Keas, Inc.

Seniorlink Incorporated

George Kassabgi

Adam Bosworth

Stu Statman

Stephan Richter

Systems and methods of automatically generating a proposed response to user input are provided. A method includes receiving, via a text-based messaging system, a plurality of messages from a user, determining a respective confidence level for each message of the plurality of messages that the respective message has a respective intent corresponding to a respective defined intent, generating, responsive to the defined intent of a first message of the plurality of messages being a structured intent, a first proposed communication from the structured intent, and generating, responsive to the defined intent of a second message of the plurality of messages being a predictive intent and the confidence level of the second message meeting a defined threshold, a second proposed communication from the predictive intent.

Methods and systems for a bimodal auto-response mechanism for messaging applications

A method for processing risk factors for a user is disclosed. The method may include receiving protocol data for creating a risk factor engine by an application stored and executed at a computing device. The risk factor engine may be stored in memory and executable by a processor to process a first set of stored user health data for a user. The protocol data may include a rule and one or more health attribute values. The rule and the one or more health attribute values may each include a computer programming expression editable by an administrator. The method may further include determining a user health score through execution of the risk factor engine by the processor and based on the user health data.

Risk factor engine that determines a user health score using a food consumption trend, and predicted user weights

Risk factor engine that determines a user health score

Risk factor data can be processed by a risk factor coaching engine to determine health risk for a user. The risk factor coaching engine may be executed within a health coaching protocol to perform actions that provide a user with information, recommendations and alerts via other coaching engines, and appointments with health care professionals. The risk factor coaching engine may also predict attribute values for a user based on a time period and goals for user health data upon which the predicted attribute value is based.

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Winchester, MA, United States of America

George Kassabgi