IP Library › Granted Patent US 12,387,493
Granted Patent B2
US 12,387,493 · App. 17/808,941 · Granted Aug 12, 2025

Machine learning for recognizing and interpreting embedded information card content

Inventors: Mihailo Stojancic (San Jose, CA); Warren Packard (Palo Alto, CA)
Assignee: STATS LLC
G06V20/42G06F16/908G06T7/12G06V20/41G06V20/46G06V20/48G06V30/153G06V40/20H04N21/435H04N21/44008H04N21/458H04N21/8133H04N21/84G06T2207/20132G06V20/44
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Quick Facts
Patent No.
US 12,387,493
App. No.
17/808,941
Granted
Aug 12, 2025
Kind
B2
Abstract

Metadata for highlights of a video stream is extracted from card images embedded in the video stream. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. Card images embedded in video frames of the video stream are identified and processed to extract text. The text characters may be recognized by applying a machine-learned model trained with a set of characters extracted from card images embedded in sports television programming contents. The training set of character vectors may be pre-processed to maximize metric distance between the training set members. The text may be interpreted to obtain the metadata. The metadata may be stored in association with the portion of the video stream. The metadata may provide information regarding the highlights, and may be presented concurrently with playback of the highlights.

Claims (42)

1. A method for generating a classification model for extracting metadata from a video stream, the method comprising: receiving, by a computing system, at least a portion of a video stream; identifying, by the computing system, one or more card images embedded in one or more video frames of the portion of the video stream; processing, by the computing system, the one or more card images to extract a plurality of character images, each character image comprising a character; generating, by the computing system, a training set of character vectors in a vector space based on the extracted plurality of character images, wherein generating the training set of the character vectors in the vector space based on the extracted plurality of character images includes: formatting, by the computing system, a set of n pixels from the plurality of character images into n-dimensional vectors; reducing, by the computing system, dimensionality of the vector space by selecting a subset of the character vectors in the training set; and generating, by the computing system, a classification model to extract metadata from video streams by training the classification model using at least one of the subset of the character vectors.

2. The method of claim 1 , further comprising:

normalizing, by the computing system, the plurality of character images to a standard size and/or a standard illumination prior to generating the training set of the character vectors.

3. The method of claim 1 , wherein reducing the dimensionality of the vector space by selecting the subset of the character vectors further comprises:

performing, by the computing system, a principal component analysis on the training set of the character vectors to determine character vectors that are orthogonal basis vectors; and

selecting, by the computing system, the orthogonal basis vectors as the subset of the character vectors.

4. The method of claim 1 , wherein reducing the dimensionality of the vector space by selecting the subset of the character vectors further comprises:

performing, by the computing system, a principal component analysis on the training set of the character vectors to determine character vectors that are orthogonal basis vectors; and

selecting, by the computing system, a limited number of the orthogonal basis vectors as the subset of the character vectors.

5. The method of claim 4 , wherein selecting the limited number of the orthogonal basis vectors further comprises:

selecting, by the computing system, orthogonal basis vectors that correspond to a largest set of singular values derived from a matrix of the orthogonal basis vectors.

6. The method of claim 1 , wherein the video stream is of a sporting event, and wherein the one or more card images include at least one of:

a current time within the sporting event;

a current phase of the sporting event;

a game clock of the sporting event; or

a game score of the sporting event.

7. The method of claim 1 , wherein the classification model is at least one of a support vector model or a convolutional neural network.

8. A system for generating a classification model for extracting metadata from a video stream, the system comprising: a non-transitory storage medium storing computer program instructions; and at least one processor configured to execute the computer program instructions to perform operations comprising: receiving at least a portion of a video stream; identifying one or more card images embedded in one or more video frames of the portion of the video stream; processing the one or more card images to extract a plurality of character images, each character image comprising a character; generating a training set of character vectors in a vector space based on the extracted plurality of character images, wherein generating the training set of the character vectors in the vector space based on the extracted plurality of character images includes: formatting, by the computing system, a set of n pixels from the plurality of character images into n-dimensional vectors; reducing dimensionality of the vector space by selecting a subset of the character vectors in the training set; and generating a classification model to extract metadata from video streams by training the classification model using at least one of the subset of the character vectors.

9. The system of claim 8 , wherein the operations further comprise:

normalizing the plurality of character images to a standard size and/or a standard illumination prior to generating the training set of the character vectors.

10. The system of claim 8 , wherein reducing the dimensionality of the vector space by selecting the subset of the character vectors further comprises:

performing a principal component analysis on the training set of the character vectors to determine character vectors that are orthogonal basis vectors; and

selecting the orthogonal basis vectors as the subset of the character vectors.

11. The system of claim 8 , wherein reducing the dimensionality of the vector space by selecting the subset of the character vectors further comprises:

performing a principal component analysis on the training set of the character vectors to determine character vectors that are orthogonal basis vectors; and

selecting a limited number of the orthogonal basis vectors as the subset of the character vectors.

12. The system of claim 11 , wherein selecting the limited number of the orthogonal basis vectors further comprises:

selecting orthogonal basis vectors that correspond to a largest set of singular values derived from a matrix of the orthogonal basis vectors.

13. The system of claim 8 , wherein the video stream is of a sporting event, and wherein the one or more card images include at least one of:

a current time within the sporting event;

a current phase of the sporting event;

a game clock of the sporting event; or

a game score of the sporting event.

14. A non-transitory storage medium storing computer program instructions that when executed by at least one processor perform operations comprising: receiving at least a portion of a video stream; identifying one or more card images embedded in one or more video frames of the portion of the video stream; processing the one or more card images to extract a plurality of character images, each character image comprising a character; generating a training set of character vectors in a vector space based on the extracted plurality of character images, wherein generating the training set of the character vectors in the vector space based on the extracted plurality of character images includes: formatting, by the computing system, a set of n pixels from the plurality of character images into n-dimensional vectors; reducing dimensionality of the vector space by selecting a subset of the character vectors in the training set; and generating a classification model to extract metadata from video streams by training the classification model using at least one of the subset of the character vectors.

15. The non-transitory storage medium of claim 14 , wherein the operations further comprise:

normalizing the plurality of character images to a standard size and/or a standard illumination prior to generating the training set of the character vectors.

16. The non-transitory storage medium of claim 14 , wherein reducing the dimensionality of the vector space by selecting the subset of the character vectors further comprises:

performing a principal component analysis on the training set of the character vectors to determine character vectors that are orthogonal basis vectors; and

selecting the orthogonal basis vectors as the subset of the character vectors.

17. The non-transitory storage medium of claim 14 , wherein reducing the dimensionality of the vector space by selecting the subset of the character vectors further comprises:

performing a principal component analysis on the training set of the character vectors to determine character vectors that are orthogonal basis vectors; and

selecting a limited number of the orthogonal basis vectors as the subset of the character vectors.

Assignments (3)
SECURITY INTEREST Recorded Apr 14, 2026
From: STATS LLC
To: MORGAN STANLEY SENIOR FUNDING, INC., AS COLLATERAL AGENT
Reel/Frame 075390/0491 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 11, 2023
From: THUUZ, INC.
To: STATS LLC
Reel/Frame 062347/0858 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 11, 2023
From: STOJANCIC, MIHAILO; PACKARD, WARREN
To: THUUZ, INC.
Reel/Frame 062355/0633 →
Continuity (8)
Division 16411710 · May 14, 2019
Provisional Application 62746454 · Oct 16, 2018
Provisional Application 62712041 · Jul 30, 2018
Provisional Application 62680955 · Jun 5, 2018
Provisional Application 62673411 · May 18, 2018
Provisional Application 62673413 · May 18, 2018
Provisional Application 62673412 · May 18, 2018
Related Publication 20220327829A1 · Oct 13, 2022
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