# US 2019/0318255 A1 · Combining Entity Analysis and Predictive Analytics

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- Number: US 2019/0318255 A1
- Document ID: US20190318255A1
- Status: Published application
- Kind: A1
- Application number: 16/109,547
- Filed: 2018-08-22
- Published: 2019-10-17
- Earliest claimed: 2018-04-13
- Assignees: Fair Isaac Corporation
- Applicants: Fair Isaac Corporation
- Inventors: John R. Ripley; Michael Betron
- Web page: https://patentgrep.com/patent/US20190318255A1

## CPC

- G06N 5/046: Forward inferencing; Production systems
- G06F 16/2237: {Vectors, bitmaps or matrices}
- G06F 16/285: {Clustering or classification}
- G06F 16/93: Document management systems
- G06Q 10/0637: Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

In an aspect, an entity group including associations of entities grouped according to a measure of similarity can be received. The entities can include units of data extracted from a set of documents. A vector can be assembled. Assembly of the vector can include evaluation of a predefined entity analytic using the received entity group. The vector can be provided to a second analytic. Related apparatus, systems, techniques, and articles are also described.

## Claims

### Claim 1

A method comprising:
receiving an entity group including associations of entities grouped according to a measure of similarity, the entities including units of data extracted from a set of documents;
assembling a vector, the assembling comprising evaluating a predefined entity analytic using the received entity group; and
providing the vector to a second analytic.

### Claim 2 (depends on 1)

The method of claim 1, further comprising evaluating the second analytic using the vector as an input to the second analytic to form an output.

### Claim 3 (depends on 1)

The method of claim 1, wherein the vector includes features, the features including a set of values generated by the evaluation of the predefined entity analytic using the entity group.

### Claim 4 (depends on 1)

The method of claim 1, wherein evaluating the predefined entity analytic includes executing the predefined entity analytic with the received entity group to compute a vector value.

### Claim 5 (depends on 1)

The method of claim 1, wherein the predefined entity analytic includes a count, a sum, a standard deviation, a distinct, an external query, a complex logic script, a time-series analytic, a time-window analytic, or a source document analytic.

### Claim 6 (depends on 1)

The method of claim 1, wherein the second analytic includes:
a predictive analytic configured to generate electronic data corresponding to a predictive output;
a decision analytic configured to provide electronic data corresponding to a decision generated by applying one or more rules to the vector; or
a descriptive analytic configured to perform operations comprising
selecting a rule set to apply to the vector, to the entity group, or to both, by accessing a stored collection of rule sets;
generating a classification of the vector or the entity group based on at least the rule set; and
providing electronic data corresponding to the classification.

### Claim 7 (depends on 1)

The method of claim 1, further comprising extracting an entity from a document.

### Claim 8 (depends on 1)

The method of claim 1, further comprising assembling at least one record comprising at least one entity from at least one record source into a document.

### Claim 9 (depends on 1)

The method of claim 1, wherein the receiving is by an entity group analyzer and from an entity group assembler; the assembling of the vector is using the entity group analyzer; and the providing the vector to the second analytic is performed by the entity group analyzer.

### Claim 10 (depends on 9)

The method of claim 9, wherein the receiving, the assembling, and the providing is performed by at least one data processor forming part of at least one computing system.

### Claim 11 (depends on 1)

The method of claim 1, further comprising:
extracting the entities from the set of documents;
persisting the entities in an entity store;
persisting the documents in a document store;
assembling the entities into the entity group; and
evaluating the second analytic using the vector;
wherein the second analytic includes:
a predictive analytic configured to generate electronic data corresponding to a predictive output;
a decision analytic configured to provide electronic data corresponding to a decision generated by applying one or more rules to the vector; or
a descriptive analytic configured to perform operations comprising
selecting a rule set to apply to the vector, to the entity group, or to both, by accessing a stored collection of rule sets;
generating a classification of the vector or the entity group based on at least the rule set; and
providing electronic data corresponding to the classification.

### Claim 12 (depends on 1)

The method of claim 1, wherein the predefined entity analytic calculates a feature using logic, the logic including a count, a sum, a standard deviation, a distinct, an external query, a complex logic script, a time-series analytic, a time-window analytic, or a source document analytic.

### Claim 13

A system comprising:
at least one data processor; and
memory storing instructions configured to cause the at least one data processor to perform operations comprising:
receiving an entity group including associations of entities grouped according to a measure of similarity, the entities including units of data extracted from a set of documents;
assembling a vector, the assembling comprising evaluating a predefined entity analytic using the received entity group; and
providing the vector to a second analytic.

### Claim 14 (depends on 13)

The system of claim 13, the operations further comprising evaluating the second analytic using the vector as an input to the second analytic to form an output.

### Claim 15 (depends on 13)

The system of claim 13, wherein the vector includes features, the features including a set of values generated by the evaluation of the predefined entity analytic using the entity group.

### Claim 16 (depends on 13)

The system of claim 13, wherein evaluating the predefined entity analytic includes executing the predefined entity analytic with the received entity group to compute a vector value.

### Claim 17 (depends on 13)

The system of claim 13, wherein the predefined entity analytic includes a count, a sum, a standard deviation, a distinct, an external query, a complex logic script, a time-series analytic, a time-window analytic, or a source document analytic.

### Claim 18 (depends on 13)

The system of claim 13, wherein the second analytic includes:
a predictive analytic configured to generate electronic data corresponding to a predictive output;
a decision analytic configured to provide electronic data corresponding to a decision generated by applying one or more rules to the vector; or
a descriptive analytic configured to perform operations comprising
selecting a rule set to apply to the vector, to the entity group, or to both, by accessing a stored collection of rule sets;
generating a classification of the vector or the entity group based on at least the rule set; and
providing electronic data corresponding to the classification.

### Claim 19 (depends on 13)

The system of claim 13, the operations further comprising extracting an entity from a document.

### Claim 20

A non-transitory computer program product storing instructions which, when executed by at least one data processor forming part of at least one computing system, cause the at least one data processor to implement operations comprising:
receiving an entity group including associations of entities grouped according to a measure of similarity, the entities including units of data extracted from a set of documents;
assembling a vector, the assembling comprising evaluating a predefined entity analytic using the received entity group; and
providing the vector to a second analytic.