarena

IPC: G06F16/35

CPC classification number: G06F16/353

Abstract: In an example, a method for adapting a machine learning model includes receiving first input data; choosing a first set of unlabeled textual spans in the first input data, wherein the chosen first set of unlabeled textual spans is associated with a first domain; labeling the chosen first set of unlabeled textual spans to generate a labeled first set of textual spans; categorizing the labeled first set of textual spans to generate a categorized labeled first set of textual spans; receiving second input data; choosing a second set of unlabeled textual spans, wherein the second set of unlabeled textual spans is associated with a second domain; and adapting the machine learning model to the second domain based on the categorized second set of unlabeled textual spans that is generated based on the categorized labeled first set of textual spans.

公开(公告)号：US20230394413A1

公开(公告)日：2023-12-07

申请号：US18330930

申请日：2023-06-07

Applicant: SRI International

Inventor： Subhodev Das ,  Aswin Nadamuni Raghavan ,  Avraham Joshua Ziskind ,  Timothy J. Meo ,  Bhoram Lee ,  Chih-hung Yeh ,  John Cadigan ,  Ali Chaudhry ,  Jonathan C. Balloch

IPC: G06Q10/0637

CPC classification number: G06Q10/06375

Abstract: In general, the disclosure describes techniques for Artificial Intelligence (AI) models that can automatically generate diverse, explainable, interpretable, reactive, and coordinated behaviors for a team. In an example, a method includes receiving multimodal input data within a simulator configured to simulate solving a predefined problem by a team including a plurality of agents; generating one or more generative neural network models based on the multimodal input data and based on a predetermined threshold of success of problem solving in the simulator; outputting, by the one or more generative neural network models, one or more multi-agent controllers, wherein each of the one or more multi-agent controllers comprises recommended behaviors for each of the plurality of agents to solve the predefined problem in a manner that is consistent with the multimodal input data.