Generating multi-dimensional recommendation data objects based on decentralized crowd sourcing

Patent metadata

Field	Value
Publication number	US-12002569-B2
Application number	US-202117143004-A
Country	US
Kind code	B2
Filing date	Jan 6, 2021
Priority date	Jan 6, 2021
Publication date	Jun 4, 2024
Grant date	Jun 4, 2024

Abstract

Various embodiments provide for decentralized crowd sourced generation of recommendation data objects. An example apparatus receives, originating from an external computing device, a recommendation data object request, the recommendation data object request comprising a user identifier and one or more user attributes. The example apparatus may further retrieve, based on a predictive recommendation model, one or more therapy identifiers associated with a therapy efficacy score exceeding a therapy efficacy score threshold for attributes of a first attributes set associated with a first cluster identifier, the first attributes set comprising one or more of the one or more user attributes. The predictive recommendation model is trained based at least in part on trusted efficacy blocks of a distributed ledger, where the trusted efficacy blocks are added to the distributed ledger when a number of therapy efficacy transaction blocks associated with a given therapy identifier and having a therapy efficacy score exceeding a therapy efficacy score threshold meets or exceeds a therapy efficacy transaction block threshold. The example apparatus may further transmit, to the external computing device, a recommendation data object configured for rendering for display via a display device of the external computing device, where the recommendation data object comprises one or more of the one or more therapy identifiers.

First claim

The invention claimed is:

1. A computing system comprising one or more processors and memory storing instructions that, with the one or more processors, configure the computing system to:
train, using a supervisory training function, a machine learning collaborative filtering model, using (i) a plurality of feature vectors corresponding to a plurality of user identifiers and (ii) known therapy efficacy data, to generate a trained machine learning collaborative filtering model;
receive, originating from an external computing device, a recommendation data object request, the recommendation data object request comprising a user identifier and one or more user attributes;
input the one or more user attributes into the trained machine learning collaborative filtering model;
generate, using the trained machine learning collaborative filtering model, one or more therapy identifiers associated with one or more therapy efficacy scores exceeding a therapy efficacy score threshold for one or more of the one or more user attributes;
provide, to the external computing device and in response to the recommendation data object request, a recommendation data object comprising one or more of the one or more therapy identifiers, causing the external computing device to provide, based at least in part on the recommendation data object, a therapy feedback object for generating a therapy efficacy transaction block for a distributed ledger, wherein the therapy efficacy transaction block comprises a therapy efficacy score for the user identifier and a therapy identifier; and
retrain, using a reinforcement learning function, the trained machine learning collaborative filtering model using (i) the plurality of feature vectors and (ii) a plurality of trusted efficacy blocks of the distributed ledger, wherein the plurality of trusted efficacy blocks is generated by:
generating, based at least in part on a therapy efficacy model, a plurality of therapy efficacy transaction blocks based at least in part on (i) a respective therapy efficacy score and (ii) one or more respective efficacy indication objects associated with each of a plurality of therapy identifiers, and
generating, based at least in part on an efficacy consensus protocol, a trusted efficacy block from the distributed ledger in response to a number of the plurality of therapy efficacy transaction blocks satisfying a therapy efficacy transaction block threshold.

2. The computing system of claim 1 , wherein the distributed ledger comprises a block chain comprising a plurality of transaction blocks.

3. The computing system of claim 1 , wherein the memory stores instructions that, with the one or more processors, further configure the computing system to:
generate, based at least in part on the user identifier and the one or more user attributes, a first feature vector comprising a plurality of data records, wherein each of the one or more user attributes corresponds to a unique data record of the plurality of data records; and
assign the first feature vector to a first cluster of a plurality of clusters according to a clustering model, the first cluster associated with the one or more of the one or more user attributes.

4. The computing system of claim 3 , wherein the memory stores instructions that, with the one or more processors, further configure the computing system to:
generate one or more of a biometrics transaction block or a patient profile transaction block based at least in part on the first feature vector; and
add the one or more of the biometrics transaction block or the patient profile transaction block to the distributed ledger.

5. The computing system of claim 4 , wherein the memory stores instructions that, with the one or more processors, further configure the computing system to:
add the one or more of the biometrics transaction block or the patient profile transaction block to the distributed ledger based at least in part on validating a hash of all blocks in the distributed ledger.

6. The computing system of claim 1 , wherein the memory stores instructions that, with the one or more processors, further configure the computing system to:
receive one or more therapy feedback objects comprising a plurality of therapy feedback data records and at least one therapy identifier of the one or more therapy identifiers;
generate, based in part on the therapy efficacy model and the plurality of therapy feedback data records, the therapy efficacy score for the at least one therapy identifier;
generate a therapy efficacy transaction block based at least in part on the therapy efficacy score, the at least one therapy identifier, and the one or more therapy feedback objects; and
cause storage of the therapy efficacy transaction block on the distributed ledger.

7. The computing system of claim 1 , wherein the therapy efficacy model is trained based at least in part on historical patient profile data, historical patient biometric data, and known therapy efficacy data.

8. The computing system of claim 1 , wherein the trained machine learning collaborative filtering model is periodically re-trained based at least in part on new trusted efficacy blocks added to the distributed ledger.

9. The computing system of claim 1 , wherein the recommendation data object is associated with a recommendation data object identifier.

10. The computing system of claim 1 , wherein the recommendation data object is associated with a recommendation category.

11. The computing system of claim 10 , wherein the recommendation category is one or more of lifestyle recommendation, mindfulness activity, sleep pattern, exercise regime, workout strategy, behavioral guideline, daily routine adjustment.

12. The computing system of claim 1 , wherein at least one therapy identifier of the one or more therapy identifiers is associated with a recommender category.

13. The computing system of claim 12 , wherein the recommender category is one or more of psychiatrist, behavioral therapist, researcher, educational institute, wellness coach, lifestyle guru, life coach.

14. The computing system of claim 3 , wherein the memory stores instructions that, with the one or more processors, further configure the computing system to:
generate, based at least in part on the first feature vector, a plurality of other feature vectors associated with a plurality of other user identifiers retrieved from a data repository; and
generate, via the clustering model, a plurality of clusters, wherein each cluster of the plurality of clusters comprises one or more other feature vectors of the plurality of other feature vectors.

15. The computing system of claim 14 , wherein the memory stores instructions that, with the one or more processors, further configure the computing system to:
generate, based at least in part on similarity scores associated with each cluster of the plurality of clusters, a plurality of sub-chains, wherein each sub-chain of the plurality of sub-chains comprises one or more clusters having similarity scores meeting or exceeding a similarity score threshold.

16. The computing system of claim 1 , wherein the memory stores instructions that, with the one or more processors, further configure the computing system to:
receive, originating from one or more electronic sensors associated with the user identifier, biometric data associated with the user identifier;
generate, based at least in part on the user identifier and the biometric data, a biometric data transaction block; and
add the biometric data transaction block to the distributed ledger.

17. The computing

Assignees

• Optum Tech Inc

Inventors

• Krishna Amit
• Dutta Rahul
• Shekhar Shubhendu
• Kumar Atul
• Hasija Love

Classifications

• G16H20/70 Primary

  relating to mental therapies, e.g. psychological therapy or autogenous training · CPC title

• G06N20/00

  Machine learning · CPC title

• G16H10/60

  for patient-specific data, e.g. for electronic patient records · CPC title

• G16H40/63

  for local operation · CPC title

• G16H50/20

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