Machine-learning-based predictive behaviorial monitoring

Patent metadata

Field	Value
Publication number	US-2022059230-A1
Application number	US-202016999133-A
Country	US
Kind code	A1
Filing date	Aug 21, 2020
Priority date	Aug 21, 2020
Publication date	Feb 24, 2022
Grant date	—

Abstract

Systems and methods are configured to perform machine-learning-based predictive behavioral response. In various embodiments, one or more behavioral monitoring data objects are identified and processed using a behavioral pattern prediction machine learning model to generate a behavioral pattern prediction model. The behavioral pattern prediction model is processed using a risk generation machine learning model to generate a risk model, wherein: (i) the risk generation machine learning model is generated based at least in part by one or more risk factors, and (ii) the risk model comprises a per-risk factor score for each risk factor of the one or more risk factors. The risk model is processed using an adjustment generation machine learning model to generate an adjustment model and one or more prediction-based actions are performed based on the adjustment model.

First claim

1 . A computer-implemented method for predictive behavioral response, the computer-implemented comprising:
receiving one or more behavioral monitoring data objects generated by one or more distributed behavioral monitoring devices; processing the one or more behavioral monitoring data objects using a behavioral pattern prediction machine learning model to generate a behavioral pattern prediction model, wherein the behavioral pattern prediction model identifies occurrences of an end user participating in one or more behaviors; and in response to generating the behavioral pattern prediction model:
processing the one or more patterns of behavior for the one or more behaviors using a risk generation machine learning model to generate a risk model, wherein: (i) the risk generation machine learning model is generated based at least in part on one or more risk factors contributed to by the one or more behaviors, (ii) the risk model comprises a per-risk factor score for each risk factor of the one or more risk factors that identifies an effect of the one or more patterns of behavior, and (iii) each of the patterns of behavior identifies instances of the end user participating in a corresponding behavior of the one or more behaviors over a period of time;
processing the risk model using an adjustment generation machine learning model to generate an adjustment model based on the risk model; and
performing one or more prediction-based actions based on the adjustment model.

2 . The computer-implemented method of claim 1 , wherein the risk generation machine learning model is configured to generate the risk model by:
generating a general risk model based on the one or more patterns of behavior; generating an individual risk model based on the one or more patterns of behavior in relation to the end user; and combining the general risk model and the individual risk model to generate the risk model.

3 . The computer-implemented method of claim 1 , wherein the adjustment generation machine learning model comprises a time series prediction model and an adjustment application model.

4 . The computer-implemented method of claim 3 , wherein the adjustment generation machine learning model generates the adjustment model by:
determining a projected contribution for a prospective period of time by using the time series prediction model; and processing the projected contribution and the risk model using the adjustment application model to generate the adjustment model.

5 . The computer-implemented method of claim 3 , wherein the time series prediction model comprises an autoregressive integrated moving average model.

6 . The computer-implemented method of claim 1 , wherein the one or more prediction-based actions comprise automatically adjusting a current contribution rate to a financial instrument based on the adjustment model.

7 . The computer-implemented method of claim 1 , wherein the one or more risk factors are determined based on one or more medical conditions applicable to the end user and the computer-implemented method further configures processing a health profile data object for the end user using a medical conditions prediction machine learning model to identify the one or more medical conditions, wherein the medical conditions prediction machine learning model is configured to identify a probability for each of a plurality of medical conditions identifying a likelihood of the end user developing the medical condition.

8 . An apparatus for predictive behavioral response, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the processor, cause the apparatus to at least:
receive one or more behavioral monitoring data objects generated by one or more distributed behavioral monitoring devices; process the one or more behavioral monitoring data objects using a behavioral pattern prediction machine learning model to generate a behavioral pattern prediction model, wherein the behavioral pattern prediction model identifies occurrences of an end user participating in one or more behaviors; and in response to generating the behavioral pattern prediction model:
process the one or more patterns of behavior for the one or more behaviors using a risk generation machine learning model to generate a risk model, wherein: (i) the risk generation machine learning model is generated based at least in part on one or more risk factors contributed to by the one or more behaviors, (ii) the risk model comprises a per-risk factor score for each risk factor of the one or more risk factors that identifies an effect of the one or more patterns of behavior, and (iii) each of the patterns of behavior identifies instances of the end user participating in a corresponding behavior of the one or more behaviors over a period of time;
process the risk model using an adjustment generation machine learning model to generate an adjustment model based on the risk model; and
have one or more prediction-based actions performed based on the adjustment model.

9 . The apparatus of claim 8 , wherein the risk generation machine learning model is configured to generate the risk model by:
generating a general risk model based on the one or more patterns of behavior; generating an individual risk model based on the one or more patterns of behavior in relation to the end user; and combining the general risk model and the individual risk model to generate the risk model.

10 . The apparatus of claim 8 , wherein the adjustment generation machine learning model comprises a time series prediction model and an adjustment application model.

11 . The apparatus of claim 10 , wherein the adjustment generation machine learning model generates the adjustment model by:
determining a projected contribution for a prospective period of time by using the time series prediction model; and processing the projected contribution and the risk model using the adjustment application model to generate the adjustment model.

12 . The apparatus of claim 10 , wherein the time series prediction model comprises an autoregressive integrated moving average model.

13 . The apparatus of claim 8 , wherein the one or more prediction-based actions comprise automatically adjusting a current contribution rate to a financial instrument based on the adjustment model.

14 . The apparatus of claim 8 , wherein the one or more risk factors are determined based on one or more medical conditions applicable to the end user and the at least one memory and the program code are configured to, with the processor, cause the apparatus to process a health profile data object for the end user using a medical conditions prediction machine learning model to identify the one or more medical conditions, wherein the medical conditions prediction machine learning model is configured to identify a probability for each of a plurality of medical conditions identifying a likelihood of the end user developing the medical condition.

15 . A computer program product for predictive behavioral response, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:
receive one or more behavioral monitoring data objects generated by one or more distributed behavioral monitoring devices; process the one or more behavioral monitoring data objects using a behavioral pattern prediction machine learning model to generate a behavioral pattern prediction model

Assignees

• Optum Inc

Inventors

• Muse Jon Kevin
• Boss Gregory J
• Godden Paul J
• Megerian Mark Gregory

Classifications

• G16H50/30 Primary

  for calculating health indices; for individual health risk assessment · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title