Granted Patent B2

US 12,566,616 · App. 17/968,108 · Granted Mar 3, 2026

Inventor: Jan Lutter (Ahlen, DE)

Assignee: Zoom Communications, Inc.

G06F9/44505G06F8/76

Abstract

Claims (73)

1 . A computer-implemented method comprising:

receiving, by a processing engine, an image from a client device;

performing, by the processing engine, object detection on the image to determine pixel boundaries of computing devices;

extracting, by the processing engine, respective pixel groups of each of the computing devices;

classifying each of the respective pixel groups;

determining, by the processing engine, a respective device model of each of the computing devices based on the respective pixel groups with a confidence level;

determining, by the processing engine, an indication of compatibility of the respective device models of the computing devices with at least one software product in a video communication platform;

providing for display, via a user interface of the client device, the indications of compatibility of the computing devices with the at least one software product in the video communication platform;

selecting a subset of computing devices, of the computing devices, that are compatible to optimally work together with the at least one software product, wherein selecting the subset of computing devices comprises ranking the computing devices based on device features including at least a device model and one or more performance characteristics retrieved from a device configuration database; and

automatically configuring, by the processing engine, the at least one software product, based on the indication of compatibility, wherein at least one configured software product is configured to use the subset of computing devices, wherein the automatic configuration comprises generating and transmitting configuration parameters to the video communication platform to select and activate the subset of computing devices for use in a video session without user intervention.

2 . The computer-implemented method of claim 1 , wherein determining the respective device model comprises:

inputting the received image into a trained machine learning model; and

identifying, by the trained machine learning model, the respective device models of the computing devices.

3 . The computer-implemented method of claim 1 , wherein determining the indication of compatibility of the respective device model comprises:

comparing the respective device model, via a comparison table, to find the at least one compatible software product, wherein the comparison table includes information for multiple computing device models that have associated compatibility information for the at least one software product.

4 . The computer-implemented method of claim 1 , wherein the confidence level indicates a level of confidence that a computing device of the computing devices is of a certain make and model.

5 . The computer-implemented method of claim 1 , further comprising:

based on the respective device model, selecting a configuration template for the respective device model; and

applying the configuration template to the at least one software product to configure the at least one software product to use the computing devices.

6 . The computer-implemented method of claim 1 , further comprising:

wherein the received image depicts multiple computing devices; and

identifying one of the multiple computing devices as most suitable for use with a particular software product.

7 . The computer-implemented method of claim 1 , wherein identifying one of the computing devices as most suitable comprises:

ranking the computing devices based on the respective device model meeting multiple compatibility requirements for using a computing device with a particular software product; and

selecting one of the computing devices having a higher rank than other of the one or more computing devices.

8 . A non-transitory computer readable medium that stores executable program instructions that when executed by a processor, cause the processor to perform operations comprising:

receiving an image from a client device;

performing object detection on the image to determine pixel boundaries of computing devices;

extracting respective pixel groups of each of the computing devices;

classifying each of the respective pixel groups;

determining a respective device model of each of the computing devices based on the respective pixel groups with a confidence level;

determining an indication of compatibility of the respective device models of the computing devices with at least one software product in a video communication platform;

providing for display, via a user interface of the client device, the indication of compatibility of the computing devices with the at least one software product in the video communication platform;

selecting a subset of computing devices, of the computing devices, that are compatible to optimally work together with the at least one software product, wherein selecting the subset of computing devices comprises ranking the computing devices based on device features including at least a device model and one or more performance characteristics retrieved from a device configuration database; and

automatically configuring the at least one software product, based on the indication of compatibility, wherein at least one configured software product is configured to use the subset of computing devices, wherein the automatic configuration comprises generating and transmitting configuration parameters to the video communication platform to select and activate the subset of computing devices for use in a video session without user intervention.

9 . The non-transitory computer readable medium of claim 8 , wherein determining the respective device model comprises:

inputting the received image into a trained machine learning model; and

identifying, by the trained machine learning model, the respective device models of the computing devices.

10 . The non-transitory computer readable medium of claim 8 , wherein determining the indication of compatibility of the respective device model comprises:

comparing the respective device model, via a comparison table, to find the at least one compatible software product, wherein the comparison table includes information for multiple computing device models that have associated compatibility information for the at least one software product.

11 . The non-transitory computer readable medium of claim 8 , wherein the confidence level indicates a level of confidence that a computing device of the computing devices is of a certain make and model.

12 . The non-transitory computer readable medium of claim 8 , the operations further comprising:

based on the respective device model, selecting a configuration template for the respective device model; and

applying the configuration template to the at least one software product to configure the at least one software product to use the computing devices.

13 . The non-transitory computer readable medium of claim 8 , the operations further comprising:

wherein the received image depicts multiple computing devices; and

identifying one of the multiple computing devices as most suitable for use with a particular software product.

14 . The non-transitory computer readable medium of claim 8 , wherein identifying one of the computing devices as most suitable comprises:

ranking the computing devices based on the respective device model meeting multiple compatibility requirements for using a computing device with a particular software product; and

selecting one of the computing devices having a higher rank than other of the one or more computing devices.

15 . A system comprising:

a processing engine configured to:

receive an image from a client device;

perform object detection on the image to determine pixel boundaries of computing devices;

extract respective pixel groups of each of the computing devices;

classify each of the respective pixel groups;

determine a respective device model of each of the computing devices based on the respective pixel groups with a confidence level;

determine an indication of compatibility of the respective device models of the computing devices with at least one software product in a video communication platform;

provide for display, via a user interface of the client device, the indication of compatibility of the computing devices with the at least one software product in the video communication platform;

select a subset of computing devices, of the computing devices, that are compatible to optimally work together with the at least one software product, wherein the selection of the subset of computing devices comprises a ranking the computing devices based on device features including at least a device model and one or more performance characteristics retrieved from a device configuration database; and

automatically configure the at least one software product, based on the indication of compatibility, wherein at least one configured software product is configured to use the subset of computing devices, wherein the automatic configuration comprises generation and transmission of configuration parameters to the video communication platform to select and activate the subset of computing devices for use in a video session without user intervention.

16 . The system of claim 15 , wherein the processing engine is configured to:

input the received image into a trained machine learning model; and

identify, by the trained machine learning model, the respective device models of the computing devices.

17 . The system of claim 15 , wherein the processing engine is configured to:

compare the respective device model, via a comparison table, to find the at least one compatible software product, wherein the comparison table includes information for multiple computing device models that have associated compatibility information for the at least one software product.

18 . The system of claim 15 , wherein the confidence level indicates a level of confidence that a computing device of the computing devices is of a certain make and model.

19 . The system of claim 15 , wherein the processing engine is configured to:

based on the respective device model, select a configuration template for the respective device model; and

apply the configuration template to the at least one software product to configure the at least one software product to use the computing devices.

20 . The system of claim 15 ,

wherein the received image depicts multiple computing devices, and the processing engine is further configured to:

identify one of the multiple computing devices as most suitable for use with a particular software product.

Assignments (2)

Continuity (1)

References Cited (10)