# US 2025/0083045 A1 · Local environment scanning to characterize physical environment for use in vr/ar > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0083045 A1 - Document ID: US20250083045A1 - Status: Published application - Kind: A1 - Application number: 18/813,875 - Filed: 2024-08-23 - Published: 2025-03-13 - Earliest claimed: 2021-09-12 - Assignees: Sony Interactive Entertainment Inc. - Applicants: Sony Interactive Entertainment Inc. - Inventors: Warren Benedetto; Yiwei Yang - Web page: https://patentgrep.com/patent/US20250083045A1 ## CPC - A63F 13/5378: for displaying an additional top view, e.g. radar screens or maps - G06F 3/011: {Arrangements for interaction with the human body, e.g. for user immersion in virtual reality (blind teaching G09B21/00)} - A63F 13/213: comprising photodetecting means, e.g. cameras, photodiodes or infrared cells (A63F13/219 takes precedence) - A63F 13/65: automatically by game devices or servers from real world data, e.g. measurement in live racing competition - G06F 3/012: {Head tracking input arrangements} - G06F 3/013: {Eye tracking input arrangements (G06F3/015 takes precedence)} - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06T 19/20: Editing of three-dimensional [3D] images, e.g. changing shapes or colours, aligning objects or positioning parts - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 20/20: in augmented reality scenes - G06T 2210/04: Architectural design, interior design - G06T 2215/16: Using real world measurements to influence rendering - G06T 2219/2008: Assembling, disassembling - A63F: CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A user's environment is scanned and an augmented reality game such as a treasure is set up based on the scan. The user needs to use a phone to uncover clues in a game environment that is customized to user's own personal real-world environment, which is discovered using SLAM or GPS so that a map of furniture can be built. The game hides a virtual object behind a virtualized image of the real-world furniture. Machine learning may be used to train a model with common objects and where interesting hidden spaces could exist. Given the user's inputted data, real world physical room data and objects are used to determine a likely location to hide a virtual object. ## Claims ### Claim 1 A method, comprising: generating a digital map of a real-world space and objects therein using images of the space; and using the digital map for execution of a virtual reality (VR) or augmented reality (AR) computer simulation. ### Claim 2 (depends on 1) The method of claim 1, comprising: identifying at least one hiding place in the digital map; and selectively presenting at least one virtual object not having a counterpart in the real-world space in the hiding place. ### Claim 3 (depends on 2) The method of claim 2, wherein the virtual object is not presented on the display unless a player executes a predetermined act to locate the virtual object. ### Claim 4 (depends on 3) The method of claim 3, wherein the predetermined act comprises looking at the virtual object. ### Claim 5 (depends on 2) The method of claim 2, comprising identifying the hiding place using at least one machine learning (ML) model. ### Claim 6 (depends on 1) The method of claim 1, wherein the digital map comprises a simultaneous localization and mapping (SLAM) data structure. ### Claim 7 An apparatus, comprising: at least one processor system configured for: generating a digital map of a real-world space and objects therein using images of the space; and using the digital map for execution of a virtual reality (VR) or augmented reality (AR) computer simulation. ### Claim 8 (depends on 7) The apparatus of claim 7, wherein the processor system is configured for: identifying at least one hiding place in the digital map; and selectively presenting at least one virtual object not having a counterpart in the real-world space in the hiding place. ### Claim 9 (depends on 8) The apparatus of claim 8, wherein the virtual object is not presented on the display unless a player executes a predetermined act to locate the virtual object. ### Claim 10 (depends on 9) The apparatus of claim 9, wherein the predetermined act comprises looking at the virtual object. ### Claim 11 (depends on 8) The apparatus of claim 8, wherein the processor system is configured for identifying the hiding place using at least one machine learning (ML) model. ### Claim 12 (depends on 7) The apparatus of claim 7, wherein the digital map comprises a simultaneous localization and mapping (SLAM) data structure. ### Claim 13 An apparatus, comprising: at least one computer memory that is not a transitory signal and that comprises instructions executable by at least one processor system configured for: generating a digital map of a real-world space and objects therein using images of the space; and using the digital map for execution of a virtual reality (VR) or augmented reality (AR) computer simulation. ### Claim 14 (depends on 13) The apparatus of claim 13, wherein the instructions are executable for: identifying at least one hiding place in the digital map; and selectively presenting at least one virtual object not having a counterpart in the real-world space in the hiding place. ### Claim 15 (depends on 14) The apparatus of claim 14, wherein the virtual object is not presented on the display unless a player executes a predetermined act to locate the virtual object. ### Claim 16 (depends on 15) The apparatus of claim 15, wherein the predetermined act comprises looking at the virtual object. ### Claim 17 (depends on 14) The apparatus of claim 14, wherein the instructions are executable for identifying the hiding place using at least one machine learning (ML) model. ### Claim 18 (depends on 13) The apparatus of claim 13, wherein the digital map comprises a simultaneous localization and mapping (SLAM) data structure.