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Sony Patent Proposes AI That Adapts Video Game Boss Fights

Sony’s patent outlines a possible system that learns from player attempts and adjusts computer-controlled opponents. It is a proposal, not confirmation of a released PlayStation feature.
Length3 min Posted Quest giverVGSources Team
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Sony’s patent proposes using machine learning to adjust computer-controlled opponents based on how players fare against them. A boss might counter a tactic a player relies on, or stop using an attack that repeatedly defeats them. The filing describes a possible system—not a confirmed feature in any released PlayStation game.

How the proposed system would adapt a fight

The patent describes a feedback loop: a game gathers information about players’ attempts against an opponent, analyzes which tactics succeed or fail, and may use that analysis to change the opponent’s behavior in a later gameplay instance. A boss is one example; the filing also discusses opponents such as rival drivers, teams, or groups of enemies.

  1. Record attempts: The system collects data about one player’s or multiple players’ encounters, including their tactics and outcomes.
  2. Analyze what happened: A machine-learning model identifies patterns in successful and unsuccessful approaches.
  3. Adjust a later encounter: The game may alter the opponent’s behavior or the difficulty of beating it, drawing on the analyzed data.

Sony discusses reinforcement learning, but the patent also allows supervised and unsupervised methods. It does not specify one required AI architecture.

What could change during a boss fight?

The examples focus on how an opponent responds, rather than simply changing a conventional difficulty setting. If players repeatedly land a particular punch, a boss might move faster to avoid it. If players rely on a certain weapon, the boss might gain a shield against it. Conversely, if an attack repeatedly defeats players, the opponent might stop using that move.

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The proposed adjustments can go in either direction: an opponent could become easier when players are struggling, or harder when players beat it too easily. The patent gives an illustrative threshold of beating a boss less than 50 percent of the time, but that is a hypothetical example—not a measured result or a confirmed rule for a game.

Whose gameplay data could shape the opponent?

The filing describes using data from one player, multiple players, or a selected group. That distinction could affect whether an opponent responds to an individual’s habits or to patterns found across a broader pool of players. The patent outlines both possibilities; it does not establish how Sony would select, collect, or apply player data in a commercial game.

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Would players have a choice?

The patent describes possible controls that could let a player opt in to adjusted difficulty or choose opponents already altered using other players’ gameplay data. It also lists potential rewards for beating an opponent at a changed difficulty, including trophies, medals, points, in-game currency, or e-commerce currency. These are illustrative options in the filing, not a promise that any game will offer them.

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Is this an announced PlayStation feature?

No. The U.S. patent record concerns Sony Interactive Entertainment LLC’s filing, “Use of machine learning to increase or decrease level of difficulty in beating video game opponent.” It lists a filing date of July 25, 2019, and a publication date of July 20, 2021; the application was later granted as U.S. Patent 11,065,545 B2. The record names Sony Interactive Entertainment LLC as the original assignee and lists Matthew A. DeVico in the assignment history. Google Patents record.

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A patent documents claimed inventions and illustrative embodiments. The patent record and The Next Web’s February 2, 2021 coverage do not establish that Sony has deployed this system in a commercial game. The record shows a maintenance-fee payment in December 2024, but that information alone does not establish the patent’s current enforceability.

Neither source provides measured evidence about how well the proposed system works, whether players prefer it, or whether it has been adopted. The filing’s numerical scenarios are hypotheticals, not study findings.

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