Deployment of self-contained decision logic

Patent metadata

Field	Value
Publication number	US-12586006-B2
Application number	US-202217970224-A
Country	US
Kind code	B2
Filing date	Oct 20, 2022
Priority date	Apr 25, 2016
Publication date	Mar 24, 2026
Grant date	Mar 24, 2026

Abstract

In one aspect there is provided a method. The method may include collecting one or more functions that implement the decision logic of a solution. A snapshot of the one or more functions can be generated. The snapshot can executable code associated with the one or more functions. The solution can be deployed by at least storing the snapshot of the one or more functions to a repository Systems and articles of manufacture, including computer program products, are also provided.

First claim

What is claimed is:

1 . A computer-implemented method comprising:
collecting one or more functions encapsulated within at least a first software container so that the one or more functions are deployable in two or more runtime environments; storing a first snapshot of the one or more functions in a first accessible location in a data repository, the first snapshot comprising first executable code associated with the one or more functions, the first executable code executable in a first runtime environment; in response to determining that the one or more functions are to be deployed in the first runtime environment, triggering a first execution component associated with the first runtime environment; downloading, using the first execution component, contents of the first snapshot and validating a configuration of the one or more functions; and deploying the validated configuration of the one or more functions in the first runtime environment.

2 . The computer-implemented method of claim 1 , wherein the validated configuration of the one or more functions is deployed by at least one of: rendering a snapshot over one or more networks as a web service, deploying the snapshot as a mobile application running on a mobile device, or deploying the snapshot as a listener to a data stream.

3 . The computer-implemented method of claim 1 , wherein the one or more functions implement a knowledge model that is part of a decision logic of a solution.

4 . The computer-implemented method of claim 3 , wherein the knowledge model comprises one or more of a predictive model, an optimization algorithm, a business and operational rule, a decision tree, a decisions table, a scorecard, and a decision graph.

5 . The computer-implemented method of claim 1 , wherein the one or more functions implement one or more of an input data object and a data transformation object.

6 . The computer-implemented method of claim 2 , wherein the first accessible location in a data repository comprises a bucket that is specifically provisioned for storing the snapshot.

7 . The computer-implemented method of claim 3 , wherein the solution is deployed as a batch job configured to apply the decision logic to a batch of input values.

8 . The computer-implemented method of claim 7 , wherein a scheduler of the batch job is configured to perform the batch job with a snapshot stored at a specific location within the data repository as a definition for the batch job.

9 . A computer-implemented system comprising:
at least one programmable processor; and a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising: collecting one or more functions encapsulated within at least a first software container so that the one or more functions are deployable in two or more runtime environments; storing a first snapshot of the one or more functions in a first accessible location in a data repository, the first snapshot comprising first executable code associated with the one or more functions, the first executable code executable in a first runtime environment; in response to determining that the one or more functions are to be deployed in the first runtime environment, triggering a first execution component associated with the first runtime environment; downloading, using the first execution component, contents of the first snapshot and validating a configuration of the one or more functions; and deploying the validated configuration of the one or more functions in the first runtime environment.

10 . A computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:
collecting one or more functions encapsulated within at least a first software container so that the one or more functions are deployable in two or more runtime environments; storing a first snapshot of the one or more functions in a first accessible location in a data repository, the first snapshot comprising first executable code associated with the one or more functions, the first executable code executable in a first runtime environment; in response to determining that the one or more functions are to be deployed in the first runtime environment, triggering a first execution component associated with the first runtime environment; downloading, using the first execution component, contents of the first snapshot and validating a configuration of the one or more functions; and deploying the validated configuration of the one or more functions in the first runtime environment.

11 . The computer-implemented system of claim 9 , wherein the validated configuration of the one or more functions is deployed by at least one of: rendering a snapshot over one or more networks as a web service, deploying the snapshot as a mobile application running on a mobile device, or deploying the snapshot as a listener to a data stream.

12 . The computer-implemented system of claim 9 , wherein the one or more functions implement a knowledge model that is part of a decision logic of a solution.

13 . The computer-implemented system of claim 12 , wherein the knowledge model comprises one or more of a predictive model, an optimization algorithm, a business and operational rule, a decision tree, a decisions table, a scorecard, and a decision graph.

14 . The computer-implemented system of claim 9 , wherein the one or more functions implement one or more of an input data object and a data transformation object.

15 . The computer-implemented system of claims 11 , wherein the first accessible location in a data repository comprises a bucket that is specifically provisioned for storing the snapshot.

16 . The computer-implemented system of claim 12 , wherein the solution is deployed as a batch job configured to apply the decision logic to a batch of input values.

17 . The computer-implemented system of claim 16 , wherein a scheduler of the batch job is configured to perform the batch job with a snapshot stored at a specific location within the data repository as a definition for the batch job.

18 . The computer program product of claim 10 , wherein the validated configuration of the one or more functions is deployed by at least one of: rendering a snapshot over one or more networks as a web service, deploying the snapshot as a mobile application running on a mobile device, or deploying the snapshot as a listener to a data stream.

19 . The computer program product of claim 18 , wherein the one or more functions implement a knowledge model that is part of a decision logic of a solution.

20 . The computer program product of claim 19 , wherein the knowledge model comprises one or more of a predictive model, an optimization algorithm, a business and operational rule, a decision tree, a decisions table, a scorecard, and a decision graph.

Assignees

• Fair Isaac Corp

Inventors

• Prismon Joshua
• Palskoi Andrei
• Holland Andrew K
• Donati Jorge Fernando Felipe Campos
• Wells Stuart Clarkson

Classifications

• G06N5/043

  Distributed expert systems; Blackboards · CPC title

• G06N5/02

  Knowledge representation; Symbolic representation · CPC title

• G06F11/16

  Error detection or correction of the data by redundancy in hardware · CPC title

• G06F11/14

  Error detection or correction of the data by redundancy in operations (error detection or correction of the data by redundancy in hardware G06F11/16) · CPC title

• G06F40/16

  Automatic learning of transformation rules, e.g. from examples · CPC title