Microsoft AR/VR patent sharing provides users with personalized experiences and digital environment adaptation methods based on saliency

Saliency-based digital environment adaptation method

（英伟网Nweon October 27, 2023) It is very difficult to create personalized experiences for users in the XR digital environment. Additionally, it is equally difficult to adapt a digital environment based on content related to another digital environment,

So in the patent application titled "Saliency-based digital environment adaptation", Microsoft introduced a saliency-based digital environment adaptation method. In examples, the digital environment may be adjusted based on a variety of factors, including content attributes, environment attributes, user profile attributes, and/or group attributes, among others. Accordingly, a salience measure may be determined for the content and/or location of the digital environment based on the factors described.

Next, content from a set of content can be determined based on an associated saliency measure, where the content set is ordered according to the saliency measure of each content instance.

Thus, a saliency measure for the highest ranking displayed to the user can be determined. For example, 2D or 3D assets can be presented to the user, and/or environment mechanics can be incorporated or modified

As another example, similar techniques can be leveraged to determine spatial locations for displaying content to users and to rank a set of spatial locations based on relevant saliency measures. Therefore, the experience provided by the digital environment may vary from user to user, providing a personalized experience for each user

To realize the function of adapting to the digital environment. Digital environment platform 102 may be a software application or a hardware device used to manage and control digital environment services 104 and computing devices 106 . The digital environment service 104 can provide various functions, including data storage, data processing, data analysis, user interface, etc. Computing device 106 may be a personal computer, smartphone, tablet, or other device connectable to a network. Through network 108, digital environment platform 102 may communicate with digital environment services 104 and computing devices 106 in order to achieve digital environment adaptation goals

Digital environment platform 102 can aggregate telemetry data related to one or more digital environments. The digital environment platform 102 includes a request processor 110, a saliency measurement engine 112, an interaction data store 114, and a content data store 116

In an example, request handler 110 handles various requests that may be received from digital environment services 104 and computing devices 106 . For example, request processor 110 may handle requests for saliency metrics associated with content. The request may contain an indication of the content whose significance is measured, an indication of the relevant digital environment, and/or one or more statistical data.

The saliency metric engine 112 may generate a saliency metric and/or an indication of location and/or content. For example, the saliency metric engine 112 may process telemetry data stored in the interaction data store 114 and/or content attributes related to the candidate content.

The saliency metric engine 112 may use any of a variety of techniques to generate saliency metrics and/or determine content and/or location from a set of candidates based on the correlation factors described above.

System 100 further includes digital environment services 104 that may be used to provide a digital environment. For example, when presenting a digital environment for display to a user of computing device 106, environment application 122 and digital environment service 104 may run as a client and server, respectively.

In other examples, environment application 122 may run locally such that digital environment service 104 may distribute environment application 122 to any of a variety of computing devices.

Digital environment service 104 consists of saliency processor 118 and content data storage 120. In an example, saliency processor 118 is used to generate and/or obtain telemetry data

Additionally, the saliency processor 118 may request saliency metrics from the digital environment platform. For example, saliency processor 118 may request a saliency measure for content from content data store 120 and/or may request external content from digital environment platform 102 .

As described above, environment application 122 may generate a digital environment for presentation to a user of computing device 106 . As another example, at least a portion of the digital environment may be presented through digital environment service 104.

Accordingly, saliency processor 124 and/or saliency processor 118 may determine content for adaptation to the digital environment. For example, saliency processor 124 may request saliency metrics and/or content from digital environment platform 102 .

In one case, the request includes at least a portion of a user profile, which may be stored by computing device 106. In other cases, information may be stored via digital environment services 104 and/or digital environment platform 102

The role of the environment application 122 is to determine the spatial location of the content, select the content to be displayed to the user, and adjust the environment mechanism based on the determined content

In other examples, any number of computing devices can be used. In these examples, the digital environment can be adapted to the user of each computing device to present different relevant representations to each user

For example, a first user may view the digital environment as containing a first content item, while a second user may view the digital environment as containing a second content item. Similarly, different environment mechanisms can be suitable for different users, just like the first user likes a certain environment mechanism, and the second user does not like this environment mechanism

As a further example, external content may be presented to a first user based on a first interest set associated with the first user, while external content may be presented to a second user based on a second interest set associated with the second user. Two users rendering external content

Figure 2 illustrates an example method 200 for generating a content significance measure.

Starting from operation 202, we can obtain a set of content attributes. For example, these attribute sets could include the relative accessibility and/or scarcity of the content

Operation 204, obtain a set of environment attributes. In an example, the set of environment attributes relates to a spatial location in the digital environment where the content may be presented, such as a spatial location close to the user. As another example, a set of environment attributes may include indications regarding the user's progress in the digital environment's storyline.

Operation 206, obtain a set of user profile attributes. For example, a user profile attribute set may be related to the user's gaming or interaction style, the user's attention habits (e.g., based on the user's perspective determined from an AR/VR headset), etc.

In operation 208, a set of attributes is obtained. In an example, the attribute set includes attributes similar to those obtained in operation 206, but aggregated based on one or more statistics. For example, a set of demographic attributes may be determined based on telemetry data associated with one or more digital environments (eg, may be provided by an interaction data store such as interaction data store 114). For example, demographic attributes may indicate difficulty levels and/or popularity related to game mechanics and/or the spatial location of the user and/or content that may be determined.

The content rewritten in Chinese is as follows: Operation 210 is to generate a saliency measure of the content based on the attributes obtained from operations 202-208. Different aspects of operation 210 may include generating a saliency measure using a machine learning model. As an example, operation 210 might generate a saliency measure based on a set of weights associated with each attribute

Operation 212 provides an indication of the generated saliency measure. For example, a significance metric may be provided in response to a significance metric request. As another example, a saliency measure can be provided and used to rank a set of content.

Figure 3 illustrates an example method 300 for adjusting a digital environment.

After starting operation 302, a set of candidate content can be obtained. For example, some of this content may be related to the digital environment. Additionally, some of this content may include content from external sources. These content collections are available from a variety of sources

Operation 304, determine a saliency measure for the content set. For example, operation 304 may include generating a saliency measure for each content instance in the content set obtained at operation 302.

Based on the significance measure generated by operation 304, the content set is sorted and proceeds to operation 308. Content is determined from the sorted content set. For example, one or more top-ranked content instances can be selected, or content can be randomly selected from the content instances based on a significance measure that is above a preset threshold.

Operation 310, adjust the digital environment according to the content determined in operation 308. For example, operation 310 may include adapting the digital environment to include 2D or 3D assets or NPCs for presentation to the user. Another example would be to present users with different RPG story options, either with varying difficulty, prominence, and/or length.

Another example method of adjusting the digital environment is shown in Figure 4 400

Starting from operation 402, a set of locations is determined. In an example, the location set may be determined based on the user's location in the digital environment. For example, a location set may include surfaces that are close to the user.

For the rewritten content, we need to convert it to Chinese and rewrite it without changing the original meaning

What needs to be rewritten is: operation 406, sorting the location set according to the significance measure generated in operation 404. Moving to operation 408, the location is determined based on the ordered set of locations. For example, one or more top-ranked positions may be selected, or as another example, positions may be randomly selected from among positions that have a significance measure above a preset threshold

Operation 410, determine content to adapt to the digital environment. Operation 412 uses the content determined at operation 410 to adjust the digital environment based on the location determined at operation 408. For example, operation 412 may include adjusting the digital environment to include a 2D or 3D asset or NPC for presentation to the user at a determined location.

Related Patents: Microsoft Patent | Saliency-based digital environment adaptation

https://patent.nweon.com/30770

The Microsoft patent application titled "Saliency-based digital environment adaptation" was originally submitted in March 2022 and was recently published by the US Patent and Trademark Office.

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