公开(公告)号：US20240281610A1

公开(公告)日：2024-08-22

申请号：US18583631

申请日：2024-02-21

Applicant: SRI International

Inventor： Dayne Freitag

IPC: G06F40/30 ,  G06Q10/101 ,  H04L67/55

CPC classification number: G06F40/30 ,  G06Q10/101 ,  H04L67/55

Abstract: The machine learning in the neural networks module can analyze an annotation and its metadata on the annotation made by a first user on a first computing device to make an embedding regarding the annotation and then cooperate with the persistence knowledge store to store the embedding of the machine learning's understanding of the annotation and its metadata. The delivery module can proactively push a notice regarding a potentially related embedding out to a second user on a second computing device based on a threshold amount of relatedness between one or more factors of i) a first task undertaken by the first user and a second task undertaken by the second user, ii) a role of the first user and a role of the second user, and iii) a subject matter of the embedding to a subject matter of a task undertaken by the second user.

公开(公告)号：US20210192972A1

公开(公告)日：2021-06-24

申请号：US17129541

申请日：2020-12-21

Applicant: SRI International

Inventor： Girish Acharya ,  Louise Yarnall ,  Anirban Roy ,  Michael Wessel ,  Yi Yao ,  John J. Byrnes ,  Dayne Freitag ,  Zachary Weiler ,  Paul Kalmar

IPC: G09B19/00 ,  G06K9/72 ,  G10L15/18 ,  G06K9/00 ,  G10L25/57 ,  G06K9/62 ,  G09B5/06 ,  G06N20/00

Abstract: This disclosure describes machine learning techniques for capturing human knowledge for performing a task. In one example, a video device obtains video data of a first user performing the task and one or more sensors generate sensor data during performance of the task. An audio device obtains audio data describing performance of the task. A computation engine applies a machine learning system to correlate the video data to the audio data and sensor data to identify portions of the video, sensor, and audio data that depict a same step of a plurality of steps for performing the task. The machine learning system further processes the correlated data to update a domain model defining performance of the task. A training unit applies the domain model to generate training information for performing the task. An output device outputs the training information for use in training a second user to perform the task.

公开(公告)号：US12118773B2

公开(公告)日：2024-10-15

申请号：US17129541

申请日：2020-12-21

Applicant: SRI International

Inventor： Girish Acharya ,  Louise Yarnall ,  Anirban Roy ,  Michael Wessel ,  Yi Yao ,  John J. Byrnes ,  Dayne Freitag ,  Zachary Weiler ,  Paul Kalmar

IPC: G06V10/82 ,  G06F18/22 ,  G06N20/00 ,  G06V20/20 ,  G06V20/40 ,  G06V30/19 ,  G06V30/262 ,  G06V40/10 ,  G06V40/20 ,  G09B5/06 ,  G09B19/00 ,  G10L15/18 ,  G10L25/57

CPC classification number: G06V10/82 ,  G06F18/22 ,  G06N20/00 ,  G06V20/20 ,  G06V20/41 ,  G06V30/19173 ,  G06V30/274 ,  G06V40/10 ,  G06V40/113 ,  G06V40/28 ,  G09B5/065 ,  G09B19/003 ,  G10L15/1815 ,  G10L25/57

Abstract: This disclosure describes machine learning techniques for capturing human knowledge for performing a task. In one example, a video device obtains video data of a first user performing the task and one or more sensors generate sensor data during performance of the task. An audio device obtains audio data describing performance of the task. A computation engine applies a machine learning system to correlate the video data to the audio data and sensor data to identify portions of the video, sensor, and audio data that depict a same step of a plurality of steps for performing the task. The machine learning system further processes the correlated data to update a domain model defining performance of the task. A training unit applies the domain model to generate training information for performing the task. An output device outputs the training information for use in training a second user to perform the task.

公开(公告)号：US20230179628A1

公开(公告)日：2023-06-08

申请号：US18059496

申请日：2022-11-29

Applicant: SRI International

Inventor： Phillip Porras ,  Kenneth Nitz ,  Keith Skinner ,  Dayne Freitag

IPC: H04L9/40 ,  G06F40/30

CPC classification number: H04L63/1483 ,  G06F40/30

Abstract: A method of determining an adversarial attack playbook includes receiving, from an adversarial actor, an electronic communication intended for a target user. The method includes engaging in a deep dialog with the adversarial actor by deploying a synthetic persona dynamically during the electronic communication. The deep dialog includes multiple rounds of communication exchanges. The method includes determining a length and type of the deep dialog to obtain attributes related to the adversarial actor. The method includes identifying a conversational pattern from the deep dialog. The conversational pattern comprises dialog interaction elements utilized by the adversarial actor. The method includes dynamically producing, based on the conversational pattern, the playbook associated with the adversarial actor. The playbook is indicative of a dialog interaction strategy implemented by the adversarial actor. The method includes providing the playbook to a social engineering attack (SEA) system in order to detect, avoid and/or mitigate future attacks.

公开(公告)号：US12267361B2

公开(公告)日：2025-04-01

申请号：US18059496

申请日：2022-11-29

Applicant: SRI International

Inventor： Phillip Porras ,  Kenneth Nitz ,  Keith Skinner ,  Dayne Freitag

IPC: H04L9/40 ,  G06F40/30 ,  G06F40/35

Abstract: A method of determining an adversarial attack playbook includes receiving, from an adversarial actor, an electronic communication intended for a target user. The method includes engaging in a deep dialog with the adversarial actor by deploying a synthetic persona dynamically during the electronic communication. The deep dialog includes multiple rounds of communication exchanges. The method includes determining a length and type of the deep dialog to obtain attributes related to the adversarial actor. The method includes identifying a conversational pattern from the deep dialog. The conversational pattern comprises dialog interaction elements utilized by the adversarial actor. The method includes dynamically producing, based on the conversational pattern, the playbook associated with the adversarial actor. The playbook is indicative of a dialog interaction strategy implemented by the adversarial actor. The method includes providing the playbook to a social engineering attack (SEA) system in order to detect, avoid and/or mitigate future attacks.

公开(公告)号：US10372704B2

公开(公告)日：2019-08-06

申请号：US14842483

申请日：2015-09-01

Applicant: SRI International

Inventor： John Byrnes ,  Dayne Freitag ,  Robert Sasseen ,  Melinda Gervasio

IPC: G06N7/00 ,  G06F16/242 ,  G06Q30/02 ,  G06F16/435

Abstract: Mathematical technologies for recommending content to a user based on a user's preferences are disclosed. Embodiments of these technologies can generate a probabilistic representation of a data set, and then adjust the probabilistic representation to reflect a user-specific weighting scheme. The user preference-adjusted representation of the data set can be used to recommend content to the user.

公开(公告)号：US20160092781A1

公开(公告)日：2016-03-31

申请号：US14842483

申请日：2015-09-01

Applicant: SRI International

Inventor： John Byrnes ,  Dayne Freitag ,  Robert Sasseen ,  Melinda Gervasio

IPC: G06N5/04 ,  G06F17/30 ,  G06N7/00

CPC classification number: G06F16/2425 ,  G06F16/435 ,  G06N7/005 ,  G06Q30/0255 ,  G06Q30/0631

Abstract: Mathematical technologies for recommending content to a user based on a user's preferences are disclosed. Embodiments of these technologies can generate a probabilistic representation of a data set, and then adjust the probabilistic representation to reflect a user-specific weighting scheme. The user preference-adjusted representation of the data set can be used to recommend content to the user.

Abstract translation: 公开了基于用户偏好向用户推荐内容的数学技术。 这些技术的实施例可以生成数据集的概率表示，然后调整概率表示以反映用户特定的加权方案。 数据集的用户偏好调整表示可以用于向用户推荐内容。