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    CROSS-VIEW VISUAL GEO-LOCALIZATION FOR ACCURATE GLOBAL ORIENTATION AND LOCATION
    12.
    发明公开
    CROSS-VIEW VISUAL GEO-LOCALIZATION FOR ACCURATE GLOBAL ORIENTATION AND LOCATION 审中-公开

    公开(公告)号：US20240303860A1

    公开(公告)日：2024-09-12

    申请号：US18600424

    申请日：2024-03-08

    Applicant: SRI International

    Inventor： Niluthpol MITHUN ,  Kshitij MINHAS ,  Han-Pang CHIU ,  Taragay OSKIPER ,  Mikhail SIZINTSEV ,  Supun SAMARASEKERA ,  Rakesh KUMAR

    IPC: G06T7/73

    CPC classification number: G06T7/74 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: A method, apparatus, and system for providing orientation and location estimates for a query ground image include determining spatial-aware features of a ground image and applying a model to the determined spatial-aware features to determine orientation and location estimates of the ground image. The model can be trained by collecting a set of ground images, determining spatial-aware features for the ground images, collecting a set of geo-referenced images, determining spatial-aware features for the geo-referenced images, determining a similarity of the spatial-aware features of the ground images and the geo-referenced images, pairing ground images and geo-referenced images based on the determined similarity, determining a loss function that jointly evaluates orientation and location information, creating a training set including the paired ground images and geo-referenced images and the loss function, and training the neural network to determine orientation and location estimates of ground images without the use of 3D data.

    Outcome-Guided Counterfactuals from a Jointly Trained Generative Latent Space
    14.
    发明公开
    Outcome-Guided Counterfactuals from a Jointly Trained Generative Latent Space 审中-公开

    公开(公告)号：US20240256858A1

    公开(公告)日：2024-08-01

    申请号：US18393182

    申请日：2023-12-21

    Applicant: SRI International

    Inventor： Chih-hung YEH ,  Pedro Daniel BARBOSA SEQUEIRA ,  Melinda T. GERVASIO ,  Jesse Albert HOSTETLER

    IPC: G06N3/08 ,  G06N3/0475

    CPC classification number: G06N3/08 ,  G06N3/0475

    Abstract: In general, techniques are described for generating counterfactuals using a machine learning system that implements a generative model. In an example, a method includes receiving, by a trained generative machine learning model, an input query, wherein the generative machine learning model is trained by jointly encoding a plurality of input observations and a plurality of outcome variables based on the plurality of input observations; generating, by the trained generative machine learning model, latent representation of the input query; and transforming, by the trained generative machine learning system, the latent representation of the input query to generate a counterfactual related to the received input query, wherein the generated counterfactual meets a predefined outcome criteria.

    METHOD AND SYSTEM FOR DETERMINING A MEASURE OF CONCEPTUAL CONSISTENCY IN LARGE LANGUAGE MODELS
    17.
    发明公开
    METHOD AND SYSTEM FOR DETERMINING A MEASURE OF CONCEPTUAL CONSISTENCY IN LARGE LANGUAGE MODELS 审中-公开

    公开(公告)号：US20240242040A1

    公开(公告)日：2024-07-18

    申请号：US18541035

    申请日：2023-12-15

    Applicant: SRI International

    Inventor： Michael COGSWELL ,  Ajay DIVAKARAN ,  Yunye GONG ,  Pritish SAHU

    IPC: G06F40/40

    CPC classification number: G06F40/40

    Abstract: Embodiments of the present principles generally relate to methods, apparatuses and systems for determining a measure of conceptual consistency in large language models for understanding of relevant concepts. In some embodiments, a method for measuring conceptual consistency may include prompting an LLM in order to extract answers to background queries and anchor tasks. The method also includes comparing background knowledge facts for a given anchor task associated with known answers with facts extracted from the LLM to determine an LLM performance. The method also includes determining a background knowledge score and an anchor task score based on the LLM's performance. The method also includes determining a conceptual may include score for the LLM by predicting the anchor task score from the background knowledge score. The method also includes outputting an indication of the conceptual may include score.

    SPATIAL-TEMPORAL ANOMALY AND EVENT DETECTION USING NIGHT VISION SENSORS
    18.
    发明公开
    SPATIAL-TEMPORAL ANOMALY AND EVENT DETECTION USING NIGHT VISION SENSORS 审中-公开

    公开(公告)号：US20240212350A1

    公开(公告)日：2024-06-27

    申请号：US18331007

    申请日：2023-06-07

    Applicant: SRI International

    Inventor： Subhodev Das ,  Ajay Divakaran ,  Ali Chaudhry ,  Julia Kruk ,  Bo Dong

    IPC: G06V20/40 ,  G06V10/44 ,  H04N23/21

    CPC classification number: G06V20/44 ,  G06V10/44 ,  H04N23/21

    Abstract: In general, the disclosure describes techniques for joint spatiotemporal Artificial Intelligence (AI) models that can encompass multiple space and time resolutions through self-supervised learning. In an example, a method includes for each of a plurality of multimodal data, generating, by a computing system, using a first machine learning model, a respective modality feature vector representative of content of the multimodal data, wherein each of the generated modality feature vectors has a different modality; processing, by the computing system, each of generated modality feature vectors with a second machine learning model comprising an encoder model to generate event data comprising a plurality of events and/or activities of interest; and analyzing, by the computing system, the event data to generate anomaly data indicative of detected anomalies in the multimodal data.

    SYSTEM DESIGN FOR AN INTEGRATED LIFELONG MACHINE LEARNING AGENT
    19.
    发明公开
    SYSTEM DESIGN FOR AN INTEGRATED LIFELONG MACHINE LEARNING AGENT 审中-公开

    公开(公告)号：US20240202538A1

    公开(公告)日：2024-06-20

    申请号：US18535928

    申请日：2023-12-11

    Applicant: SRI International

    Inventor： Aswin NADAMUNI RAGHAVAN ,  Indranil SUR ,  Zachary DANIELS ,  Jesse HOSTETLER ,  Abrar RAHMAN ,  Ajay DIVAKARAN ,  Michael R. PIACENTINO

    IPC: G06N3/092

    CPC classification number: G06N3/092

    Abstract: A method, apparatus and system for lifelong reinforcement learning include receiving features of a task, communicating the task features to a learning system, where the learning system learns or performs a task related to the features based on learning or performing similar previous tasks, determining from the features if the task has changed and if so, communicating the features of the changed task to the learning system, where the learning system learns or performs the changed task based on learning or performing similar previous tasks, automatically annotating feature characteristics of received features including differences between the features of the original task and the features of the changed task to enable the learning system to more efficiently learn or perform at least the changed task, and if the task has not changed, processing the task features of a current task by the learning system to learn or perform the current task.

    ACCELERATED INFORMATION EXTRACTION THROUGH FACILITATED RULE DEVELOPMENT
    20.
    发明公开
    ACCELERATED INFORMATION EXTRACTION THROUGH FACILITATED RULE DEVELOPMENT 审中-公开

    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.

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