# US 10,769,190 B2 · Group analysis using content data

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- Number: US 10,769,190 B2
- Document ID: US10769190B2
- Status: Granted
- Kind: B2
- Application number: 15/518,756
- Filed: 2015-01-23
- Published: 2017-08-17
- Granted: 2020-09-08
- Earliest claimed: 2015-01-23
- Assignees: Hewlett-Packard Development Company, L.P.
- Applicants: Hewlett-Packard Development Company, L.P.
- Inventors: Lei Liu; Georgia Koutrika; Jerry Liu
- Web page: https://patentgrep.com/patent/US10769190B2

## CPC

- G06F 16/337: {Profile generation, learning or modification}
- G06Q 10/0631: Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q 10/105: Human resources
- G06Q 50/20: Education
- 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

Examples relate to grouping students using content fields. Student data including a plurality of content fields is obtained. Each content field of the plurality of content fields includes a value that represents an unstructured marking linked to a content data collection. Student profiles are generated by assigning a student identification number to each of the plurality of content fields. Each of the student identification numbers are organized into at least one student group by analyzing the set of student profiles.

## Claims

### Claim 1

A system for grouping students comprising:
a pre-processing engine implemented by a processor to receive a set of student data for a plurality of students, the set of student data including a set of values for a plurality of content fields, wherein each of the content fields corresponds to a respective location of a text passage of a content data collection document, and the set of values represent a plurality of unstructured markings associated with the content data collection;
a profile engine implemented by the processor to generate a student profile by associating a student identification number with the plurality of content fields for the set of student data received; and
an analysis engine implemented by the processor to organize the student identification numbers based on unstructured markings by different students to a same text passage indicated by the plurality of content fields.

### Claim 2 (depends on 1)

The system of claim 1, wherein the pre-processing engine defines the plurality of content fields from the content data collection by:
dividing the content data collection into the plurality of content fields, and
assigning a set of values to the plurality of content fields, the set of values to represent the plurality of unstructured markings.

### Claim 3 (depends on 1)

The system of claim 1, wherein the plurality of unstructured markings comprise at least one marking selected from a highlight marking, an underline, and an annotation to the content data collection readable by the pre-processing engine.

### Claim 4 (depends on 1)

The system of claim 1, wherein the pre-processing engine scans the data set to obtain the plurality of unstructured markings.

### Claim 5

A non-transitory computer-readable storage medium encoded with instructions that, when executed by a processor, perform a method, the method comprising:
extracting a plurality of content fields from a set of student data collected from a plurality of students, wherein each content field of the plurality of content fields corresponds to a respective location of a text passage of a content data collection document and is assigned a value that represents an unstructured marking associated with the content data collection;
associating the plurality of content fields with a set of student identification numbers to generate a plurality of student profiles; and
clustering the set of student identification numbers based on an analysis of the plurality of content fields indicating unstructured markings by different students to a same text passage.

### Claim 6 (depends on 5)

The non-transitory computer-readable storage medium of claim 5, wherein the unstructured marking comprises at least one marking selected from a highlight marking, an underline, and an annotation to the content data collection.

### Claim 7 (depends on 5)

The non-transitory computer-readable storage medium of claim 5, wherein the analysis of the plurality of content fields includes grouping the set of student identification numbers based on common features.

### Claim 8

A method to group students comprising:
obtaining a set of student data including a plurality of content fields, wherein the plurality of content fields are associated with each student of a plurality of students, and wherein each content field of the plurality of content fields corresponds to a respective location of a text passage of a content data collection document and includes a value that represents an unstructured marking linked to the content data collection;
generating a set of student profiles by assigning a student identification number to each of the plurality of content fields; and
organizing each of the student identification numbers into at least one student group by analyzing the set of student profiles and based on unstructured markings by different students to a same text passage indicated by the plurality of content fields.

### Claim 9 (depends on 8)

The method of claim 8, further comprising determining the values associated with the plurality of content fields for each student from the set of student data to build the set of student profiles.

### Claim 10 (depends on 8)

The method of claim 8, further comprising defining a plurality of content fields in the content data collection and determining a set of values to represent the plurality of content fields.

### Claim 11 (depends on 10)

The method of claim 10, wherein the plurality of content fields are defined based on an analysis of a plurality of unstructured markings on the content data collection.

### Claim 12 (depends on 8)

The method of claim 8, wherein generating the set of student profiles comprises generating a student feature matrix with rows representing each student and columns corresponding to the plurality of content fields for each student.

### Claim 13 (depends on 8)

The method of claim 8, wherein the set of student profiles are analyzed using a clustering method that evaluates the plurality of content fields.

### Claim 14 (depends on 13)

The method of claim 13, wherein the clustering method comprises at least one of the following types of clustering selected from a K-means, a modularity clustering, and a spectral clustering.

### Claim 15 (depends on 8)

The method of claim 8, further comprising defining each content field by assigning a value to represent the unstructured marking linked with a content data collection.