Layered environmental effect wallpaper

Abstract

Techniques are disclosed for generating and displaying a layered environmental effect wallpaper based on environmental conditions and a current or default wallpaper image of a mobile device. When a. particular user input is detected or certain criteria, are met, a. mobile device generates a layered environmental effect wallpaper that includes graphical effects and color grading that are relevant to real-world environmental conditions, taking into account image features of the current or default wallpaper image. This approach ensures a user can easily view updated environmental information without sacrificing their ability to view their chosen or default wallpaper image.

Description

Docket No.: 1333-823WO01 LAYERED ENVIRONMENTAL EFFECT WALLPAPER RELATED APPLICATION

[0001] This application claims benefit of U.S. Provisional Application No.63 / 621,214, filed January 16, 2024 the entire contents which are incorporated herein by reference. BACKGROUND

[0002] Wallpaper images on mobile devices are often selected by users from their personal collection of images. Many times, these wallpapers are sentimental photographs or other images that the users desire to view on a regular basis. However, wallpaper images can get cluttered and partially or fully obscured by application icons, notifications, widgets, and other screen items. SUMMARY

[0003] The techniques of this disclosure are directed to generating a layered environmental effect wallpaper based on a current or default wallpaper image set to be displayed on a mobile device. The mobile device may generate the layered environmental effect wallpaper using one or more machine learning models and / or one or more lookup tables or data repositories to segment the default or current wallpaper image into image segments corresponding to particular objects and / or layers, determine current or predicted environmental conditions, determine graphical effects relevant to the current or predicted environmental conditions, and determine a color grading to apply to the image segments and graphical effects based on the current or predicted environmental conditions. In this way, various aspects of the techniques may enable the computing device to present a user of a mobile device with environmental information without requiring them to view notifications / widgets or to give up their view of certain image features in their chosen wallpaper image.

[0004] In some examples, a method comprises: determining, by a computing device, environmental conditions relevant to a particular location associated with the computing device; segmenting, by the computing device, a wallpaper image of the computing device into image layers, wherein the layers include at least a background image layer and a foreground image layer; generating, by the computing device, one or more graphical effect layers based on the environmental conditions; generating, by the computing device, a layeredDocket No.: 1333-823WO01 environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more graphical effect layers; and presenting, by the computing device and on a display associated with the computing device, the layered environmental effect wallpaper.

[0005] In some examples, a system comprises: one or more processors; and a non-transitory computer-readable medium encoded with instructions that, when executed by the one or more processors, cause the one or more processors to: determine environmental conditions relevant to a particular location associated with a computing device; segment a wallpaper image of the computing device into image layers, wherein the layers include at least a background image layer and a foreground image layer; generate one or more graphical effect layers based on the environmental conditions; generate a layered environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more graphical effect layers; and output, for display, the layered environmental effect wallpaper.

[0006] In some examples, non-transitory computer-readable medium is encoded with computer-executable instructions that, when executed by one or more processors, cause the one or more processors to: determine environmental conditions relevant to a particular location associated with a computing device; segment a wallpaper image of the computing device into image layers, wherein the layers include at least a background image layer and a foreground image layer; generate one or more graphical effect layers based on the environmental conditions; generate a layered environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more graphical effect layers; and output, for display, the layered environmental effect wallpaper.

[0007] The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the disclosure will be apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF DRAWINGS

[0008] FIG.1 illustrates an example of implementing a layered environmental effect on a wallpaper image of a mobile device, in accordance with techniques described herein.

[0009] FIG.2 illustrates an example of generating image segments from a wallpaper image and generating graphical effects based on environmental conditions, in accordance with techniques described herein.Docket No.: 1333-823WO01

[0010] FIG.3 illustrates an example of generating color grading for a layered environmental effect wallpaper using wallpaper image segments and determined environmental conditions, in accordance with techniques described herein.

[0011] FIG.4 is a block diagram illustrating a mobile device for generating and displaying layered environmental effect wallpaper images, in accordance with techniques of this disclosure.

[0012] FIG.5 is a flowchart illustrating an example operation of a mobile device in accordance with techniques of this disclosure. DETAILED DESCRIPTION

[0013] FIG.1 illustrates an example of implementing a layered environmental effect on a wallpaper image of a mobile device. Mobile device 102 may include display module 106, user interface (“UI”) component(s) 140 (e.g., “UI comp(s) 140”), and applications 108. In some examples, UI component(s) 140 may include a presence-sensitive display configured to detect input (e.g., touch and non-touch input) from a user of respective mobile device 102. UI component(s) 140 may output information to a user in the form of a UI, which may be associated with functionality provided by mobile device 102. Mobile device 102 may output such UIs that may be associated with computing platforms, operating systems, applications, and / or services executing at or accessible from mobile device 102, including outputting UIs including wallpaper image 101A and wallpaper image 101B, as will be described in more detail below. Display module 106 of mobile device 102 may control the function of the input devices and / or output devices included in mobile device 102, such as UI component(s) 140, as described herein. Applications 108 may include several applications installed on mobile device 102 and include at least a weather application or other application capable of obtaining environmental information from an environmental conditions monitoring service (e.g., a web browser application). Mobile device 102 may execute instructions of display module 106 and applications 108 using one or more processors of mobile device 102 to perform various operations described herein.

[0014] In this example, mobile device 102 is depicted with a display screen UI component 140 that renders wallpaper image(s) 101A, 101B displayed on the lock screen, home screen, or other background screen of mobile device 102. Wallpaper image 101A may be the background image for the lock screen or home screen. In the example of FIG.1, wallpaper image 101A includes a photograph of the user of mobile device 102 on a sunny beach. Wallpaper image 101B depicts the lock screen or home screen background of mobile deviceDocket No.: 1333-823WO01 102 once the layered environmental effect has been applied to wallpaper image 101A. While wallpaper images are discussed herein with respect to home screens, lock screens, and other graphical backgrounds on mobile device 102, it should be appreciated that the term “wallpaper image” as used herein may apply to any type of foreground or background image set to display on mobile device 102. For example, in some implementations, wallpaper image 101A and / or 101B may include an icon, a screensaver, an image displayed in one or more application(s) 108, and / or a slideshow including image(s).

[0015] Mobile device 102 includes the capability to communicate with a network node through one or more wireless communication protocols such as Advanced Mobile Phone System (AMPS), Code division multiple access (CDMA), Time division multiple access (TDMA), Global System for Mobile communications (GSM), Integrated Digital Enhanced Network (iDEN), General Packet Radio Service (GPRS), Enhanced Data rates for GSM Evolution (EDGE), Universal Mobile Telecommunications System (UMTS), Wideband Code Division Multiple Access (WCDMA), Code division multiple access 2000 (CDMA2000), Worldwide Interoperability for Microwave Access (WiMAX), Wireless-Fidelity (Wi-Fi), Long Term Evolution (LTE), 5G, and their variants. Mobile device 102 may also use ad-hoc or Bluetooth connectivity to execute applications 108 that utilize the ad-hoc or Bluetooth connection.

[0016] In one example, mobile device 102 may also be known as client device, mobile station, communication device, computing device, and the like. Mobile device 102 may be any suitable wireless computing device, including a cell phone, smart phone, laptop, wearable computing device (e.g., smart watch), tablet computer, infotainment system (e.g., vehicle head unit), and the like. Further, it should be understood that the present disclosure is not limited to a wireless computing device. Other types of wireless access terminal that include fixed wireless terminals may be used. For a better understanding, only the term mobile device is used herein and discussed hereafter. However, it should be understood that the term “mobile device” in the claims and description below may include mobile wireless or wired communication devices, stationary wireless terminals (e.g., fixed wireless router) or any other electronic devices coupled to a network.

[0017] Mobile device 102 may access environmental data associated with a particular location, for example the current location of mobile device 102 or another particular location entered via user input (e.g., via a touchscreen UI component 140 and weather application 108 of mobile device 102). The environmental data may be any environmental data available to mobile device 102, for example environmental data temporarily stored on mobile device 102Docket No.: 1333-823WO01 in association with a weather application 108 included in mobile device 102. In some examples, the environmental data may be obtained by mobile device 102 by communicating with a server associated with a weather or other environmental monitoring service. Once mobile device 102 has obtained the environmental data associated with the particular location, mobile device 102 processes the environmental data to determine one or more environmental conditions associated with the environmental data. Mobile device 102 may include one or more machine learning models or lookup tables that mobile device 102 may use to process the environmental data and determine a plurality of environmental conditions that apply to the environmental data. Thus, for example, mobile device 102 may process the obtained environmental data to determine environmental conditions for a current location of mobile device 102 include “humid”, “cold”, “frosty”, “snow – intensity 0.7, velocity 4.5”, and any other indications of environmental conditions or combinations of environmental conditions that may aid mobile device 102 in generating wallpaper image 101B.

[0018] Mobile device 102, for example using display module 106, processes wallpaper image 101A to generate a plurality of image segments to be used in the layered environmental effect. Mobile device 102 may include one or more machine learning models trained to accept wallpaper image 101A as input(s) and to generate, as output(s), the plurality of image segments. The generated plurality of image segments may include, for example, groups of pixels included in wallpaper image 101A associated with particular foreground object(s) 103A, particular background object(s) 103B, and / or any number of objects layered between such. In some examples, the generated image segments may be associated with labels indicating particular color schemes, recognized objects, recognized environmental conditions, or other contextual details determined to be indicated by wallpaper image 101A. The image segments may include or correspond to layers, such as a foreground layer, a background layer, and in some cases layers in between the foreground layer and a background layer. For example, display module 106 may use one or more machine learning models trained to identify or recognize one or more foreground objects 104A, one or more background objects 104B, and / or one or more other objects at depths between the foreground and background of wallpaper image 101A. Display module 106 may provide wallpaper image 101A as input to the one or more machine learning models and receive, as output from the machine learning model, indication(s) of the identified / recognized objects and their respective layers.

[0019] In the example of FIG.1, wallpaper image 101B that features the layered environmental effect applied to wallpaper image 101A includes one or more of the foreground objects 104A included in a foreground layer and one or more of the backgroundDocket No.: 1333-823WO01 objects 104B included in a background layer of wallpaper image 101A as well as one or more graphical effect(s) 104C and color grading or shading that were not featured in wallpaper image 101A. Display module 106 of mobile device 102 may generate one or more graphical effect layers including graphical effect(s) 104C to display in wallpaper image 101B and based on the determined environmental conditions associated with the particular location. In the example of FIG.1, display module 106 of mobile device 102 selects graphical effect(s) 104C associated with snow falling at a particular wind angle and particular velocity that mimic real world environmental conditions at the current location of mobile device 102 for inclusion in one or more of the graphical effect layers. The graphical effect(s) 104C of FIG.1 further include snow animations that bounce off of or accumulate on foreground object(s) 104A. Although snow animations are included in the example of FIG.1, it should be appreciated that any number of animations relevant to environmental conditions may be used alternatively or in addition to the snow animations. For example, when environmental conditions indicate that it is raining and windy outside, display module 106 of mobile device 102 may select graphical effect(s) 104C that include rain animations that pitter-patter or bounce off of objects at multiple layers of the layered environmental effect wallpaper 101B, as well as generate various graphical effect(s) 104C that correspond to hair or trees blowing in the wind to be placed at layers to these objects in wallpaper image 101A.

[0020] Display module 106 of mobile device 102 may place graphical effect(s) 104C at depths including between, behind, on top of, and in the same layer with the foreground layer corresponding to foreground object(s) 104A or the background layer corresponding to background object(s) 104B. In some examples, display module 106 may generate separate graphical effect layer(s), which include graphical effect(s) 104C and are separate from the foreground / background layers and any other image layers of wallpaper image 101A. For example, during foggy conditions, display module 106 may place a foggy layer in the foreground of wallpaper image 101B, so that it appears to the user as if they were gazing at their home screen through a foggy window. In some examples, display module 106 may generate combined layers that include both graphical effect(s) 104C and image segments of wallpaper image 101A. For example, display module 106 may generate a combined foreground layer that includes snow animations that bounce off of or accumulate on foreground object(s) 104A.

[0021] Graphical effect(s) 104C may include any graphical representations of environmental conditions. Graphical effect(s) 104C may be displayed with the plurality of image segments and / or may replace or cover up one or more of the image segments. In some examples, aDocket No.: 1333-823WO01 graphical effect layer may include one or more graphical effect(s) 104C corresponding to landscapes undergoing the environmental conditions. For example, display module 106 may replace a background layer of a sunny beach with a graphical effect 104C that includes a snowy forest. In some examples, display module 106 may generate graphical effect layers including graphical effect(s) 104C to display in wallpaper image 101B further based on one or more of the plurality of image segments. For example, Display module 106 may generate graphical effect layers including graphical effect(s) 104C based on the foreground objects 104A that include animated snow or other precipitation falling and accumulating on the foreground objects 104A, background objects 104B, or any other objects in wallpaper image 101A and / or wallpaper image 101B.

[0022] In some examples, display module 106 of mobile device 102 may generate one of the graphical effect layers to include color grading to one or more layers of wallpaper image 101B. For example, wallpaper image 101B may be generated to include a frosty blue tint applied on top of the background layer featuring background object(s) 104B or a frosty white tint applied to the borders of the screen as the topmost layer, or wallpaper image 101B may include sunrise colors applied as the topmost layer on top of wallpaper image 101B during sunrise hours. Display module 106 may generate the color grading using one or more machine learning models or one or more lookup tables accessible to mobile device 102, as will be described in more detail herein. Display module 106 may generate or select the color grading based on one or more of the image segments (e.g., the sky in wallpaper image 101A) and one or more of the determined environmental conditions (e.g., a blue tint when cold or raining). In some examples the color grading may further be generated or selected based on one or more of the graphical effect(s) 104C (e.g., applying a dark hue to a rain animation at night time).

[0023] Display module 106 of mobile device 102 may then generate the layered environmental effect wallpaper of wallpaper image 101B based on the plurality of image segments (e.g., objects or layers featured in wallpaper image 101A) and the one or more graphical effect layers that include graphical effect(s) 104C, as described herein. Display module 106 may generate and / or display wallpaper image 101B (e.g., using one or more UI component(s) 140) based on wallpaper image 101A and the determined environmental conditions in order to provide the user with a more efficient way to see the current or predicted environmental conditions for a particular location without having to navigate to an application or sacrifice their ability to view their selected wallpaper image 101A to weather widgets. Instead, the user may merely look at the display screen of mobile device 102 andDocket No.: 1333-823WO01 instantly understand the relevant environmental conditions based on the context of wallpaper image 101B.

[0024] In order to conserve resources, the layered environmental effect of wallpaper image 101B may only be displayed at certain times and for certain periods of time. For example, mobile device 102 may monitor various sensors and input devices associated with mobile device 102 to determine that the user is viewing their phone for the first time after waking up, has not viewed their phone screen in over X hours, has not opened their weather app or other environmental monitoring app since the environmental conditions have changed, etc. Wallpaper image 101B may then be displayed featuring the layered environmental effect on wallpaper image 101A for a few minutes, until the user turns off their screen or opens an application, until the environmental conditions change, until the user provides input indicating they wish the effect to end (e.g., a “swipe away” gesture on a touchscreen of mobile device 102), and / or according to a user’s preferred display settings. Once wallpaper image 101B is no longer to be displayed, wallpaper image 101A is displayed again and / or another wallpaper image 101B is displayed (e.g., corresponding to changed environmental conditions).

[0025] In some examples, the layered environmental effect of wallpaper image 101B may be interactive via user input provided to one or more of the UI component(s) 140 of mobile device 102. For example, a user may perform one or more gestures on a touch screen of mobile device 102 to move the graphical effect(s) 104C (e.g., dragging raindrops, wiping away fog, etc.). As another example, graphical effect (s) 104C may change orientations, directions, or velocities based on sensor values of mobile device 102 (e.g., snow may fly around when mobile device 102 is shaken, raindrops may flow in the opposite direction when mobile device 102 is held upside down, etc.).

[0026] In some examples, a user of mobile device 102 may further be able to select certain environmental conditions or their respective graphical effects to be displayed according to user preferences. For example, mobile device 102 may include one or more application(s) 108 that feature selectable weather effects for overlaying on wallpaper image 101A of mobile device 102. When mobile device 102 and / or one of the application(s) 108 receives an input selecting snow, for instance, mobile device 102 may generate a layered environmental effect wallpaper 101B based on wallpaper image 101A and the selected snow effect as if mobile device 102 had determined the snow effect based on environmental conditions.

[0027] FIG.2 is a block diagram illustrating a mobile device for generating and displaying layered environmental effect wallpaper images, in accordance with techniques of thisDocket No.: 1333-823WO01 disclosure. Mobile device 202 may be one example of any mobile device mentioned herein (e.g., mobile device 102). FIG.2 illustrates only one particular example of mobile device 202, and many other examples of mobile device 202 may be used in other instances and may include a subset of the components included in example mobile device 202 or may include additional components not shown in FIG.2.

[0028] As shown in FIG.2, mobile device 202 may include one or more processors 238 (“processors 238”), one or more user interface components 240 (“UI component 240”), one or more input components 242 (“input component(s) 242”), one or more output components 242 (“output component(s) 242”), one or more communications units 246 (“communication unit(s) 246”), one or more location units 248 (“location unit(s) 248”), one or more communication channels 252 (illustrated as “COMM. CHANNELS 252” in FIG.2, hereinafter “comm channels 252”), and one or more storage components 250 (“storage component(s) 250”). As also shown in FIG.2, storage components 250 may include an operating system 204 (“OS 204”), applications 208 (“application(s) 208”), and environmental data repository 203. OS 204 may include a user interface component module 206A (“Display module 206A”), a location module 206B, a weather module 206C, and an image processing module 206D. Although display module 206A, location module 206B, weather module 206C, and image processing module 206D are depicted as components of OS 204, it should be appreciated that, in some examples, any of or all of modules 206A-D may not be a part of OS 204 and may instead be included in application(s) 208. For example, OS 204 may perform any of the processes described herein with respect to modules 206A-D by requesting that modules 206A-D of application(s) 208 perform the processes as described herein.

[0029] In some examples, UI component 240 may be a presence-sensitive display configured to detect input (e.g., touch and non-touch input) from a user of respective mobile device 202. UI component 240 may output information to a user in the form of a UI, which may be associated with functionality provided by mobile device 202. Mobile device 202 may output such UIs that may be associated with computing platforms, operating systems, applications, and / or services executing at or accessible from mobile device 202 (e.g., electronic message applications, chat applications, Internet browser applications, mobile or desktop operating systems, social media applications, electronic games, menus, and other types of applications).

[0030] Processors 238 may implement functionality and / or execute instructions within mobile device 202. For example, processors 238 may receive and execute instructions that provide the functionality of applications 208, and OS 204. These instructions executed by processors 238 may cause mobile device 202 to store and / or modify information withinDocket No.: 1333-823WO01 storage components 250 during program execution. Processors 238 may execute instructions of modules 206A-D and applications 208 to perform one or more operations. That is, modules 206A-D, applications 208, and OS 204 may be operable by processors 238 to perform various functions described herein.

[0031] Communication units 246 of mobile device 202 may communicate with one or more external devices via one or more wired and / or wireless networks by transmitting and / or receiving network signals on the one or more networks. Examples of communication units 246 include a network interface card (e.g., an Ethernet card), an optical transceiver, a radio frequency transceiver, a GPS receiver, or any other type of device that can send and / or receive information. Other examples of communication units 246 may include short wave radios, cellular data radios, satellite data radios, wireless network radios, as well as universal serial bus (USB) controllers.

[0032] Input components 242 of mobile device 202 may receive input. Examples of input are tactile, audio, and video input. Input components 242 of mobile device 202, in one example, includes a presence-sensitive display, a fingerprint sensor, touch-sensitive screen, mouse, keyboard, voice responsive system, video camera, microphone or any other type of device for detecting input from a human or machine.

[0033] Input components 242 may include one or more sensors. Numerous examples of sensors exist and include any input component configured to obtain environmental information about the circumstances surrounding mobile device 202 and / or physiological information that defines the activity state and / or physical well-being of a user of mobile device 202. In some examples, a sensor may be an input component that obtains physical position, movement, and / or location information of mobile device 202. For instance, sensors may include one or more location sensors (e.g., GNSS components, Wi-Fi components, cellular components), one or more temperature sensors, one or more motion sensors (e.g., multi-axial accelerometers, gyros), one or more pressure sensors (e.g., barometer), one or more ambient light sensors, and one or more other sensors (e.g., microphone, camera, infrared proximity sensor, hygrometer, and the like).

[0034] Output components 242 of mobile device 202 may generate one or more outputs. Examples of outputs are tactile, audio, and video output. Output components 242 of mobile device 202, in one example, includes a presence-sensitive display, sound card, video graphics adapter card, speaker, liquid crystal display (LCD), or any other type of device for generating output to a human or machine.Docket No.: 1333-823WO01

[0035] Comm channels 252 may interconnect each of the components 238, 240, 242, 242, 246, 248, and 250 for inter-component communications (physically, communicatively, and / or operatively). In some examples, comm channels 252 may include a system bus, a network connection, an inter-process communication data structure, or any other method for communicating data.

[0036] Storage components 250 of mobile device 202 may store information for processing during operation of mobile device 202 (e.g., mobile device 202 may store applications 208, OS 204, and modules 206A-D during execution at mobile device 202). In some examples, storage components 250 may include a temporary memory. Thus some of storage components 250 of mobile device 202 may be configured for short-term storage of information as volatile memory and therefore not retain stored contents if powered off. Examples of volatile memories include random access memories (RAM), dynamic random- access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.

[0037] Storage components 250 may include one or more non-transitory computer-readable storage media. Storage components 250 may be configured to store larger amounts of information than volatile memory. Storage components 250 may further be configured for long-term storage of information as non-volatile memory space and retain information after power on / off cycles. Examples of non-volatile memories include magnetic hard disks, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Storage components 250 may store program instructions and / or information associated with applications 208, OS 204, and modules 206A-D.

[0038] As shown in the example of FIG.2, mobile device 202 includes OS 204. OS 204 may provide an execution environment for one or more modules, such as display module 206A, location module 206B, weather module 206C, image processing module 206D, and one or more applications, such as applications 208. OS 204 may represent a multi-threaded operating system or a single-threaded operating system with which display module 206A, location module 206B, weather module 206C, image processing module 206D, and applications 208 may interface to access hardware of mobile device 202. OS 204 may include a kernel that facilitates access to the underlying hardware of mobile device 202, where kernel may present a number of different interfaces (e.g., application programmer interfaces (APIs)) that display module 206A, location module 206B, weather module 206C,Docket No.: 1333-823WO01 image processing module 206D, and applications 208 may invoke to access the underlying hardware of mobile device 202.

[0039] Display module 206A of mobile device 202 may control the function of the input devices and / or output devices included in mobile device 202, such as input component(s) 242 and / or output component(s) 242 using various technologies. In some examples, display module 206A may control an input device that includes a presence-sensitive input component, such as a resistive touchscreen, a surface acoustic wave touchscreen, a capacitive touchscreen, a projective capacitance touchscreen, a pressure sensitive screen, an acoustic pulse recognition touchscreen, or another presence-sensitive display technology. In some examples, display module 206A may control an input device using microphone technologies, infrared sensor technologies, or other input device technology for use in receiving user input. Display module 206A may function as an output (e.g., display) device using any one or more display components, such as a liquid crystal display (LCD), microLED, miniLED, dot matrix display, light emitting diode (LED) display, organic light-emitting diode (OLED) display, e- ink, or similar monochrome or color display capable of outputting visible information to a user of mobile device 202. In some examples display module 206A may control an output device configured to present output to a user using any one or more display devices, speaker technologies, haptic feedback technologies, or other output device technology for use in outputting information to a user.

[0040] Location module 206B of mobile device 202 may monitor the location of the mobile device 202. Location module 206B may control various computing components included in mobile device 202 that are configured to process and store location data received via one or more location components or communication components of the mobile device 202. For example, location module 206B may control location unit(s) 248 in order to obtain and track over time location data of mobile device 202 according to one or more location determination protocols of location module 206B, for use by OS 204 and / or applications 208 of mobile device 202 as described herein.

[0041] For example, in some implementations, location module 206B may perform constant or periodic monitoring of the location of mobile device 202 in order to assist the user in tracking their fitness or workout goals, or to provide necessary location information to OS 204 or various applications 208 (e.g., a weather application) installed on mobile device 202. In some implementations, location module 206B may determine the location of mobile device 202 based on location data received from location unit(s) 248 and / or communication unit(s) 246. For example, location module 206B may determine the location of mobile deviceDocket No.: 1333-823WO01 202 based on data received from one or more nearby mobile devices via one or more network link(s), location data received from satellites via the GNSS system, or location data received from some other computing device over a network connection.

[0042] Weather module 206C of mobile device 202 may monitor environmental data and / or conditions relevant to particular locations associated with the mobile device 202. Although the term “weather module” is used herein, it should be appreciated that weather module 206C may be used to monitor any environmental data and / or conditions relevant to those particular locations, as described elsewhere herein. Weather module 206C may control various computing components included in mobile device 202 that are configured to process and store environmental data received via one or more communication units 246 and / or one or more applications 208 of the mobile device 202. For example, weather module 206C may control communication unit(s) 246 in order to request environmental data from an environmental monitoring service server for a current location of mobile device 202 determined by location module 206B. Weather module 206C may obtain the environmental data from one or more environmental monitoring service servers, via one or more network links and communication unit(s) 246, and / or from one or more application(s) 208 (e.g., a weather or other application) installed on mobile device 202. Weather module 206C may store this environmental data in environmental data repository 203 for use by one or more other modules 206A,B,D and / or one or more application(s) 208 installed on mobile device 202.

[0043] Image processing module 206D may orchestrate the generation of the layered environmental effect wallpaper as described herein. Responsive to detecting a triggering event, image processing module 206D determines environmental conditions based on the environmental data stored in environmental data repository and / or received from weather module 206C, determines appropriate graphical effects, and applies these effects to a plurality of image segments created from the current wallpaper image on mobile device 202, as described herein. Image processing module 206D may perform one or more of the processes or control one or more of the machine learning models mentioned elsewhere herein. For example, image processing module 206D may segment wallpaper images, determine depicted environmental conditions, determine color grading, etc. in order to generate the layered environmental effect wallpaper. Detection of triggering events may include detecting particular user inputs and / or determining that certain criteria have been met. Example triggering events may include a user turning their mobile device screen on after it was previously off, the user doing so after a certain period of time since they turned their screenDocket No.: 1333-823WO01 off, the user turning an off mobile device on, the user turning their mobile device screen on for the first time since waking, the user gazing at their mobile device screen, the user gazing at their mobile device screen since environmental conditions last changed, updates in environmental conditions indicated by the environmental data, current or predicted battery levels, user selections within a relevant application or of settings within mobile device 202, or any other events or criteria related to user inputs or obtained environmental data. For example, image processing module 206D may update the layered environmental effect wallpaper on mobile device 202 less often or may fade out the layered environmental effect wallpaper more quickly (e.g., returning the display to the default wallpaper image) when environmental conditions are predicted to stay the same for a threshold period of time and when battery levels of mobile device 202 are predicted to run out before the user can charge mobile device 202.

[0044] FIG.3 illustrates an example of generating image segments from a wallpaper image and selecting graphical effects based on environmental conditions, in accordance with techniques described herein. The mobile device 302 of FIG.3 may be any of the mobile devices or computing devices mentioned herein. For example, mobile device 302 may be the same mobile device 102, 202 as in FIGS.1 and / or 2. For example, mobile device 302 will be discussed with respect to the components of FIG.2, although it should be understood that any of the other mobile devices or components mentioned herein capable of performing the same processes may additionally or alternatively be used.

[0045] As depicted in the example of FIG.3, mobile device 302 may be configured to display wallpaper image 301 as a background image or other wallpaper image on mobile device 302, as described herein. Mobile device 302 may also include a environmental data repository 303 that stores environmental data associated with a particular location, for example a current location of mobile device 302 determined by location module 206B or a location that a user manually selected for a weather application 208 of mobile device 302 to monitor. The environmental data may be received by mobile device 302 from one or more applications 208 installed on mobile device 302 and / or from communication with a server that includes environmental data, and mobile device 302 may monitor the environmental data, including monitoring for changes in the environmental conditions indicated by the environmental data.

[0046] As depicted in FIG.3, wallpaper image 301 may be applied as input to machine learning model 304 to generate a plurality of image segments 310 as output. For example, image processing module 206D may apply wallpaper image 301 as input to machine learningDocket No.: 1333-823WO01 model 304 to receive the responsive output, as described herein. Machine learning model 304 may be any machine learning model, or series of machine learning models, trained to accept images as inputs and provide image segments as outputs in response. Machine learning model 304 may segment wallpaper image 301 using any one or more of a variety of techniques and may take on a variety of forms including, but not limited to, a convolutional neural network (“CNN”). For example, machine learning model 304 may be trained to detect objects and / or visual phenomena, in which case image segments 310 may be indicated by bounding shapes such as bounding boxes. As another example, machine learning model 304 may be trained to perform image segmentation, in which case image segments 310 may include pixel-wise annotations (or pixel-region-annotations). In some examples, machine learning model 304 may be trained to determine depths of certain objects captured in an image (e.g., foreground objects, background objects, and objects in between on multiple layers), and image segments 310 may include depth or layer information. For example, the plurality of image segments may include a foreground layer and / or one or more foreground objects, a background layer and / or one or more background objects, and one or more layers and / or objects at depths between the foreground and background layers / objects.

[0047] Mobile device 302 may include environmental data repository 303 in local memory on mobile device 302, and / or environmental data repository 303 may be a remote repository accessible by mobile device 302 over one or more networks. The environmental data included in environmental data repository 303 may include environmental data stored in association with a weather service / application or other environmental monitoring service / application available on mobile device 302. The environmental data may include predicted or observed environmental data associated with particular location(s), such as a current, recent, or predicted location of mobile device 302 as determined by location module 206B or a location selected by the user. The environmental data of environmental data repository 303 may include any data available to mobile device 302 from an associated weather service / application or other environmental monitoring service / application, including but not limited to information on current or predicted: temperature, humidity, precipitation levels / frequency, UV index, wind speed, wind direction, wind chill / heat index (i.e., the “feels like” temperature), visibility conditions, time of day, position of the sun or moon in the sky, indications of natural disasters or other environmental phenomena (e.g., tornados, eclipses, etc.), and any other data relevant to environmental conditions obtainable by mobile device 102. Mobile device 302 may obtain the environmental data from environmental data repository 303 in order to determine environmental conditions 305, as described herein.Docket No.: 1333-823WO01

[0048] environmental conditions 305 may include observations determined based on instances (or clusters of instances) of environmental data obtained from environmental data repository 303. In some examples, weather module 206C of mobile device 302 may have access to one or more lookup tables that may associate environmental conditions 305 with environmental data. For example, weather module 206C of mobile device 302 may access a lookup table that associates labels such as “cold”, “snowy”, “twilight”, and “dreary” with similar instances of environmental data. In some examples, one of the machine learning models mentioned herein, or another machine learning model, included in or accessible to mobile device 302 may be used by weather module 206C of mobile device 302 to process the environmental data to determine environmental conditions 305. environmental conditions 305 may include any conditions indicated by the environmental data obtained from environmental data repository 303 that may be relevant to generating the layered environmental effect wallpaper on wallpaper image 301.

[0049] Image processing module 206D of mobile device 302 may use determined environmental conditions 305 to select or generate one or more graphical effects 307 for inclusion in the graphical effect layers of the layered environmental effect wallpaper, as described herein. Image processing module 206D of mobile device 302 can select or generate graphical effects 307, determine different positions and / or depths for graphical effects 307 within the generated wallpaper, and then generate one or more graphical effect layers that depict a contextually appropriate environmental effect for the user’s current or chosen location (or other location determined by location module 206B). The selection or generation of specific graphical effects 307 may include referencing one or more lookup tables associating environmental conditions 305 with corresponding graphical effects 307, and / or utilizing a machine learning model to generate graphical effects 307 dynamically based on determined environmental conditions 305. In some examples, mobile device 302 may also use one or more of the plurality of image segments 310 generated based on wallpaper image 301 and graphical effects 307 to generate a graphical effect layer that combines elements of image segment(s) 310 and graphical effects 307. For example, mobile device 302 may select and position a snow accumulation animation on the same layer as a background entity, such that snow slowly accumulates on top of the background entity. As another example, mobile device 302 may generate a wet hair and / or wind-blown hair animation that features hair strands matching a photo subject of wallpaper image 301 looking wet or as if they are blowing in the wind. As yet another example, mobile device 302 may select a graphical effectDocket No.: 1333-823WO01 that features sunglasses, a hat, or an umbrella for placing on or directly above a head of the photo subject of wallpaper image 301.

[0050] FIG.4 illustrates an example of generating a layered environmental effect wallpaper based on wallpaper image segments and graphical effect layer(s), in accordance with techniques described herein. Wallpaper 401 of FIG.4 may be generated and / or displayed by any of the mobile devices or computing devices mentioned herein. For example, mobile device 202 as described with respect to FIG.2 may generate and / or display wallpaper 401. Image segments 410 may include the plurality of image segments 310 generated by machine learning model 304 of FIG.3. environmental conditions 405 and graphical effect layers 407 may correspond to environmental conditions 305 and the one or more graphical effect layers generated based on graphical effect layer(s) 407, respectively, which have been generated as discussed with respect to FIG.3. Moreover, while the examples of FIG.4 are described with respect to a machine learning model, it should be appreciated that various other components of mobile device 202 may perform one or more of the processes described herein without using a machine learning model, as described herein.

[0051] In the example depicted in FIG.4, the plurality of image segments 410 and the generated graphical effect layer(s) 407 are applied as inputs to machine learning model 404. For example, image processing module 206D of mobile device 202 may apply the plurality of image segments 410 and the generated graphical effect layer(s) 407 as input(s) to machine learning model 404. Machine learning model 404 may be trained to accept these inputs and to generate, in response, the layered environmental effect wallpaper 401 including the plurality of image segments 410, which may correspond to various image layers, and the graphical effect layer(s) 407 at various depths in relation to one another.

[0052] In some examples, as depicted in FIG.4, environmental conditions 405 may additionally be applied as input(s) to machine learning model 404 by image processing module 206A and / or weather module 206C of mobile device 202, and machine learning model 404 may be trained to generate one or more additional graphical effect layers that corresponds to color gradings, and then to position that color grading layer at a particular depth when generating the layered environmental effect wallpaper 401. Machine learning model 404 may be trained to generate the color grading layer(s) based at least on the determined environmental conditions 405. In some examples, image processing module 206D of mobile device 202 may also generate the color grading layer(s) based on one or more of the plurality of image segments 410 and / or one or more of the other generated graphical effect layer(s) 407. In other examples, the color grading layers may be generated withoutDocket No.: 1333-823WO01 taking into account the plurality of image segments 410 and / or the generated graphical effect layer(s) 407. For example, mobile device 202 may include one or more machine learning models, such as machine learning model 404, trained to generate a particular color grading layer to include as the topmost layer on the layered environmental effect wallpaper image 401. In examples in which machine learning model 404 is not used, image processing module 206D of mobile device 202 may have access to one or more lookup tables that associate environmental conditions 405 and image features included in image segments 410 with color schemes. In such examples, image processing module 206D of mobile device 202 may cross- reference the lookup table(s) to generate a color grading layer to apply to the layered environmental effect wallpaper image 401. Subsequent to generating the color grading layer, machine learning model 404 and / or image processing module 206D of mobile device 202 may then position the one or more color grading layer(s) behind, on top of, or between one or more of the plurality of image segments 410 and / or one or more of the graphical effect layer(s) 407. The generated color grading layers may correlate with the determined environmental conditions 405 (e.g., blue tint for rain, dark tint for night, etc.). Machine learning model 404 and / or image processing module 206D of mobile device 202 may also generate one or more color grading layers based on image features detected in one or more of the plurality of image segments 410 and / or based on features of graphical effect layer(s) 407. For example, machine learning model 404 may determine to apply a gray tint to image segments 410 and a blue tint to graphical effect layer(s) 407 based on environmental conditions 405 indicating freezing but wet sleeting conditions, and / or based on a difference between a current value of an image segment versus a desired value (e.g., turning sunny yellow skies to blue-gray).

[0053] As an illustrative example of the combination of processes described with respect to FIGS.2-4, consider the following processes as performed by a mobile device (e.g., mobile device 202, 302). In this example, user’s default wallpaper image (e.g., wallpaper image 401) that includes a photo of the user on a sunny day may be applied to an image segmentation machine learning model (e.g., machine learning model 304) by image processing module 206D to generate a plurality of image segments (e.g., image segments 310, 410) corresponding to various layers and objects of the default wallpaper image (301). environmental data for a current location of the mobile device (302, 402) may be retrieved (e.g., from environmental data repository 303) and processed by weather module 206C to determine the current environmental conditions relevant to the current location (e.g., environmental conditions 305, 405). Image processing module 206D selects or generatesDocket No.: 1333-823WO01 particular static and / or dynamic (i.e., still or moving) graphical effects (e.g., graphical effect(s) 307) that are associated with the current environmental conditions (305, 405) using a lookup table or machine learning model (e.g., machine learning model 304) and image processing module 206D generates one or more graphical effect layer(s) (e.g., graphical effect layer(s) 407) based at least on the graphical effects (307). Image processing module 206D applies the plurality of image segments (310, 410), the current environmental conditions (305, 405), and the graphical effect layer(s) (407) as inputs to one or more machine learning model(s) (e.g., machine learning model 404) that generate, as responsive output, the layered environmental effect wallpaper image 401 that includes: a gray layer covering the sunny sky in the background muting the blues and yellows of the sky; a cloudy / snowy animation on top of the background sky layer; several layers of snow animations falling behind, in front of, and in-line with the user in the photo; snow animations bouncing off the user in some places and accumulating in a pile on the user in other places on in the photo; and a topmost blue tint layer applied on top of everything giving everything a frosty feel. Thus, color grading may be used to transform the disparate elements included in image segments 410 and graphical effect layer(s) 407 into a coherent illustration of environmental conditions 405 that is integrated with a user’s selected wallpaper image (e.g., wallpaper image 301, 401). Once the layered environmental effect wallpaper 401 has been generated, display module 206A then causes one or more display components, such as one or more output component(s) 242 or UI component(s) 240, of the mobile device to display the layered environmental effect wallpaper 401 for presentation to the user.

[0054] FIG.5 is a flowchart illustrating an example operation of a mobile device in accordance with techniques of this disclosure. Although primarily described with respect to mobile device 202 of FIG.2, it should be understood that the techniques illustrated by FIG.5 may be applied to any of the mobile devices disclosed herein. Process 500 of FIG.5 may be performed by image processing module 206D or any combination of modules 206A-D of FIG.2, which shall from hereon be referred to as “the system”. While the steps of process 500 are discussed in a particular order, this order is not meant to be limiting and it should be appreciated that various steps may be reordered, omitted, or added in accordance with techniques described herein. Moreover, while process 500 is discussed with respect to the current location of mobile device 202, it should be appreciated that any location associated with mobile device 202, such as a location manually selected by a user on a weather application 208 of mobile device 202, may be used by process 500 instead.Docket No.: 1333-823WO01

[0055] The system begins process 500 at block 502 by determining the environmental conditions relevant to a particular location associated with mobile device 202. The particular location can include, for example, the current location of mobile device 202 or a location manually set by the user (e.g., within a weather application 208). The system (e.g., weather module 206C) may, for example, process the environmental data included in environmental data repository 203, which was obtained in association with the particular location, in order to determine the environmental conditions relevant to the particular location associated with mobile device 202. In some examples, the system may communicate with a remote server associated with a weather or other environmental monitoring service to obtain the environmental data and / or indications of the environmental conditions of the particular location associated with mobile device 202.

[0056] At block 504, the system (e.g., image processing module 206D) applies the current wallpaper image of mobile device 202 as input to one or more machine learning models to receive a plurality of image segments as output(s), as described herein. The generated plurality of image segments may include, for example, image layers that correspond to groups of pixels included in the current wallpaper image associated with particular foreground object(s), particular background object(s), and / or any number of objects at various depths. In some examples, the generated image segments may be associated with labels indicating particular color schemes, recognized objects, recognized environmental conditions, or other contextual details determined to be indicated by the current wallpaper image. Thus, the plurality of image segments may include various layers / depths of objects and regions. Thus, at block 504, the current wallpaper image is segmented into various image layers, which include at least a background image layer and a foreground image layer.

[0057] At block 506, the system (e.g., image processing module 206D) generates one or more graphical effect layers based on the environmental conditions determined at block 502. The one or more graphical effect layers are generated based on the environmental conditions such that they include various animated environmental effects that match the current environmental conditions at the particular location. In some examples, the graphical effect layers may be generated based on at least one of the plurality of image segments such that they may interact with objects and regions included in the current wallpaper image. For example, the system may generate a first graphical effect layer to apply on top of a background layer of the current wallpaper, such as a snow fall animation selected to go over the sky shown in the background of the current wallpaper image. As another example, the system may determine angles or positions in which the snow animations should bounce off ofDocket No.: 1333-823WO01 recognized objects in the current wallpaper image. The generation of the graphical effect layers may include referencing one or more lookup tables associating environmental conditions with corresponding graphical effects, and / or utilizing a machine learning model to generate graphical effects dynamically based on determined environmental conditions, as described herein.

[0058] At block 508, the system (e.g., image processing module 206D) generates the layered environmental effect wallpaper including at least the foreground image layer, the background image layer, and the one or more graphical effect layers. The system may generate the layered environmental effect wallpaper to include the one or more graphical effect layers at one or more depths, as described herein. For example, a graphical effect layer may be placed in front of the foreground image layer (e.g., as a topmost layer) and / or between the foreground image layer and the background image layer.

[0059] At block 510, the system (e.g., display module 206A) causes a display of mobile device 202 (e.g., output component(s) 242f) to display the layered environmental effect wallpaper, as described herein. In some examples, the system may display the layered environmental effect wallpaper responsive to receiving particular user input, including any of the triggering user inputs discussed herein, including the criteria for user inputs to be considered triggering inputs. For example, display module 206A may cause the display of the layered environmental effect wallpaper based on detecting that the user has turned the screen of mobile device 202 on for the first time since the monitored environmental conditions have changed. As another example, display module 206A may cause the display based on detecting that the user is turning the screen of mobile device 202 on for the first time for the day.

[0060] This disclosure includes the following examples.

[0061] Example 1: A method, comprising: determining, by a computing device, environmental conditions relevant to a particular location associated with the computing device; segmenting, by the computing device, a wallpaper image of the computing device into image layers, wherein the layers include at least a background image layer and a foreground image layer; generating, by the computing device, one or more graphical effect layers based on the environmental conditions; generating, by the computing device, a layered environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more graphical effect layers; and presenting, by the computing device and on a display associated with the computing device, the layered environmental effect wallpaper.Docket No.: 1333-823WO01

[0062] Example 2: The method of example 1, wherein generating the layered environmental effect wallpaper includes positioning at least one of the one or more graphical effect layers at a depth between the background image layer and the foreground image layer.

[0063] Example 3: The method of any of examples 1 or 2, wherein segmenting the wallpaper image of the computing device into image layers includes segmenting the wallpaper image based on one or more objects included in the wallpaper image, wherein at least one of the image layers corresponds to the one or more objects.

[0064] Example 4: The method of example 3, wherein at least one of the one or more graphical effect layers includes a weather animation graphical effect in which animated environmental effects interact with at least one of the one or more objects included in the wallpaper image.

[0065] Example 5: The method of example 3, further comprising: identifying, by the computing device, the one or more objects included in the wallpaper by at least: providing the wallpaper image as input to a machine learning model; and receiving, from the machine learning model, an indication of the one or more objects included in the wallpaper.

[0066] Example 6: The method of any of examples 1-5, wherein at least one of the one or more graphical effect layers includes color grading that corresponds to the environmental conditions.

[0067] Example 7: The method of any of examples 1-6, further comprising: determining, by the computing device, depicted environmental conditions included in the wallpaper image of the computing device, wherein at least one of the one or more graphical effect layers is generated further based on the depicted environmental conditions.

[0068] Example 8: The method of any of examples 1-7, further comprising: responsive to determining a change in environmental conditions relevant to the particular location associated with the computing device: generating, by the computing device, one or more different graphical effect layers based on the changed environmental conditions; generating, by the computing device, a different layered environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more different graphical effect layers; and outputting, by the computing device and for display by a display associated with the computing device, the different layered environmental effect wallpaper.

[0069] Example 9: A system including one or more processors; and a non-transitory computer-readable medium encoded with instructions that, when executed by the one or more processors, cause the one or more processors to: determine environmental conditions relevant to a particular location associated with a computing device; segment a wallpaper image of theDocket No.: 1333-823WO01 computing device into image layers, wherein the layers include at least a background image layer and a foreground image layer; generate one or more graphical effect layers based on the environmental conditions; generate a layered environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more graphical effect layers; and output, for display, the layered environmental effect wallpaper.

[0070] Example 10: The system of example 9, wherein the instructions that cause the one or more processors to generate the layered environmental effect wallpaper further include instructions that cause the one or more processors to position at least one of the one or more graphical effect layers at a depth between the background image layer and the foreground image layer.

[0071] Example 11: The system of any of examples 9-10, wherein the instructions that cause the one or more processors to segment the wallpaper image of the computing device into image layers further include instructions that cause the one or more processors to segment the wallpaper image based on one or more objects included in the wallpaper image, wherein at least one of the image layers corresponds to the one or more objects.

[0072] Example 12: The system of example 11, wherein at least one of the one or more graphical effect layers includes a weather animation graphical effect in which animated environmental effects interact with at least one of the one or more objects included in the wallpaper image.

[0073] Example 13: The system of any of examples 9-12, wherein at least one of the one or more graphical effect layers includes color grading that corresponds to the environmental conditions.

[0074] Example 14: The system of any of examples 9-13, wherein the instructions further cause the one or more processors to: determine depicted environmental conditions included in the wallpaper image of the computing device, wherein the instructions cause the one or more processors to generate the at least one of the one or more graphical effect layers based on the depicted environmental conditions.

[0075] Example 15: The system of any of examples 9-14, wherein the instructions further cause the one or more processors to: responsive to determining a change in environmental conditions relevant to the particular location associated with the computing device: generate one or more different graphical effect layers based on the changed environmental conditions; generate a different layered environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more different graphical effect layers; and output, for display, the different layered environmental effect wallpaper.Docket No.: 1333-823WO01

[0076] Example 16: A non-transitory computer-readable medium encoded with computer- executable instructions that, when executed by one or more processors, cause the one or more processors to: determine environmental conditions relevant to a particular location associated with a computing device; segment a wallpaper image of the computing device into image layers, wherein the layers include at least a background image layer and a foreground image layer; generate one or more graphical effect layers based on the environmental conditions; generate a layered environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more graphical effect layers; and output, for display, the layered environmental effect wallpaper.

[0077] Example 17: The non-transitory computer-readable medium of example 16, wherein the instructions that cause the one or more processors to generate the layered environmental effect wallpaper further include instructions that cause the one or more processors to position at least one of the one or more graphical effect layers at a depth between the background image layer and the foreground image layer.

[0078] Example 18: The non-transitory computer-readable medium of any of examples 16- 17, wherein the instructions that cause the one or more processors to segment the wallpaper image of the computing device into image layers further include instructions that cause the one or more processors to segment the wallpaper image based on one or more objects included in the wallpaper image, wherein at least one of the image layers corresponds to the one or more objects.

[0079] Example 19: The non-transitory computer-readable medium of example 18, wherein at least one of the one or more graphical effect layers includes a weather animation graphical effect in which animated environmental effects interact with at least one of the one or more objects included in the wallpaper image.

[0080] Example 20: The non-transitory computer-readable medium of any of examples 16- 19, wherein at least one of the one or more graphical effect layers includes color grading that corresponds to the environmental conditions.

[0081] Example 21: A computing device comprising means for performing any combination of the methods of examples 1-8.

[0082] By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other storage medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-Docket No.: 1333-823WO01 readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage mediums and media and data storage media do not include connections, carrier waves, signals, or other transient media, but are instead directed to non- transient, tangible storage media. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable medium.

[0083] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and / or software modules. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0084] The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of ICs (e.g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and / or firmware.

[0085] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

Docket No.: 1333-823WO01 WHAT IS CLAIMED IS:

1. A method, comprising: determining, by a computing device, environmental conditions relevant to a particular location associated with the computing device; segmenting, by the computing device, a wallpaper image of the computing device into image layers, wherein the layers include at least a background image layer and a foreground image layer; generating, by the computing device, one or more graphical effect layers based on the environmental conditions; generating, by the computing device, a layered environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more graphical effect layers; and presenting, by the computing device and on a display associated with the computing device, the layered environmental effect wallpaper.

2. The method of claim 1, wherein generating the layered environmental effect wallpaper includes positioning at least one of the one or more graphical effect layers at a depth between the background image layer and the foreground image layer.

3. The method of any of claims 1 or 2, wherein segmenting the wallpaper image of the computing device into image layers includes segmenting the wallpaper image based on one or more objects included in the wallpaper image, wherein at least one of the image layers corresponds to the one or more objects.

4. The method of claim 3, wherein at least one of the one or more graphical effect layers includes a weather animation graphical effect in which animated environmental effects interact with at least one of the one or more objects included in the wallpaper image.

5. The method of claim 3, further comprising: identifying, by the computing device, the one or more objects included in the wallpaper by at least: providing the wallpaper image as input to a machine learning model; and receiving, from the machine learning model, an indication of the one or more objects included in the wallpaper.Docket No.: 1333-823WO01 6. The method of any of claims 1-5, wherein at least one of the one or more graphical effect layers includes color grading that corresponds to the environmental conditions.

7. The method of any of claims 1-6, further comprising: determining, by the computing device, depicted environmental conditions included in the wallpaper image of the computing device, wherein at least one of the one or more graphical effect layers is generated based on the depicted environmental conditions.

8. The method of any of claims 1-7, further comprising: responsive to determining a change in environmental conditions relevant to the particular location associated with the computing device: generating, by the computing device, one or more different graphical effect layers based on the changed environmental conditions; generating, by the computing device, a different layered environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more different graphical effect layers; and outputting, by the computing device and for display by a display associated with the computing device, the different layered environmental effect wallpaper.Docket No.: 1333-823WO01 9. A system, comprising: one or more processors; and a non-transitory computer-readable medium encoded with instructions that, when executed by the one or more processors, cause the one or more processors to: determine environmental conditions relevant to a particular location associated with a computing device; segment a wallpaper image of the computing device into image layers, wherein the layers include at least a background image layer and a foreground image layer; generate one or more graphical effect layers based on the environmental conditions; generate a layered environmental effect wallpaper including at least the background image layer, the foreground image layer, and the one or more graphical effect layers; and output, for display, the layered environmental effect wallpaper.

10. The system of claim 9, wherein the instructions that cause the one or more processors to generate the layered environmental effect wallpaper further include instructions that cause the one or more processors to position at least one of the one or more graphical effect layers at a depth between the background image layer and the foreground image layer.

11. The system of claims 9 or 10, wherein the instructions that cause the one or more processors to segment the wallpaper image of the computing device into image layers further include instructions that cause the one or more processors to segment the wallpaper image based on one or more objects included in the wallpaper image, wherein at least one of the image layers corresponds to the one or more objects.

12. The system of claim 11, wherein at least one of the one or more graphical effect layers includes a weather animation graphical effect in which animated environmental effects interact with at least one of the one or more objects included in the wallpaper image.

13. The system of any of claims 9-12, wherein at least one of the one or more graphical effect layers includes color grading that corresponds to the environmental conditions.Docket No.: 1333-823WO01 14. A computing device comprising means for performing any combination of the methods of claims 1-8.

15. A computer program product encoded with computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform any of the methods of claims 1-8.

Images