公开(公告)号：WO2018208813A1

公开(公告)日：2018-11-15

申请号：PCT/US2018/031645

申请日：2018-05-08

Applicant: SRI INTERNATIONAL

Inventor： BYRNES, John ,  ROHWER, Richard

IPC: G06F15/18

Abstract: An artificial intelligence engine that has two or more modules cooperating with each other in order to create one or more machine learning models that use an adaptive semantic learning for knowledge representations and reasoning. The modules cause encoding the representations and reasoning from one or more sources in a particular field with terminology used by one or more human sources in that field into a set of rules that act as constraints and that are graphed into a neural network understandable by a first machine learning model, and then ii) adapting an interpretation of that set of encoded rules. The understanding of that set of encoded rules is adapted by i) allowing for semantically similar terms and ii) by conclusions derived from training data, to create an understanding of that set of encoded rules utilized by the machine learning model and the Al engine.

公开(公告)号：WO2021184013A1

公开(公告)日：2021-09-16

申请号：PCT/US2021/022401

申请日：2021-03-15

Applicant: SRI INTERNATIONAL

Inventor： BYRNES, John ,  ROHWER, Richard ,  SILBERFARB, Andrew

IPC: G06N3/02 ,  G06N7/00 ,  H03K19/00

Abstract: A neural-symbolic computing engine can have two or more modules that are configured to cooperate with each other in order to create one or more gradient- based machine learning models that use machine learning on i) knowledge representations and ii) reasoning to solve an issue. A model representation module in the neural-symbolic computing engine is configured to apply one or more mathematical functions, at least including a logit transform, to truth values from first order logic elements supplied from a language module of the neural-symbolic computing engine.

公开(公告)号：WO2017184204A1

公开(公告)日：2017-10-26

申请号：PCT/US2016/061558

申请日：2016-11-11

Applicant: SRI INTERNATIONAL

Inventor： ACHARYA, Girish ,  NIEKRASZ, John ,  BYRNES, John ,  YEH, Chih-Hung

IPC: G06F17/27

CPC classification number: G06F16/3334 ,  G06F16/345 ,  G06F16/9535 ,  G06F17/27 ,  G06F17/2705 ,  G06F17/2745

Abstract: Disclosed techniques can generate content object summaries. Content of a content object can be parsed into a set of word groups. For each word group, at least one topic to which the word group pertains can be identified and it can be determined, via a user model, at least one weight of the plurality of weights corresponding to the topic(s). For each word group, a score can be determined for the word group based on the weight(s). A subset of the set of word groups can be selected based on the scores for the word group. A summary of the content object can be generated that includes the subset but that does not include one or more other word groups in the set of word groups that are not in the subset. At least part of the summary of the content object can be output.

Abstract translation: 所公开的技术可以生成内容对象摘要。 内容对象的内容可以被解析为一组词组。 对于每个词组，至少一个该词组所属的主题可以被标识，并且可以通过用户模型确定与该主题相对应的多个权重中的至少一个权重。 对于每个单词组，可以基于权重为单词组确定分数。 可以基于单词组的分数来选择该组单词的子集。 可以生成内容对象的摘要，其包括该子集但不包括不在该子集中的一组词组中的一个或多个其他词组。 至少可以输出内容对象摘要的一部分。