# US 2025/0094508 A1 · Digital component provision based on contextual feature driven audience interest profiles

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2025/0094508 A1
- Document ID: US20250094508A1
- Status: Published application
- Kind: A1
- Application number: 18/577,448
- Filed: 2023-01-18
- Published: 2025-03-20
- Earliest claimed: 2023-01-18
- Assignees: Google Inc.
- Applicants: Google LLC
- Inventors: Wei Huang; Zhenyu Liu; Liang Wang; Kumar Rishabh
- Web page: https://patentgrep.com/patent/US20250094508A1

## CPC

- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06F 16/9536: Search customisation based on social or collaborative filtering
- G06F 16/9537: Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
- G06Q 30/0201: Market modelling; Market analysis; Collecting market data
- G06Q 30/0271: {Personalized advertisement}
- 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

Methods, systems, and media comprising; obtaining, from a client device and during a browsing session conducted by a user, contextual features relating to context within the browsing session, wherein the contextual features do not include any personally-identifiable data; generating, using a trained contextual model and based on the contextual features, an audience interest profile, wherein the audience interest profile represents a prediction of affinity to one or more content categories, wherein the trained contextual model is trained using a set of historical contextual data aggregated from a plurality of prior browsing sessions and audience interest profiles that each represent an affinity to one or more content categories, and wherein the set of historical contextual data does not include any personally-identifiable data; identifying, based on the generated audience interest profile, a digital component for provision; and providing, for display on the client device and during the browsing session, the digital component.

## Claims

### Claim 1

A computer-implemented method comprising:
obtaining, from a client device and during a browsing session conducted by a user of the client device, a plurality of contextual features relating to context within which the browsing session is conducted, wherein the plurality of contextual features do not include any personally-identifiable data of the user;
generating, using a trained contextual model and based on the plurality of contextual features, an audience interest profile applicable to the user of the client device,
wherein the trained contextual model is trained using training data including a set of historical contextual data aggregated from a plurality of prior browsing sessions and a corresponding set of labels indicating audience interest profiles that each represent an affinity of a particular audience interest segment to one or more content categories, and
wherein the set of historical contextual data does not include any personally-identifiable data of users from the plurality of prior browsing sessions;
identifying, based on the generated audience interest profile, a digital component for provision on the client device; and
providing, for display within a page displayed on the client device and during the browsing session, the digital component.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, further comprising:
training a plurality of models using a set of training data including sets of contextual features and label data including audience interest profiles corresponding to the sets of contextual features;
determining that a performance of at least one of the models meets a set of evaluation criteria;
in response to determining that the performance of the model meets the set of evaluation criteria, deploying one of the plurality of models as the trained model.

### Claim 3 (depends on 2)

The computer-implemented method of claim 2, wherein determining that a performance of the model meets a set of evaluation criteria further comprises applying one or more filters with a minimum preset relevance value, wherein the preset relevance value is numerical value based on a divergence between a predetermined mapping of two content categories.

### Claim 4 (depends on 3)

The computer-implemented method of claim 3, further comprising:
receiving, from a third party, a new content category and minimum relevance value;
creating a new content category mapping based on the new content category; and
establishing a new filter based on the new content category and minimum relevance value.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, further comprising:
in response to providing the digital component for display on the client device;
obtaining, from the client device during the browsing session conducted by a user of the client device, subsequent contextual features related to the provided digital component; and
modifying, using data relating to the subsequent contextual features, the audience interest profile of the user.

### Claim 6 (depends on 5)

The computer-implemented method of claim 5, wherein modifying the audience interest profile of the user is conducted in real-time during the browsing session.

### Claim 7 (depends on 5)

The computer-implemented method of claim 5, wherein the data relating to the subsequent contextual features is discarded after the termination of the browsing session on the client device.

### Claim 8

A system comprising:
one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
obtaining, from a client device and during a browsing session conducted by a user of the client device, a plurality of contextual features relating to context within which the browsing session is conducted, wherein the plurality of contextual features do not include any personally-identifiable data of the user;
generating, using a trained contextual model and based on the plurality of contextual features, an audience interest profile applicable to the user of the client device, wherein the audience interest profile,
wherein the trained contextual model is trained using training data including a set of historical contextual data aggregated from a plurality of prior browsing sessions and a corresponding set of labels indicating audience interest profiles that each represent an affinity of a particular audience interest segment to one or more content categories, and
wherein the set of historical contextual data does not include any personally-identifiable data of users from the plurality of prior browsing sessions;
identifying, based on the generated audience interest profile, a digital component for provision on the client device; and
providing, for display within a page displayed on the client device and during the browsing session, the digital component.

### Claim 9 (depends on 8)

The system of claim 8, further comprising:
training a plurality of models using a set of training data including sets of contextual features and label data including audience interest profiles corresponding to the sets of contextual features;
determining that a performance of at least one of the models meets a set of evaluation criteria;
in response to determining that the performance of the model meets the set of evaluation criteria, deploying one of the plurality of models as the trained model.

### Claim 10 (depends on 9)

The system of claim 9, wherein determining that a performance of the model meets a set of evaluation criteria further comprises applying one or more filters with a minimum preset relevance value, wherein the preset relevance value is numerical value based on a divergence between a predetermined mapping of two content categories.

### Claim 11 (depends on 10)

The system of claim 10, further comprising:
receiving, from a third party, a new content category and minimum relevance value;
creating a new content category mapping based on the new content category; and
establishing a new filter based on the new content category and minimum relevance value.

### Claim 12 (depends on 8)

The system of claim 8, further comprising:
in response to providing the digital component for display on the client device;
obtaining, from the client device during the browsing session conducted by a user of the client device, subsequent contextual features related to the provided digital component; and
modifying, using data relating to the subsequent contextual features, the audience interest profile of the user.

### Claim 13 (depends on 12)

The system of claim 12, wherein modifying the audience interest profile of the user is conducted in real-time during the browsing session.

### Claim 14 (depends on 12)

The system of claim 12, wherein the data relating to the subsequent contextual features is discarded after the termination of the browsing session on the client device.

### Claim 15

One or more non-transitory computer storage media encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:
obtaining, from a client device and during a browsing session conducted by a user of the client device, a plurality of contextual features relating to context within which the browsing session is conducted, wherein the plurality of contextual features do not include any personally-identifiable data of the user;
generating, using a trained contextual model and based on the plurality of contextual features, an audience interest profile applicable to the user of the client device, wherein the audience interest profile,
wherein the trained contextual model is trained using training data including a set of historical contextual data aggregated from a plurality of prior browsing sessions and a corresponding set of labels indicating audience interest profiles that each represent an affinity of a particular audience interest segment to one or more content categories, and
wherein the set of historical contextual data does not include any personally-identifiable data of users from the plurality of prior browsing sessions;
identifying, based on the generated audience interest profile, a digital component for provision on the client device; and
providing, for display within a page displayed on the client device and during the browsing session, the digital component.

### Claim 16 (depends on 15)

The media of claim 15, further comprising:
training a plurality of models using a set of training data including sets of contextual features and label data including audience interest profiles corresponding to the sets of contextual features;
determining that a performance of at least one of the models meets a set of evaluation criteria;
in response to determining that the performance of the model meets the set of evaluation criteria, deploying one of the plurality of models as the trained model.

### Claim 17 (depends on 16)

The media of claim 16, wherein determining that a performance of the model meets a set of evaluation criteria further comprises applying one or more filters with a minimum preset relevance value, wherein the preset relevance value is numerical value based on a divergence between a predetermined mapping of two content categories.

### Claim 18 (depends on 17)

The media of claim 17, further comprising:
receiving, from a third party, a new content category and minimum relevance value;
creating a new content category mapping based on the new content category; and
establishing a new filter based on the new content category and minimum relevance value.

### Claim 19 (depends on 15)

The media of claim 15, further comprising:
in response to providing the digital component for display on the client device;
obtaining, from the client device during the browsing session conducted by a user of the client device, subsequent contextual features related to the provided digital component; and
modifying, using data relating to the subsequent contextual features, the audience interest profile of the user.

### Claim 20 (depends on 19)

The media of claim 19, wherein modifying the audience interest profile of the user is conducted in real-time during the browsing session.