# US 12,179,103 B2 · Systems and methods for analyzing video data of predictive movements

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- Number: US 12,179,103 B2
- Document ID: US12179103B2
- Status: Granted
- Kind: B2
- Application number: 17/700,698
- Filed: 2022-03-22
- Published: 2023-09-28
- Granted: 2024-12-31
- Earliest claimed: 2022-03-22
- Assignees: Adeia Guides Inc.
- Applicants: Adeia Guides Inc.
- Inventors: Serhad Doken
- Web page: https://patentgrep.com/patent/US12179103B2

## CPC

- A63F 13/52: involving aspects of the displayed game scene
- A63F 13/213: comprising photodetecting means, e.g. cameras, photodiodes or infrared cells (A63F13/219 takes precedence)
- A63F 13/428: involving motion or position input signals, e.g. signals representing the rotation of an input controller or a player's arm motions sensed by accelerometers or gyroscopes
- A63F 13/46: Computing the game score
- A63F 13/65: automatically by game devices or servers from real world data, e.g. measurement in live racing competition
- A63F 13/812: Ball games, e.g. soccer or baseball
- G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments
- G06V 10/74: Image or video pattern matching; Proximity measures in feature spaces
- G06V 20/40: in video content (extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44)
- G06V 20/48: {Matching video sequences}
- G06V 40/23: {Recognition of whole body movements, e.g. for sport training}
- G06F 2218/12: Classification; Matching
- G06T 2207/10016: Video; Image sequence
- G06T 2207/30196: Human being; Person
- A63F: CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Systems and methods for analyzing predictive movements are disclosed herein. In an embodiment, a system identifies an entity in a video feed of an event and generates for display a prompt for a user to perform a predictive movement to predict an action of the entity in the video feed. The system uses computer vision techniques to analyze the action taken by the entity in the video feed and the predictive movement performed by the user prior to display of the action on the user's display device. If the system determines that the predictive movement matches the action taken by the entity, the system identifies the predictive movement as a correct prediction.

## Claims

### Claim 1

A method comprising:
identifying, in a video feed being transmitted to a display device, an entity;
generating, for display on the display device, an invitation to perform a predictive movement, the predictive movement comprising one or more movements that a user proximate to the display device predicts the entity will perform as part of an action;
detecting performance of the predictive movement by the user prior to the display device displaying performance of the action by the entity; and
determining that the predictive movement performed by the user matches the action performed by the entity and, in response, storing data indicating a correct prediction by the user.

### Claim 2 (depends on 1)

The method of claim 1, wherein the entity is a player in a sports broadcast, and wherein determining that the predictive movement performed by the user matches the action performed by the entity comprises:
using a first machine learning system, detecting a plurality of motions of a plurality of body parts of the player in the sports broadcast during the performance of the action;
using a second machine learning system, detecting a plurality of motions of a plurality of body parts of the user during the predictive movement; and
comparing the plurality of motions of the plurality of body parts of the player with the plurality of motions of the plurality of body parts of the user to determine that the predictive movement matches the action.

### Claim 3 (depends on 1)

The method of claim 1, wherein the entity is a computer-generated character in a video game that is being played at a location separate from the display device, and wherein determining that the predictive movement performed by the user matches the action performed by the entity comprises:
identifying a first plurality of inputs received to control the character in the video game to cause the performance of the action that is transmitted to and displayed at the display device;
identifying a second plurality of inputs received from the user to cause the performance of the predictive movement; and
comparing the first plurality of inputs with the second plurality of inputs to determine that the predictive movement matches the action.

### Claim 4 (depends on 3)

The method of claim 3, wherein identifying the first plurality of inputs received to control the character in the video game to cause the performance of the action comprises:
analyzing video data of the video game to identify particular graphical effects;
identifying one or more abilities of the entity that correspond to the particular graphical effects; and
identifying the first plurality of inputs as inputs that cause performance of the one or more abilities of the entity.

### Claim 5 (depends on 1)

The method of claim 1, further comprising generating, for display on the display device, a scoreboard identifying one or more other users and a score indicative of a number of correct predictions for the one or more other users.

### Claim 6 (depends on 1)

The method of claim 1, further comprising generating, for display on the display device, a highlight comprising a video of a particular user performing a particular predictive movement.

### Claim 7 (depends on 6)

The method of claim 6, wherein the generating, for display on the display device, the highlight is performed in response to determining that the particular user's particular predictive movement comprises a correct prediction.

### Claim 8 (depends on 1)

The method of claim 1, wherein the determining that the predictive movement performed by the user matches the action performed by the entity comprises:
determining a level of similarity between the predictive movement and the action performed by the entity; and
determining that the level of similarity is greater than a stored threshold value and, in response, determining that the predictive movement performed by the user matches the action performed by the entity.

### Claim 9 (depends on 8)

The method of claim 8, further comprising generating a score based on the level of similarity between the predictive movement and the action performed by the entity.

### Claim 10 (depends on 1)

The method of claim 1, further comprising:
generating an action table comprising identifiers of a plurality of body movements that make up the action performed by the entity; and
generating a predictive movement table comprising identifiers of a plurality of body movements that make up the predictive movement;
wherein the determining that the predictive movement performed by the user matches the action performed by the entity comprises comparing the action table to the predictive movement table.

### Claim 11

A system comprising:
input/output circuitry configured to receive a video feed that is transmitted to a display device;
control circuitry configured to:
identify, in the video feed, an entity;
generate, for display on the display device, an invitation to perform a predictive movement, the predictive movement comprising one or more movements that a user proximate to the display device predicts the entity will perform as part of an action;
detect performance of the predictive movement by the user prior to the display device displaying performance of the action by the entity; and
determine that the predictive movement performed by the user matches the action performed by the entity and, in response, store data indicating a correct prediction by the user.

### Claim 12 (depends on 11)

The system of claim 11, wherein the entity is a player in a sports broadcast and wherein, when determining that the predictive movement performed by the user matches the action performed by the entity, the control circuitry is further configured to:
using a first machine learning system, detect a plurality of motions of a plurality of body parts of the player in the sports broadcast during the performance of the action;
using a second machine learning system, detect a plurality of motions of a plurality of body parts of the user during the predictive movement; and
compare the plurality of motions of the plurality of body parts of the player with the plurality of motions of the plurality of body parts of the user to determine that the predictive movement matches the action.

### Claim 13 (depends on 11)

The system of claim 11, wherein the entity is a computer-generated character in a video game that is being played at a location separate from the display device and wherein, when determining that the predictive movement performed by the user matches the action performed by the entity, the control circuitry is further configured to:
identify a first plurality of inputs received to control the character in the video game to cause the performance of the action that is transmitted to and displayed at the display device;
identify a second plurality of inputs received from the user to cause the performance of the predictive movement; and
compare the first plurality of inputs with the second plurality of inputs to determine that the predictive movement matches the action.

### Claim 14 (depends on 13)

The system of claim 13, wherein, when identifying the first plurality of inputs received to control the character in the video game to cause the performance of the action, the control circuitry is further configured to:
analyze video data of the video game to identify particular graphical effects;
identify one or more abilities of the entity that correspond to the particular graphical effects; and
identify the first plurality of inputs as inputs that cause performance of the one or more abilities of the entity.

### Claim 15 (depends on 11)

The system of claim 11, wherein the control circuitry is further configured to generate, for display on the display device, a scoreboard identifying one or more other users and a score indicative of a number of correct predictions for the one or more other users.

### Claim 16 (depends on 11)

The system of claim 11, wherein the control circuitry is further configured to generate, for display on the display device, a highlight comprising a video of a particular user performing a particular predictive movement.

### Claim 17 (depends on 16)

The system of claim 16, wherein the control circuitry is further configured to perform the generating, for display on the display device, the highlight in response to determining that the particular user's particular predictive movement comprises a correct prediction.

### Claim 18 (depends on 11)

The system of claim 11, wherein, when determining that the predictive movement performed by the user matches the action performed by the entity, the control circuitry is further configured to:
determine a level of similarity between the predictive movement and the action performed by the entity; and
determine that the level of similarity is greater than a stored threshold value and, in response, determine that the predictive movement performed by the user matches the action performed by the entity.

### Claim 19 (depends on 18)

The system of claim 18, wherein the control circuitry is further configured to generate a score based on the level of similarity between the predictive movement and the action performed by the entity.

### Claim 20 (depends on 11)

The system of claim 11, wherein the control circuitry is further configured to:
generate an action table comprising identifiers of a plurality of body movements that make up the action performed by the entity; and
generate a predictive movement table comprising identifiers of a plurality of body movements that make up the predictive movement;
wherein, when determining that the predictive movement performed by the user matches the action performed by the entity, the control circuitry is further configured to compare the action table to the predictive movement table.