Game performance prediction from real-world performance data

Patent metadata

Field	Value
Publication number	US-2024042330-A1
Application number	US-202318490117-A
Country	US
Kind code	A1
Filing date	Oct 19, 2023
Priority date	Nov 11, 2021
Publication date	Feb 8, 2024
Grant date	—

Abstract

In non-limiting examples of the present disclosure, systems, methods, and devices for matching device configurations to games are presented. A set of device configuration tiers may be generated from gameplay telemetry data generated by a plurality of client devices executing a plurality of games. A device configuration for a specific client device may be determined based at least on the specific client device's GUI type. When the specific client device accesses a software game library a determination may be made based on a performance tier corresponding to the device configuration for the specific client device as to whether the specific client device can adequately execute each game. One or more recommendations may be rendered and displayed in the game library based on the determination of whether the specific client device can adequately execute each game.

First claim

What is claimed is:

1 . A computer-implemented method for matching device configurations to games, the computer-implemented method comprising:
determining a device configuration of a client device, the determining comprising analyzing a graphics processing unit (GPU) ID of a GPU of the client device; determining a performance tier of the device configuration from a set of device configuration performance tiers generated from telemetry data from a plurality of client devices with different GPUs executing a plurality of games, wherein the telemetry data comprises frame rate data for each of the plurality of games executed by the plurality of client devices; receiving an indication that the client device is accessing a software game library; identifying, from a subset of the telemetry data comprising telemetry data for a specific game included in the software game library, that the determined performance tier of the device configuration for the client device is associated with a frame rate that is below a minimum acceptable frame rate for the specific game; and providing, for display on a display device connected to the client device, an icon corresponding to the specific game and an indication that the client device is not a good fit for executing the specific game.

2 . The computer-implemented method of claim 1 , wherein the telemetry data further comprises display resolution data for each of the plurality of games executed by the plurality of client devices.

3 . The computer-implemented method of claim 1 , wherein the plurality of client devices that generated the telemetry data are classified into device configuration groups based on GPU IDs and central processing unit (CPU) IDs.

4 . The computer-implemented method of claim 1 , further comprising:
identifying, from a second subset of the telemetry data comprising telemetry data for a second specific game included in the software game library, that the determined performance tier of the device configuration for the client device is associated with a minimum acceptable frame rate for the second specific game; and providing, for display on the display device, a second icon corresponding to the second specific game and an indication that the client device is a good fit for executing the specific game.

5 . The computer-implemented method of claim 1 , further comprising:
in response to the client device executing the specific game, automatically reducing processing operations for one or more tasks other than execution of the specific game on the client device.

6 . The computer-implemented method of claim 5 , wherein automatically reducing processing operations for one or more tasks other than execution of the specific game on the client device comprises turning off electronic notifications for one or more applications executed by the client device.

7 . The computer-implemented method of claim 5 , wherein automatically reducing processing operations for one or more tasks other than execution of the specific game on the client device comprises pausing one or more background tasks being executed by the client device.

8 . The computer-implemented method of claim 1 , further comprising:
moving, for display on the display device, the specific game to a lower position in the software game library.

9 . The computer-implemented method of claim 1 , wherein generating the set of device configuration performance tiers comprises processing the telemetry data with one or more machine learning models that have been trained to classify device configurations into performance tiers.

10 . The computer-implemented method of claim 9 , wherein the one or more machine learning models comprise a neural network and an activation function.

11 . The computer-implemented method of claim 10 , further comprising:
determining that a frame rate of the specific game on the client device is below a threshold value; and providing negative feedback to the neural network, wherein providing the negative feedback comprises training the neural network using back propagation.

12 . The computer-implemented method of claim 10 , further comprising:
determining that a frame rate of the specific game on the client device is above a threshold value; and providing positive feedback to the neural network, wherein providing the positive feedback comprises training the neural network using back propagation.

13 . The computer-implemented method of claim 1 , wherein in generating the set of device configuration performance tiers. the telemetry data is processed with one or more machine learning models that have been trained to automatically remove personal identifying information.

14 . A computer-implemented method for matching device configurations to games, the computer-implemented method comprising:
determining a device configuration of a client device, the determining comprising analyzing a graphics processing unit (GPU) ID of a GPU of the client device; determining a performance tier of the device configuration from a set of device configuration performance tiers generated from telemetry data from a plurality of client devices with different GPUs executing a plurality of games, wherein the telemetry data comprises frame rate data for each of the plurality of games executed by the plurality of client devices; receiving an indication that the client device is accessing a software game library; identifying, from a subset of the telemetry data comprising telemetry data for a specific game included in the software game library, that the determined performance tier of the device configuration for the client device is associated with a frame rate that is below a minimum acceptable frame rate for the specific game; and providing, for display on a display device connected to the client device, an icon corresponding to the specific game and a selectable user interface element for executing the specific game by a server computing device.

15 . The computer-implemented method of claim 14 , wherein the telemetry data further comprises display resolution data for each of the plurality of games executed by the plurality of client devices.

16 . The computer-implemented method of claim 14 , wherein the plurality of client devices that generated the telemetry data are classified into device configuration groups based on GPU IDs and central processing unit (CPU) IDs.

17 . The computer-implemented method of claim 14 , further comprising:
identifying, from a second subset of the telemetry data comprising telemetry data for a second specific game included in the software game library, that the determined performance tier of the device configuration for the client device is associated with a minimum acceptable frame rate for the second specific game; and providing, for display on the display device, a second icon corresponding to the second specific game and an indication that the client device is a good fit for executing the specific game.

18 . The computer-implemented method of claim 14 , further comprising:
in response to the client device executing the specific game, automatically reducing processing operations for one or more tasks other than execution of the specific game on the client device, wherein automatically reducing processing operations comprises one or more of turning off electronic notifications for one or more applications executed by the client device and pausing one or more background tasks being executed by the client device.

19 . The computer-implemented method of claim 14 , wherei

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Verma Arunabh
• Rana Rajneil Singh
• Hosseini Khayat Seyed Ali
• Dubois Matthew Carl
• Dobyns Daniel Aaron
• Merry Sebastian Carl
• Solimini Griffin
• Hsu Shu-Wei
• Bailey William Jarrad
• Kiesow Timothy John
• Hamilton Eric
• Kavi Kripal

Classifications

• A63F13/77 Primary

  involving data related to game devices or game servers, e.g. configuration data, software version or amount of memory · CPC title

• G06N3/10

  Interfaces, programming languages or software development kits, e.g. for simulating neural networks · CPC title

• A63F13/335

  using Internet · CPC title

• A63F2300/535

  for monitoring, e.g. of user parameters, terminal parameters, application parameters, network parameters · CPC title

• A63F13/67 Primary

  adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use · CPC title