18099187. SYSTEM AND METHOD FOR LEARNING RELATIONSHIPS BETWEEN PHYSIOLOGICAL SIGNALS AND PSYCHOLOGICAL STATE TO PREDICT INDIVIDUAL BEHAVIOR simplified abstract (TOYOTA RESEARCH INSTITUTE, INC.)

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SYSTEM AND METHOD FOR LEARNING RELATIONSHIPS BETWEEN PHYSIOLOGICAL SIGNALS AND PSYCHOLOGICAL STATE TO PREDICT INDIVIDUAL BEHAVIOR

Organization Name

TOYOTA RESEARCH INSTITUTE, INC.

Inventor(s)

Shabnam Hakimi of San Francisco CA (US)

Charlene Wu of San Francisco CA (US)

Yanxia Zhang of Foster City CA (US)

SYSTEM AND METHOD FOR LEARNING RELATIONSHIPS BETWEEN PHYSIOLOGICAL SIGNALS AND PSYCHOLOGICAL STATE TO PREDICT INDIVIDUAL BEHAVIOR - A simplified explanation of the abstract

This abstract first appeared for US patent application 18099187 titled 'SYSTEM AND METHOD FOR LEARNING RELATIONSHIPS BETWEEN PHYSIOLOGICAL SIGNALS AND PSYCHOLOGICAL STATE TO PREDICT INDIVIDUAL BEHAVIOR

The abstract describes a method for predicting learned behavior based on physiological data from different modalities, grouping the data by similar behaviors, learning a shared latent space, and utilizing a behavior prediction engine to predict future behavior.

  • Physiological data from various modalities is received.
  • The data is grouped based on similar behaviors.
  • A shared latent space is learned by a transfer function learner.
  • Embeddings from different modalities are grouped in the latent space according to similar behaviors.
  • A behavior prediction engine uses the trained transfer function learner to predict an individual's future behavior based on physiological data and a user-specified prediction model.

Potential Applications: - Personalized healthcare monitoring - Behavioral analysis in psychology - Predictive maintenance in industrial settings

Problems Solved: - Predicting future behavior based on physiological data - Grouping data from different modalities to enhance prediction accuracy

Benefits: - Improved accuracy in predicting learned behavior - Personalized behavior prediction for individuals - Enhanced understanding of behavior patterns

Commercial Applications: Predictive analytics software for healthcare providers to monitor patient behavior and predict health outcomes.

Questions about the technology: 1. How does the shared latent space improve behavior prediction accuracy? 2. What are the potential limitations of this method in real-world applications?


Original Abstract Submitted

A method for learned behavior prediction is described. The method includes receiving physiological data from a plurality of different modalities. The method also includes grouping the received physiological data by corresponding, similar behaviors. The method further includes learning, by a transfer function learner, a shared latent space, in which embeddings from the different modalities are grouped according to the similar behaviors. The method also includes utilizing, by a behavior prediction engine, a trained, transfer function learner to predict an individual's future behavior based on an input of physiological data and a user-specified prediction model.

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