Sri International

Fair Isaac Corporation

Toyota Motor Corporation

John James Byrnes

Richard Rohwer

Girish Acharya

Dayne B Freitag

Amir Tamrakar

Yi Yao

Edmond D Chow

Melinda Gervasio

John Niekrasz

Chih-Hung Yeh

Michael Wessel

William T Deans

Anirban Roy

Louise Yarnall

Kyle J McIntyre

Robert Sasseen

Clint Frederickson

Zachary Weiler

Tulay Muezzinoglu

Andrew Silberfarb

Christina Freyman

Makoto Okabe

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.

Machine learning system for technical knowledge capture

Systems and methods for forecasting the prominence of various attributes in a future subject matter area are disclosed. An attribute is determined based on inputs received by a computing system. A set of indicators is determined based on the attribute and features extracted from an existing document set. The prominence of the attribute in the existing document set is determined. A prominence estimate of the attribute in a future document set is determined.

Prediction of future prominence attributes in data set

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.

Neural-symbolic computing

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 AI engine.

Deep adaptive semantic logic network

An evaluation engine has two or more modules to assist a driver of a vehicle. A driver drowsiness module analyzes monitored features of the driver to recognize two or more levels of drowsiness of the driver of the vehicle. The driver drowsiness module evaluates drowsiness of the driver based on observed body language and facial analysis of the driver. The driver drowsiness module is configured to analyze live multi-modal sensor inputs from sensors against at least one of i) a trained artificial intelligence model and ii) a rules based model while the driver is driving the vehicle to produce an output comprising a driver drowsiness-level estimation. A driver assistance module provides one or more positive assistance mechanisms to the driver to return the driver to be at or above the designated level of drowsiness.

Driver monitoring and response system

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.

Techniques for user-centric document summarization

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.

Similarity metric relativized to a user's preferences

Technology for classifying a data set includes extracting one or more features from items of the data set, computing a specificity measure for the extracted features, and measuring the similarity of the extracted features to a set of characteristic features associated with the property of one or more reference models.

Distributional alignment of sets

The present invention is directed to a method and method which analyzes large amounts of information on a real-time basis with no previous static data set. Attributes of the data, which can be thought of as data concepts, that are present in the data stream are detected and isolated. These concepts are referred to as clusters and are used to ultimately determine the semantics of the data stream. The streaming clusters have no 'current membership' in the existing state of the clustering and thus the cluster sets, and their relationship to each other, must be generated and updated as the data is being received.

Systems and methods for real-time determination of the semantics of a data stream

A knowledge base is first characterized by an association-grounded semantics collapsed language. In response to the receipt of a query of the knowledge base, the collapsed language is used to determine whether there is an indication that the knowledge base contains knowledge requested in the query. Thereafter, the collapsed language can be used to carry out a full search for the knowledge much more efficiently than would otherwise be possible. Related methods, apparatus, and articles are also described.

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Poway, CA, United States of America

John James Byrnes