# US 2026/0099720 A1 · Training a content authenticity validator as a variable resolution game

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- Number: US 2026/0099720 A1
- Document ID: US20260099720A1
- Status: Published application
- Kind: A1
- Application number: 18/926,575
- Filed: 2024-10-25
- Published: 2026-04-09
- Earliest claimed: 2022-03-31
- Assignees: D5Ai LLC
- Applicants: D5Ai LLC
- Inventors: James K. BAKER
- Web page: https://patentgrep.com/patent/US20260099720A1

## CPC

- G06N 3/094: Adversarial learning
- A63F 13/67: adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use
- G06N 3/045: Combinations of networks
- G06N 3/0475: Generative networks
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- A63F: CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR

## Abstract

Computer-implemented systems and method train a generator and a discriminator, through machine learning, where the generator and discriminator are trained in an adversarial relationship using a simulated, multi-player game. The model parameters for the generator and the discriminator can be updated non-simultaneously. Also, the simulated, multi-player game may comprise a two-person, zero-sum game.

## Claims

### Claim 1

A method of building a content authenticity validator, the method comprising:
training, by a programmed computer system, through machine learning, a generator and a discriminator together in a multi-player, multi-round simulated game, where in each round of the multi-player, multi-round simulated game, the discriminator is trained to determine whether a selected data item, presented to the discriminator, is from the generator or from a data source that is different from the generator, wherein the training comprises:
training, by the programmed computer system, the discriminator to perform first and second tasks, wherein:
the first task is whether the selected data item is from the generator or from the data source; and
the second task is whether the selected data item is authentic;
training, by the programmed computer system, the generator to generate data that the discriminator incorrectly determines are not from the generator; and
updating, iteratively and non-simultaneously, by the programmed computer system, model parameters for the generator and for the discriminator; and
after training the generator and discriminator, deploying, by the programmed computer system, the discriminator to determine whether a content sample presented to the discriminator is authentic.

### Claim 2 (depends on 1)

The method of claim 1, wherein:
the multi-player, multi-round simulated game comprises at least a round A and a round B, where round B is after round A;
the discriminator employs a discriminator mixed strategy in the multi-player, multi-round simulated game;
the generator employs a generator mixed strategy in the multi-player, multi-round simulated game; and
the training comprises:
updating the discriminator mixed strategy in round A; and
updating the generator mixed strategy in round B.

### Claim 3 (depends on 1)

The method of claim 1, wherein the content sample comprises a text sample.

### Claim 4 (depends on 1)

The method of claim 1, wherein the content sample comprises an audio sample.

### Claim 5 (depends on 1)

The method of claim 1, wherein the content sample comprises a video sample.

### Claim 6 (depends on 1)

The method of claim 1, wherein the content sample comprises an image.

### Claim 7 (depends on 1)

The method of claim 1, wherein the multi-player, multi-round, simulated game comprises a two-person, zero-sum game.

### Claim 8 (depends on 7)

The method of claim 7, wherein the two-person, zero-sum game comprises a two-person, finite zero-sum game.

### Claim 9 (depends on 2)

The method of claim 2, wherein:
updating the discriminator mixed strategy in round A comprises updating a current mixed strategy for the discriminator based on payoffs from rounds of the simulated game prior to round A; and
updating the generator mixed strategy in round B comprises updating a current mixed strategy for the generator based on payoffs from rounds of the simulated game prior to round B.

### Claim 10 (depends on 9)

The method of claim 9, wherein:
updating the discriminator mixed strategy in round A comprises finding a pure strategy for the discriminator that performs better against a then-current generator mixed strategy than does the current mixed strategy of the discriminator; and
updating the generator mixed strategy in round B comprises finding a pure strategy for the generator that performs better against a then-current discriminator mixed strategy than does the current mixed strategy of the generator.

### Claim 11 (depends on 2)

The method of claim 2, wherein:
the discriminator comprises a plurality of local region detectors;
each of the plurality of local region detectors is trained, through machine learning, to discriminate whether a presented data item to the local region detector is accepted or rejected as being a member of a specified set associated with the local region detector; and
updating the discriminator mixed strategy in round A comprises selecting a selected local region detector of the plurality of local region detectors, such that a first data item from the data source is based on the selected local region detector.

### Claim 12 (depends on 11)

The method of claim 11, wherein:
the plurality of local region detectors comprises at least a first local region detector and a second local region detector; and
a specified set for the first local region detector overlaps in part with a specified set for the second local region detector.

### Claim 13 (depends on 12)

The method of claim 12, wherein:
the generator comprises a plurality of local generators;
each of the plurality of local generators is trained, through machine learning, to generate data items that are in a local data region associated with the local generator; and
updating the generator mixed strategy in round B comprises selecting a selected local generator of the plurality of local generators, such that data item generated by the generator in round B is generated by the selected local generator.

### Claim 14 (depends on 13)

The method of claim 13, wherein:
the discriminator comprises a plurality of local discriminators; and
each of the plurality of local discriminators is trained to determine whether a data item presented to the local discriminator is from the generator.

### Claim 15 (depends on 14)

The method of claim 14, wherein:
each of the plurality of local generators comprises a neural network;
each of the plurality of local region detectors comprises a neural network; and
each of the plurality of local discriminators comprises a neural network.

### Claim 16 (depends on 15)

The method of claim 15, further comprising, prior round A:
training, with the programmed computer system, through machine learning, the plurality of local generators;
training, with the programmed computer system, through machine learning, the plurality of local region detectors; and
training, with the programmed computer system, through machine learning, the plurality of local discriminators.

### Claim 17 (depends on 1)

The method of claim 1, wherein the data source comprises a cooperative generator that is trained to be cooperative with the discriminator.

### Claim 18 (depends on 1)

The method of claim 1, wherein:
the programmed computer system comprises a plurality of graphical processing units (GPUs); and
training the generator and discriminator together comprises processing training data in parallel with the plurality of GPUs.

### Claim 19

A computer system for building a content authenticity validator, the computer system comprising:
one or more processing units; and
computer memory in communication with the one or more processing units, wherein the computer memory stores instructions that when executed by the one or more processing units cause the one or more processing units to:
train, through machine learning, a generator and a discriminator together in a multi-player, multi-round simulated game, where in each round of the multi-player, multi-round simulated game, the discriminator is trained to determine whether a selected data item, presented to the discriminator, is from the generator or from a data source that is different from the generator, wherein the computer memory stores instructions that cause the one or more processing units to train the generator and discriminator together by, in part:
training the discriminator to perform first and second tasks, wherein:
the first task is whether the selected data item is from the generator or from the data source; and
the second task is whether the selected data item is authentic;
training the generator to generate data that the discriminator incorrectly determines are not from the generator; and
updating, iteratively and non-simultaneously, model parameters for the generator and for the discriminator; and
after training the generator and discriminator, deploy the discriminator to determine whether a content sample presented to the discriminator is authentic.

### Claim 20 (depends on 19)

The computer system of claim 19, wherein:
the multi-player, multi-round simulated game comprises at least a round A and a round B, where round B is after round A;
the discriminator employs a discriminator mixed strategy in the multi-player, multi-round simulated game;
the generator employs a generator mixed strategy in the multi-player, multi-round simulated game; and
the computer memory stores instructions that cause the one or more processing units to train the generator and the discriminator by, in part:
updating the discriminator mixed strategy in round A; and
updating the generator mixed strategy in round B.

### Claim 21 (depends on 19)

The computer system of claim 19, wherein the content sample comprises a text sample.

### Claim 22 (depends on 19)

The computer system of claim 19, wherein the content sample comprises an audio sample.

### Claim 23 (depends on 19)

The computer system of claim 19, wherein the content sample comprises a video sample.

### Claim 24 (depends on 19)

The computer system of claim 19, wherein the content sample comprises an image.

### Claim 25 (depends on 19)

The computer system of claim 19, wherein the multi-player, multi-round, simulated game comprises a two-person, zero-sum game.

### Claim 26 (depends on 25)

The computer system of claim 25, wherein the two-person, zero-sum game comprises a two-person, finite zero-sum game.

### Claim 27 (depends on 20)

The computer system of claim 20, wherein the computer memory stores instructions that cause the one or more processing units to:
update the discriminator mixed strategy in round A by, in part, updating a current mixed strategy for the discriminator based on payoffs from rounds of the simulated game prior to round A; and
update the generator mixed strategy in round B by, in part, updating a current mixed strategy for the generator based on payoffs from rounds of the simulated game prior to round B.

### Claim 28 (depends on 27)

The computer system of claim 27, wherein the computer memory stores instructions that cause the one or more processing units to:
update the discriminator mixed strategy in round A by, in part, finding a pure strategy for the discriminator that performs better against a then-current generator mixed strategy than does the current mixed strategy of the discriminator; and
update the generator mixed strategy in round B by, in part, finding a pure strategy for the generator that performs better against a then-current discriminator mixed strategy than does the current mixed strategy of the generator.

### Claim 29 (depends on 20)

The computer system of claim 20, wherein:
the discriminator comprises a plurality of local region detectors;
each of the plurality of local region detectors is trained, through machine learning, to discriminate whether a presented data item to the local region detector is accepted or rejected as being a member of a specified set associated with the local region detector; and
the computer memory stores instructions that cause the one or more processing units to update the discriminator mixed strategy in round A by, in part, selecting a selected local region detector of the plurality of local region detectors, such that a first data item from the data source is based on the selected local region detector.

### Claim 30 (depends on 29)

The computer system of claim 29, wherein:
the plurality of local region detectors comprises at least a first local region detector and a second local region detector; and
a specified set for the first local region detector overlaps in part with a specified set for the second local region detector.

### Claim 31 (depends on 30)

The computer system of claim 30, wherein:
the generator comprises a plurality of local generators;
each of the plurality of local generators is trained, through machine learning, to generate data items that are in a local data region associated with the local generator; and
the computer memory stores instructions that cause the one or more processing units to update the generator mixed strategy in round B by, in part, selecting a selected local generator of the plurality of local generators, such that data item generated by the generator in round B is generated by the selected local generator.

### Claim 32 (depends on 31)

The computer system of claim 31, wherein:
the discriminator comprises a plurality of local discriminators; and
each of the plurality of local discriminators is trained to determine whether a data item presented to the local discriminator is from the generator.

### Claim 33 (depends on 32)

The computer system of claim 32, wherein:
each of the plurality of local generators comprises a neural network;
each of the plurality of local region detectors comprises a neural network; and
each of the plurality of local discriminators comprises a neural network.

### Claim 34 (depends on 33)

The computer system of claim 33, wherein the computer memory stores instructions that cause the one or more processing units to, prior round A:
train, through machine learning, the plurality of local generators;
train, through machine learning, the plurality of local region detectors; and
train, through machine learning, the plurality of local discriminators.

### Claim 35 (depends on 19)

The computer system of claim 19, wherein the data source comprises a cooperative generator that is trained to be cooperative with the discriminator.

### Claim 36 (depends on 19)

The computer system of claim 19, wherein the one or more processing units comprises plurality of graphical processing units that process in parallel training data for the training of the generator and discriminator.