# US 2019/0171958 A1 · Adaptive analytical behavioral and health assistant system and related method of use

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- Number: US 2019/0171958 A1
- Document ID: US20190171958A1
- Status: Published application
- Kind: A1
- Application number: 16/179,060
- Filed: 2018-11-02
- Published: 2019-06-06
- Earliest claimed: 2011-03-24
- Assignees: Welldoc, Inc.
- Applicants: WellDoc, Inc.
- Inventors: Bharath SUDHARSAN
- Web page: https://patentgrep.com/patent/US20190171958A1

## CPC

- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR

## Abstract

This present disclosure relates to systems and methods for providing an Adaptive Analytical Behavioral and Health Assistant. These systems and methods may include collecting one or more of patient behavior information, clinical information, or personal information; learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms; identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns; preparing a plan based on the collected information and the identified interventions; and/or presenting the plan to a user or executing the plan.

## Claims

### Claim 1

A computer-implemented method for providing a health assistant system for dynamically creating a plan, the method comprising:
collecting one or more of patient behavior information, clinical information, or personal information;
learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms;
identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns;
preparing the plan based on the collected information and the identified interventions; and
presenting the plan to a user or executing the plan.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, the method further comprising:
receiving feedback relating to the plan; and
revising the plan based on the feedback.

### Claim 3 (depends on 2)

The computer-implemented method of claim 2, wherein:
the feedback is one or more patient behaviors that occur after the plan; and
the revised plan includes one or more additional interventions selected based on the feedback.

### Claim 4 (depends on 3)

The computer-implemented method of claim 3, wherein the one or more patient behaviors that occur after the plan include a behavior transition.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, the method further comprising determining one or more persons to associate with the identified one or more interventions.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, further comprising:
automatically revising probabilities from the collected information; and
storing the revised probabilities, wherein the revised probabilities are used to determine the plan.

### Claim 7 (depends on 1)

The computer-implemented method of claim 1, the method further comprising automatically making one or more inferences based on machine learning using one or more of the clinical information, behavior information, or personal information.

### Claim 8

An information processing device for determining a plan, the device comprising:
a processor for processing a set of instructions; and
a computer-readable storage medium for storing the set of instructions, wherein the instructions, when executed by the processor, perform a method comprising:
collecting one or more of patient behavior information, clinical information, or personal information;
learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms;
identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns;
preparing the plan based on the collected information and the identified interventions; and
presenting the plan to a user or executing the plan.

### Claim 9 (depends on 8)

The information processing device of claim 8, the method further comprising:
receiving feedback relating to the plan; and
revising the plan based on the feedback.

### Claim 10 (depends on 9)

The information processing device of claim 9, wherein:
the feedback is one or more patient behaviors that occur after the plan; and
the revised plan includes one or more additional interventions selected based on the feedback.

### Claim 11 (depends on 10)

The information processing device of claim 10, wherein the one or more patient behaviors that occur after the plan include a behavior transition.

### Claim 12 (depends on 8)

The information processing device of claim 8, the method further comprising determining one or more persons to associate with the identified intervention.

### Claim 13 (depends on 8)

The information processing device of claim 8, the method further comprising:
automatically revising probabilities from the collected information; and
storing the revised probabilities, wherein the revised probabilities are used to determine the plan.

### Claim 14 (depends on 8)

The information processing device of claim 8, wherein the one or more inferences are automatically made based on machine learning using one or more of the clinical information, behavior information, or personal information.

### Claim 15

A non-transitory computer-readable medium storing a set of instructions that, when executed by a processor, perform a method for determining a plan, the method comprising:
collecting one or more of patient behavior information, clinical information, or personal information;
learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms;
identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns;
preparing an plan based on the collected information and the identified interventions; and
presenting the plan to a user or executing the plan.

### Claim 16 (depends on 15)

The non-transitory computer-readable medium of claim 15, the method further comprising:
receiving feedback relating to the plan; and
revising the plan based on the feedback.

### Claim 17 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein:
the feedback is one or more patient behaviors that occur after the plan; and
the revised plan includes one or more additional interventions selected based on the feedback.

### Claim 18 (depends on 17)

The non-transitory computer-readable medium of claim 17, wherein the one or more patient behaviors that occur after the plan include a behavior transition.

### Claim 19 (depends on 15)

The non-transitory computer-readable medium of claim 15, the method further comprising determining one or more persons to associate with the identified one or more interventions.

### Claim 20 (depends on 15)

The non-transitory computer-readable medium of claim 15, the method further comprising:
automatically revising probabilities from the collected information; and
storing the revised probabilities, wherein the revised probabilities are used to determine the plan.