Virtual environment setup and configuration

Abstract

This invention relates to the arrangement and configuration of virtual environments. Techniques for arranging and configuring virtual environments are described. In one example, virtualization technology is used to generate a virtual environment implemented via a virtual reality platform using one or more computing devices. The virtual environment includes virtual shops arranged along virtual streets. Virtual digital content is included in the shops to initiate the redemption of goods or services represented by the content. The configuration of the virtual environment is based on user input, provider input, and / or machine learning input.

Description

Technical Field

[0001] This invention relates to the setup and configuration of virtual environments. Virtual environment setup and configuration techniques are described. Background Technology

[0002] The amount of publicly available digital content owned by users continues to increase. For example, users can access hundreds of digital documents, thousands of digital movies, tens of thousands of digital images, and seemingly countless web pages. Therefore, technologies developed to manage access to digital content are challenged by the sheer volume of digital content available to users.

[0003] Consider an instance involving user navigation between web pages containing items of interest in digital content (e.g., using a browser executed by a computing device). Conventional techniques available to computing devices for managing such interactions rely on "bookmarking" specific web pages to enable their location during subsequent navigation. However, this technique may soon become impractical due to the large number of items that may be of interest and, consequently, difficult to navigate. Furthermore, even though techniques have been developed to expand the richness of displays and user interactions with digital content through virtualization (e.g., augmented reality and virtual reality), this increased richness presents additional challenges in navigating through representations of digital content and may therefore fail to achieve its intended purpose. Summary of the Invention

[0004] The text describes techniques for setting up and configuring virtual environments. In one example, virtualization technology is used to generate a virtual environment implemented via a virtual reality platform using one or more computing devices. The virtual environment includes virtual shops arranged along virtual streets. The shops contain virtual digital content to initiate the redemption of goods or services represented by that content. The configuration of the virtual environment is based on user input, provider input, and / or machine learning input.

[0005] The present invention provides a simplified summary of a series of concepts further described in the detailed embodiments. Thus, the present invention is not intended to identify essential features of the claimed subject matter, nor is it intended to serve as an aid in determining the scope of the claimed subject matter. Attached Figure Description

[0006] Specific embodiments are described with reference to the accompanying drawings. Entities shown in the drawings may refer to one or more entities, and therefore entities in the singular or plural form may be referred to interchangeably in the discussion.

[0007] Figure 1 This is a diagram illustrating the environment in which the virtual environment setup and configuration techniques described in this article can be used during the operations in the example implementation.

[0008] Figure 2The system in the example implementation is depicted, which illustrates a virtual environment platform for implementing the layout of a virtual store. Figure 1 Operating the virtual environment management system.

[0009] Figure 3 Describing as about Figure 2 An example of a virtual environment with the described configuration.

[0010] Figure 4 It is a flowchart depicting the process in the example implementation, in which the virtual platform configures the virtual environment for output to the client device.

[0011] Figure 5 The system in the example implementation is depicted, which shows in more detail the use of machine learning techniques as part of the configuration of the virtual environment. Figure 2 Operating the virtual environment platform.

[0012] Figure 6 It is a flowchart depicting the process of using machine learning to train and use a model to configure a virtual environment in the example implementation.

[0013] Figure 7 The system in the example implementation is described, in which data input by the user of the client device and / or the provider of the virtual store is used as part of the configuration of the virtual environment.

[0014] Figure 8 An example system is shown, including various components of an example device, which can be implemented as described in reference. Figures 1 to 7 Any type of computing device described and / or utilized to implement the techniques described herein. Detailed Implementation

[0015] Overview

[0016] The ubiquity and abundance of digital content that even casual users can interact with through computing devices present numerous challenges that impact the operation of these devices and the ability of users to interact with them. These challenges are further exacerbated by the availability of technologies designed to enrich user interactions supported by computing devices.

[0017] Therefore, virtual environment setup and configuration techniques are described, which address the increased richness supported by computing devices while still supporting navigation through digital content. In one example, virtualization techniques are employed to generate a virtual environment implemented by one or more computing devices via a virtual reality platform. Virtualization techniques include: the use of augmented reality, in which the virtual environment enhances the direct view of the physical environment of the computing devices; and the use of virtual reality, in which the virtual environment replaces the direct view of the physical surroundings.

[0018] The virtual environment platform is configured to manage the layout and configuration of virtual environments accessed by initiators and viewers, and to expose functionality for managing the creation of such environments. From the perspective of a user on a client device, this includes, for example, a representation of digital content (e.g., web pages, websites) exposed by the virtual environment platform to create virtual stores that mimic the appearance of physical stores in a physical environment. This includes defining the “look” of the virtual store. Functionality for specifying the arrangement of virtual stores within the environment relative to each other is also exposed (e.g., grouping based on a virtual street layout).

[0019] For example, it can receive user input specifying the layout of virtual stores along corresponding streets, as well as indicators for specifying grouping criteria (e.g., user-specified or automatically generated using natural language processing). Virtual digital content representing goods or services available for redemption within the virtual stores can also be arranged through user interaction with the virtual environment platform. In this way, users can customize the virtual environment in a user-intuitive manner.

[0020] The virtual environment platform is also configured to support interactions through the provider of the virtual store. For example, the virtual environment platform can be configured to expose a software development kit (SDK) to create virtual stores and virtual digital content representing goods or services that can be purchased within the virtual store. As mentioned above, this includes the ability to customize the "look and feel" of the virtual store and the ability to virtual content within the store, such as the ability to mimic physical stores and physical items.

[0021] For providers, this also includes the ability to set standards that can be used, for example, to automatically and without user intervention, arrange virtual stores alongside other virtual stores along streets within the virtual environment. Standards could include, for example, a set of tags specified by the user that describe the virtual store's potential purpose, theme, visual characteristics, etc. The virtual environment platform can then use these tags to automatically and without user intervention arrange the virtual environment. In this way, virtual store providers are given a degree of control over how their virtual stores are arranged relative to other stores through the virtual environment platform. Other examples are also considered for using machine learning classification techniques to assign tags. Therefore, the SDK's functionality improves the efficiency of user interaction when creating and managing virtual stores and virtual digital content.

[0022] The virtual environment platform is also configured to leverage machine learning to support the automated placement of virtual stores, virtual digital content within those stores, and the appearance and feel of the virtual stores and their digital content. For example, the virtual environment platform can use machine learning to train models to manage the placement of virtual stores based on the likelihood of achieving outcomes (e.g., redemption of goods or services). Other examples include training models to adjust the appearance and / or virtual digital content of virtual stores, for example, also to adjust the appearance to increase the likelihood of achieving outcomes (e.g., redemption). For instance, the virtual environment platform can process data describing user interactions with digital content and apply machine learning through model usage to identify features that influence the outcome. Those features identified by the model are then exposed to automatically and without user intervention in configuring the virtual environment.

[0023] Therefore, training data used to train the model can originate from a variety of sources, including previous user interactions based on virtual environments, virtual stores, and / or virtual digital content set up on virtual stores. Training data can also originate “external” to the virtual environment, such as through monitored user interactions with other digital content such as web pages, digital marketing content (e.g., advertisements). Training data can also include data describing user characteristics (e.g., demographic information, user preferences regarding the appearance of the virtual store and virtual digital content, etc.). In this way, virtual environment platforms leverage machine learning to assist user interaction and improve the operation of computing devices through increased accuracy. Further discussion of these and other examples is included in the following sections and illustrated with corresponding figures.

[0024] In the following discussion, example environments in which the techniques described herein can be employed are described. Example procedures that can be executed in the example environments, as well as in other environments, are also described. Therefore, the execution of the example procedures is not limited to the example environments, and the example environments are not limited to the execution of the example procedures.

[0025] Example Virtual Environment

[0026] Figure 1 This is an illustration of a digital media environment 100 in an example implementation, which can be operated using the virtual environment setup and configuration techniques described herein. The environment 100 shown includes a service provider system 102 and a client device 104 communicatively coupled via a network 106, such as the Internet. The computing devices implementing the service provider system 102 and the client device 104 can be configured in various ways.

[0027] For example, computing devices can be configured as desktop computers, laptop computers, mobile devices (e.g., assumed to be handheld configurations such as tablets or mobile phones), wearable devices (e.g., digital watches, digital goggles as shown), etc. Therefore, the range of computing devices extends from fully-resourced devices with abundant memory and processor resources (e.g., personal computers, game consoles) to low-resource devices with limited memory and / or processing resources (e.g., mobile devices). Furthermore, computing devices also refer to several different types of devices, such as those used by enterprises to perform operations "in the cloud" for service provider systems and... Figure 8 Further description of the multiple servers.

[0028] Client device 104 includes a communication module 108, which represents the functionality for communicating with service provider system 102 via network 106. Examples include network-enabled applications such as browsers, plug-in modules, etc. Communication module 108 also includes a virtual environment interaction system 110, which is configured to support interaction with and rendering of virtual environments. Motion sensors, such as accelerometers, image capture, time-of-flight devices, sound wave or electronic wave reflection technologies, can be used to support navigation through these environments. Voice navigation is also supported, where users initiate searches for virtual stores, virtual streets, virtual digital content, etc., using their spoken words.

[0029] In the augmented reality example, the virtual environment is provided as part of a real-time stream of digital images of the physical surroundings of client device 104, captured by a digital camera and displayed on a user interface via a display device. In this way, the virtual environment enhances the real-time view of the physical environment 108, for example, as if "it really is there." In the virtual reality example, the virtual environment replaces the direct view of the physical surroundings with a virtualization of the physical environment.

[0030] The service provider system includes a service manager module 112 configured to manage the provision of digital services for access via network 106. An example of this functionality is illustrated as a virtual environment management system 114 implementing a virtual environment platform 116. The virtual environment platform 116 implements functions for creating, editing, deploying, and distributing virtual environments 120, shown as being maintained in storage device 118.

[0031] Virtual environment 120 can be configured to include one or more of the following: virtual reality streets 122, virtual stores 124, virtual digital content 126, and support objects 128 representing the digital content and supporting user interaction with that content. In an illustrative example 130 rendered by client device 104, virtual stores 124 are grouped and arranged along virtual streets 122. Virtual digital content 126 is set within virtual stores 124 representing goods or services available for purchase. For example, selecting items in virtual digital content 126 is configured to initiate a purchase of the represented goods or services. Support objects 128 represent additional functionality that can be added to the virtual environment, examples of which include street name indicators 132 (e.g., list criteria for grouping virtual stores 124 along virtual streets) and example 134 depicting support objects with mobile billboards displaying advertisements as digital marketing content. Various other examples are also envisioned.

[0032] As previously described, the virtual environment platform 116 is implemented by the virtual environment management system 114 to support a wide range of functions across various use cases. In the first example, the virtual environment 120 is deployed and customized based on the identification of the user accessing the environment. Figures 2 to 4 Further discussion of this is described. In the second example, machine learning techniques are employed to configure the virtual environment 120 via the virtual environment platform 116; further discussion of this can be found regarding... Figure 5 and Figure 6 Found. In the third example, the virtual environment platform 116 supports the customization of the virtual store and virtual digital content 126 by a provider associated with the virtual store through an open software development kit (SDK). Further discussion of this can be found on [link to relevant documentation]. Figure 7 turn up.

[0033] Generally, the functions, features, and concepts described above and below can be employed within the context of the exemplary processes described in this section. Furthermore, the functions, features, and concepts described with respect to the different figures and examples herein are interchangeable and are not limited to implementation within the context of a particular figure or process. Additionally, blocks associated with the different representative processes and corresponding figures herein can be applied together and / or combined in different ways. Therefore, the various functions, features, and concepts described with respect to the different exemplary environments, devices, components, figures, and processes herein can be used in any suitable combination and are not limited to the specific combinations represented by the examples listed in this specification.

[0034] Figure 2 System 200 in an example implementation is depicted, which illustrates the operation of a virtual environment management system 114 that implements a virtual environment platform 116, which is configured to deploy virtual stores within a virtual environment. Figure 3 Depicting the use Figure 2 Example 300 of the virtual environment 120 configured by the system. Figure 4 The example implementation describes process 400, in which the virtual platform configures the virtual environment to output to the client device.

[0035] The following discussion describes techniques that can be implemented using the systems and devices previously described. Aspects of the process are implemented in hardware, firmware, software, or a combination thereof. The process is shown as a set of boxes specifying operations performed by one or more devices and is not necessarily limited to the order shown for the operations performed by the respective boxes. In the sections of the following discussion, reference is made to… Figures 1 to 4 .

[0036] In this example, the input module 202 begins by receiving a request 204 from the client device 104 via network 106 for accessing a platform (e.g., virtual environment platform 116) that implements the virtual environment 120. Request 204 includes a user identifier (ID) (box 402). The client device 104 may, for example, use a browser to navigate to a network address through which the virtual environment 120 becomes available. Request 204 includes a user ID 206 associated with the user, such as a cookie, which can be used to identify the user, including any other technology such as an IP address.

[0037] User ID 206 is passed from input module 202 to user data location module 208. User data location module 208 represents the function of locating user data 210 from user data 212 stored in storage device 214 based on user ID 206 (block 404). User data 212 can be configured in various ways. In one example, user data 212 is manually entered by the user so as to set the following preferred layout: virtual store 124 and / or virtual streets 122 grouped thereon, virtual digital content 126 within virtual store 124, appearance (e.g., virtual streets 122, virtual store 124, virtual digital content 126), supporting objects 128, etc., as per [reference to...]. Figure 7 Further description.

[0038] In another example, user data 212 describes characteristics associated with a user. These characteristics include the user's demographics, features of the client device 104 (e.g., software and / or hardware), etc. Other examples include user data 212 describing previous user interactions with virtual environments, other digital content (e.g., redemptions associated with digital marketing content), etc. This data can be used to train models using machine learning and then used to configure virtual stores 124, virtual streets 122, virtual digital content 126, supporting objects 128, etc., as per [reference to specific contexts / resources]. Figure 5 and Figure 6 Further description.

[0039] User data 210 is passed as input from user data location module 208 to virtual configuration module 216. Virtual configuration module 216 is configured to determine the layout of virtual store 124 within virtual environment 120 based on user data 212. In this example, virtual store 124 includes virtual digital content 126 (box 406) that can be selected to initiate purchases of goods or services. User data 210 may specify configurations, for example. Other examples include automatically generating configuration data 218 using a model trained through machine learning.

[0040] User data 210 can be processed, for example, by a model trained using training data as part of machine learning (e.g., previous user data collected for user ID 206, a set of user IDs, etc.). The model is trained to achieve various different results, such as arranging virtual stores 124 with similar criteria based on the probability of interest in the corresponding user, assigning labels to virtual stores for grouping along virtual streets, etc. In this way, the model can be used to determine the correlation between virtual stores 124 and virtual digital content 126 that cannot be detected by humans (e.g., in a hidden state), and these correlations can be used to generate configuration data 218. Other examples are also envisioned, such as utilizing identification information output by the provider associated with virtual store 124, such as information about... Figure 7 Further description is shown below.

[0041] Then, the virtual generation module 220 generates a virtual environment 120 (box 408) with a defined arrangement specified by configuration data 218. The virtual generation module 220 may, for example, access virtual streets 122, virtual shops 124, virtual digital content 126, supporting objects 128, etc., held in storage device 222. Then, as indicated, the virtual generation module 220 arranges the virtual shops 124 along the virtual streets 122. This also includes the arrangement of the virtual digital content 126 within those shops, which may be specified by the user, provider, and / or using a machine learning model. Once generated, the virtual environment 120 is output by the output module 224 to communicate with client device 104 via network 106 (box 410), for example, for rendering and subsequent user navigation to purchase goods or services.

[0042] exist Figure 3In the illustrated example 300, the virtual environment 120 includes multiple virtual shops 302(1)-302(M), 304(1)-304(N), 306(1)-306(O), 308(1)-308(P), and 310(1)-310(Q) grouped along corresponding virtual streets 312, 314, 316, 318, and 320. Virtual streets 312 to 320 are shown as including indicators (depicted as virtual signs) for grouping the respective virtual shops together along corresponding axes. Additional axes can also be used with corresponding indicators 322, such that the grouping of streets and virtual shops is performed together for multiple criteria, for example, as a matrix arrangement. For example, a first axis can be used to group virtual shops based on a theme, while a second axis can be used to group virtual shops based on the frequency of visits. Additional axes may also be employed.

[0043] Figure 3 The virtual environment 120 also includes examples of supporting objects 324, which are depicted as physical mobile billboards mimicking those located on vehicles moving through the virtual environment 120 and displaying digital marketing content. The digital marketing content may be generated, for example, based on machine learning techniques to increase the likelihood of achieving outcomes such as redemption of goods or services initiated by selecting an object in the user interface. Other examples of supporting objects include signs placed in corresponding virtual stores, descriptions on park benches, etc. Various technologies are used to support navigation through the environment, such as motion sensing, head tracking, handheld controllers, etc. In this way, the virtual environment 120 supports user navigation with multiple contents in an easy-to-understand and intuitive manner, and thus improves the operational efficiency of the computing devices implementing these technologies and user interaction with these devices.

[0044] Figure 5 The example implementation of system 500 is depicted, which further illustrates the use of machine learning techniques as part of the configuration of virtual environment 120. Figure 2 Operating the virtual environment platform. Figure 6 The process of using machine learning to train and use a model to configure a virtual environment is described in an example implementation 600.

[0045] The following discussion describes techniques that can be implemented using the systems and devices previously described. Aspects of the process are implemented in hardware, firmware, software, or a combination thereof. The process is shown as a set of boxes specifying operations performed by one or more devices and is not necessarily limited to the order shown for the operations performed by the individual boxes. References will be made to the following sections of the discussion. Figures 1 to 6 .

[0046] In this example, the process begins by receiving training data (box 602) and using that data to train a model using machine learning (box 604). Figure 5 As shown, the training data collection module 502 is configured to collect training data 504, which can utilize various different sources 506.

[0047] Training data 504, for example, can describe with Figure 2 The user ID 206 is associated with previous user interactions involving previous interactions with the virtual environment. Data generated by monitoring such interactions includes user interactions with virtual stores, virtual digital content, virtual streets, and / or supporting objects. Therefore, this data can describe the user's overall preferences relative to the virtual environment, and based on this, the model training module 508 uses this data to train model 510 using machine learning to identify those preferences. The results of training the model include user efficiency during navigation within the virtual environment 120, conversion of goods or services, etc., based on the arrangement of virtual stores, virtual streets, and virtual digital content.

[0048] In another example, training data 504 describes user interactions “outside” the virtual environment. For example, training data 504 could be collected based on previous user interactions with digital marketing content and whether a conversion occurred, characteristics of the digital content (e.g., appearance, type), goods or services associated with the digital marketing content, etc. In this example, model 510 is then trained by model training module 508 to configure virtual environment 120 based on insights gained from the processing, thereby arranging virtual stores, virtual streets, and / or virtual digital content within virtual stores. This also includes configuring the perception (i.e., appearance) based on these insights (e.g., preferred colors, themes, etc.).

[0049] As used herein, the terms "model" and "machine learning" refer to a computer representation that can be tuned (e.g., trained) by computing devices based on inputs to approximate unknown functionality. Specifically, this can include models that utilize algorithms to learn from and predict known data by analyzing known data to generate outputs that reflect patterns and properties of the known data. For example, models can include, but are not limited to, decision trees, support vector machines, linear regression, logistic regression, Bayesian networks, random forest learning, dimensionality reduction algorithms, augmentation algorithms, artificial neural networks, deep learning, etc. Thus, when used as part of machine learning, a model represents a high-level abstraction of data by generating data-driven predictions or decisions based on known input data.

[0050] The training model 510 is then passed from the model training module 508 to the virtual configuration module 216 for use. The model 510 is then used to process the user data 512 (box 606) received by the virtual configuration module 216, which describes user interactions with digital content, to generate configuration data 218 to determine the layout of the virtual store within the virtual environment 120 (box 608). Continuing the previous example, the user data 512 may describe user interactions with the virtual environment, user characteristics, user interactions with digital content “outside” the virtual environment, etc.

[0051] As previously described, the virtual generation module 220 then generates a virtual environment 120 with a defined layout (box 610), and the output module 224 outputs this virtual environment 120 to communicate with the client device 104 via the network 106 (box 612). In the illustrated example, monitored interactions with the virtual environment 120 are used to further collect training data 504 to train a subsequent model 510 using machine learning. In this way, the virtual environment platform 116 is adapted for further user interactions that are not possible to be performed by humans.

[0052] Figure 7 System 700 in an example implementation is depicted, in which data input by a user and / or virtual store provider of client device 104 is used as part of the configuration of the virtual environment. Virtual environment platform 116 includes a user data input module 702 configured to collect user input data 704 from client device 104. User input data 704 describes, for example, the manual arrangement of virtual store 124 and virtual digital content 126 within virtual store 124. User input data 704 may also describe the appearance of virtual store 124 or virtual digital content 126, overall display theme, etc.

[0053] Additionally, user input data 704 may describe weights to be assigned to features that serve as the basis for the automatic arrangement of virtual stores 124, virtual digital content 126, supporting objects 128, virtual streets 122, etc. As previously mentioned, tags can be associated with virtual stores 124 by the store provider, for example, through classification using machine learning or the like. These tags can then be used by the virtual environment management system 114 to automatically arrange virtual stores within the virtual environment without user intervention. In this example, user input data 704 includes weights assigned by the user of client device 104 via a user interface, which will be considered by the virtual configuration module 216 as part of the arrangement. For example, the user can specify a greater weight to be assigned to virtual stores 124 that have a specific type of goods or services, a specific appearance, etc., which serve as the basis for arranging virtual stores 124 and / or virtual digital content 126 within virtual stores 124. Weights can also be used to change the order of virtual stores by changing the weights assigned to the corresponding tags. Various other examples involving virtual streets 122, supporting objects 128, etc., are also envisioned.

[0054] The virtual environment platform 116 also includes a provider input module 706 configured to expose to a provider computing device 708 to generate provider data 710, which can be used by the virtual configuration module 216 as part of configuring the virtual environment 120. A provider is an entity associated with a virtual store (e.g., a “seller” of goods or services represented by virtual digital content within the virtual store).

[0055] To support this, provider input module 706 discloses a software development kit 712 with content creation functionality 716 for creating virtual stores 124, virtual digital content 126, supporting objects 128, and even virtual streets 122 included as part of virtual environment 120. Examples of this functionality are shown as virtual store creation module 718, virtual digital content creation module 720, and layout standard creation module 722.

[0056] The software development kit (SDK) is configured, for example, to include a single executable file for compilation and debugging, as well as a software framework of objects included in the virtual environment. Application programming interfaces (APIs) are included as part of the SSD 712 as template functions and reusable functions, configured to allow content created by the provider to be used as part of the virtual environment 120. The SSD 712 can also be used by a user of the client device 104, for example, for inputting user input data 704 and supporting content creation functions. Therefore, the virtual environment platform 116 supports the functionality of uniting providers and clients within the virtual environment 120.

[0057] Example systems and devices

[0058] Figure 8 An example system, typically located at 800, is illustrated, including example computing device 802, which represents one or more computing systems and / or devices that can implement the various technologies described herein. This is illustrated by including a virtual environment platform 116. Computing device 802 can be, for example, a server of a service provider, a device associated with a client (e.g., a client device), a system-on-a-chip, and / or any other suitable computing device or computing system.

[0059] The example computing device 802 shown includes a processing system 804 communicatively coupled to each other, one or more computer-readable media 806, and one or more I / O interfaces 808. Although not shown, the computing device 802 may also include a system bus or other data and command transfer system that couples various components to each other. The system bus may include any one or a combination of different bus architectures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and / or a processor or local bus using any of a variety of bus architectures. Various other examples, such as control lines and data lines, are also contemplated.

[0060] Processing system 804 represents a function for performing one or more operations using hardware. Therefore, processing system 804 is shown as including hardware elements 810 that can be configured as processors, function blocks, etc. This can include implementations of hardware as application-specific integrated circuits or other logic devices formed using one or more semiconductors. Hardware elements 810 are not limited by the materials forming them or the processing mechanisms employed therein. For example, a processor can include semiconductors and / or transistors (e.g., integrated circuits (ICs)). In this context, processor-executable instructions can be electronically executable instructions.

[0061] Computer-readable storage medium 806 is shown as including memory / storage device 812. Memory / storage device 812 represents the memory / storage device capacity associated with one or more computer-readable media. Memory / storage device component 812 may include volatile media (e.g., random access memory (RAM)) and / or non-volatile media (e.g., read-only memory (ROM), flash memory, optical disk, magnetic disk, etc.). Memory / storage device component 812 may include fixed media (e.g., RAM, ROM, fixed hard disk drive, etc.) and removable media (e.g., flash memory, removable hard disk drive, optical disk, etc.). As further described below, computer-readable medium 806 may be configured in various other ways.

[0062] Input / output interface 808 represents a function that allows a user to input commands and information into computing device 802 and also allows information to be presented to the user and / or other components or devices using various input / output devices. Examples of input devices include keyboards, cursor control devices (e.g., mice), microphones, scanners, touch functionality (e.g., capacitive sensors or other sensors configured to detect physical touch), camera devices (e.g., which may employ visible or invisible wavelengths, such as infrared frequencies, to identify movements not involving touch, such as gestures), etc. Examples of output devices include display devices (e.g., monitors or projectors), speakers, printers, network interface cards, haptic-responsive devices, etc. Therefore, as further described below, computing device 802 can be configured in various ways to support user interaction.

[0063] This document describes various techniques in the general context of software elements, hardware elements, or program modules. Typically, such modules include routines, programs, objects, elements, components, data structures, etc., that perform specific tasks or implement specific abstract data types. As used herein, the terms “module,” “function,” and “component” generally refer to software, firmware, hardware, or a combination thereof. The techniques described herein are characterized as platform-independent, meaning they can be implemented on a variety of commercial computing platforms with various processors.

[0064] The described modules and technologies can be stored on or transmitted on some form of computer-readable medium. Computer-readable media can include various media accessible by computing device 802. By way of example, and not limitation, computer-readable media can include "computer-readable storage media" and "computer-readable signal media".

[0065] "Computer-readable storage medium" can refer to a medium and / or device capable of persistently and / or non-transitory storing information compared to mere signal transmission, carrier waves, or signals themselves. Therefore, a computer-readable storage medium refers to a non-signal-bearing medium. Computer-readable storage media include hardware such as volatile and non-volatile, removable and non-removable media and / or storage devices implemented using methods or techniques suitable for storing information (e.g., computer-readable instructions, data structures, program modules, logic elements / circuits, or other data). Examples of computer-readable storage media may include, but are not limited to: RAM, ROM, EEPROM, flash memory or other storage technologies, CD-ROM, digital versatile disk (DVD) or other optical storage devices, hard disks, magnetic tape cassettes, magnetic tapes, disk storage devices or other magnetic storage devices, or other storage devices, tangible media, or articles of art suitable for storing desired information that can be accessed by a computer.

[0066] "Computer-readable signal medium" can refer to a signal-bearing medium configured to transmit instructions to the hardware of computing device 802, for example, via a network. Signal media can typically embody computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves, data signals, or other transmission mechanisms. Signal media also includes any information transmission medium. The term "modulated data signal" means a signal whose characteristics are set or altered in a manner that encodes information in the signal. By way of example, and not limitation, communication media include wired media (e.g., wired networks or direct wired connections) and wireless media (e.g., acoustic, RF, infrared, and other wireless media).

[0067] As previously described, hardware element 810 and computer-readable medium 806 represent modules, programmable device logic, and / or fixed device logic implemented in hardware form, which may be employed in some embodiments to implement at least some aspects of the techniques described herein (e.g., executing one or more instructions). The hardware may include components of integrated circuits or systems-on-a-chip, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), and other implementations of silicon or other hardware. In this context, the hardware may operate as a processing device to perform program tasks defined by instructions and / or logic embodied by the hardware, and as hardware for storing instructions for execution (e.g., the previously described computer-readable storage medium).

[0068] The various techniques described herein can also be implemented using the combinations described above. Therefore, software, hardware, or executable modules can be implemented as one or more instructions and / or logic implemented on some form of computer-readable storage medium and / or by one or more hardware elements 810. Computing device 802 can be configured to implement specific instructions and / or functions corresponding to the software and / or hardware modules. Therefore, the implementation of modules executable as software by computing device 802 can be at least partially implemented in hardware, for example, by using computer-readable storage media and / or hardware elements 810 of processing system 804. Instructions and / or functions can be executable / operable by one or more manufactured products (e.g., one or more computing devices 802 and / or processing systems 804) to implement the techniques, modules, and examples described herein.

[0069] The techniques described herein can be supported by various configurations of computing device 802 and are not limited to specific examples of the techniques described herein. As described below, this functionality can also be implemented, in whole or in part, using a distributed system (e.g., via platform 816 on “cloud” 814).

[0070] Cloud 814 includes and / or represents platform 816 for resource 818. Platform 816 extracts the underlying functionality of hardware (e.g., server) and software resources of cloud 814. Resource 818 may include applications and / or data that can be utilized when performing computer processing on a server remote from computing device 802. Resource 818 may also include services provided via the Internet and / or via subscriber networks such as cellular or Wi-Fi networks.

[0071] Platform 816 can abstract resources and functions to connect computing device 802 to other computing devices. Platform 816 can also be used to abstract the scale of resources to provide a corresponding level of scale for the needs encountered by resource 818 implemented via platform 816. Therefore, in interconnected device implementations, the implementation of the functions described herein can be distributed throughout system 800. For example, the function can be implemented partly on computing device 802 and partly via platform 816, which abstracts the functions of cloud 814.

[0072] in conclusion

[0073] Although the invention has been described in language specific to structural features and / or methodological actions, it should be understood that the invention as defined in the appended claims is not necessarily limited to the specific features or actions described. Rather, the specific features and actions are disclosed as exemplary forms of implementing the claimed invention.

Claims

1. A method implemented by a computing device, the method comprising: A machine learning model is trained based on user data to achieve results, including user navigation efficiency in a virtual environment; The computing device receives a request from the user's client device via a network for accessing the platform that implements the virtual environment, the request including a user identifier; The computing device locates the user data corresponding to the user identifier; The computing device determines the layout of virtual stores along virtual streets within the virtual environment based on the user data. Each virtual store includes virtual digital content that can be selected to initiate a purchase of goods or services. Specifically, it is determined that the arrangement is performed by a trained machine learning model. The virtual streets include standard indicators within the virtual environment for grouping the virtual stores along the virtual streets. The criteria include a set of tags specified by the user, each tag describing the potential purpose of the corresponding virtual store, and The set of labels is assigned to the virtual store by the trained machine learning model; A virtual environment with a defined layout is generated by the computing device using the trained machine learning model and the set of labels; and The virtual environment generated by the computing device is used to communicate with the client device via the network.

2. The method according to claim 1, wherein, The determination also includes determining the arrangement of the virtual store along multiple axes and virtual streets within the virtual environment.

3. The method according to claim 1, wherein, The determination also includes determining the arrangement of the virtual digital content within their respective virtual stores.

4. The method according to claim 1, wherein, The determination is also based, at least in part, on data received from the service providers associated with the respective virtual stores in the virtual stores as a means of customizing the respective virtual stores.

5. The method according to claim 1, wherein, The user data also includes custom data input by a user associated with the user identifier, and the determination is based at least in part on the custom data.

6. The method according to claim 1, wherein, The user data describes previous user interactions with digital content associated with the user identifier.

7. The method according to claim 1, wherein: The virtual store is described as mimicking physical buildings; and The virtual digital content is depicted as mimicking physical items on a physical shelf.

8. A computing device, comprising: Processing system; as well as A computer-readable storage medium having instructions stored thereon, the instructions being responsive to execution by the processing system to cause the processing system to perform the method according to any one of claims 1 to 7.

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