Optimized split artificial intelligence (AI) in smart glasses

Abstract

Systems and techniques are described for artificial intelligence (AI) assistance. For example, a computing device associated with a user can obtain, from sensor(s), sensor data associated with a scene. The computing device can determine, based on the sensor data, one or more contexts for the scene. The computing device can receive a query based on user input from the user. The computing device can generate a prompt based on the query and the one or more contexts. The computing device can determine an AI assistance processing strategy from a plurality of AI assistance processing strategies based on the one or more contexts, the prompt, one or more device properties, or a combination thereof. The computing device can process, based on the AI assistance strategy, the query to generate an answer to the query.

Description

PATENTQualcomm Ref. No. 2501466WO1OPTIMIZED SPLIT ARTIFICIAL INTELLIGENCE (Al) IN SMART GLASSES FIELD

[0001] The present disclosure generally relates to artificial intelligence (Al) assistance. For example, aspects of the present disclosure relate to system designs and methods for optimized split artificial intelligence (Al) in smart glasses.BACKGROUND

[0002] Electronic devices are increasingly equipped with camera hardware to capture images and / or videos for consumption. For example, a computing device can include a camera (e.g., a mobile device such as a mobile telephone or smartphone including one or more cameras) to allow the computing device to capture a video or image of a scene, a person, an object, etc. The image or video can be captured and processed by the computing device (e.g., a mobile device, an IP camera, extended reality device, connected device, etc.) and stored or output for consumption (e.g., displayed on the device and / or another device). In some cases, the image or video can be further processed for effects (e.g., compression, image enhancement, image restoration, scaling, framerate conversion, etc.) and / or certain applications such as computer vision, extended reality' (e.g., augmented reality, virtual reality, and the like), object detection, image recognition (e.g., face recognition, object recognition, scene recognition, etc.), feature extraction, authentication, and automation, among others.

[0003] In some cases, an electronic device can process images to detect obj ects, faces, and / or any other items captured by the images. The object detection can be useful for various applications such as, for example, artificial intelligence assistance, authentication, automation, gesture recognition, surveillance, extended reality', computer vision, among others. In some examples, the electronic device can implement a lower-power or “always-on” (AON) camera that persistently or periodically operates to automatically detect certain objects in an environment. The lower-power camera can be implemented for a variety of use cases such as, for example, persistent gesture detection, persistent object (e.g., face / person, animal, vehicle, device, plane, etc.) detection, persistent object scanning (e.g., quick response (QR) code scanning, barcode scanning, etc.), persistent facial recognition for authentication, etc.PATENTQualcomm Ref. No. 2501466WO2

[0004] Artificial intelligence (Al) assistants, associated with an electronic device (e.g., smart glasses), can have access to rich, always-available visual context derived from these captured images. Al assistance is a popular use case for smart glasses (e.g., an extended reality head-mounted device). Running some large models (e.g., large machine learning models, such as large language models) for the Al assistance can require more computational resources than are available on the smart glasses. Split assistance on smart glasses allows for such workloads to be offloaded onto a companion device, such as a smart phone.SUMMARY

[0005] The following presents a simplified summary relating to one or more aspects disclosed herein. Thus, the following summary should not be considered an extensive overview relating to all contemplated aspects, nor should the following summary' be considered to identify key or critical elements relating to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary has the sole purpose to present certain concepts relating to one or more aspects relating to the mechanisms disclosed herein in a simplified form to precede the detailed description presented below.

[0006] Systems and techniques are described for artificial intelligence (Al) assistance. In some aspects, an apparatus for Al assistance is provided. The apparatus includes at least one memory' and at least one processor coupled to the at least one memory' and configured to: obtain, from one or more sensors of a device associated with a user, sensor data associated with a scene; determine, based on the sensor data, one or more contexts for the scene; receive a query based on user input from the user; generate a prompt based on the query and the one or more contexts; determine an Al assistance processing strategy from a plurality of Al assistance processing strategies based on the one or more contexts, the prompt, one or more device properties, or a combination thereof; and process, based on the Al assistance strategy, the query to generate an answer to the query.

[0007] In some aspects, a method for artificial intelligence (Al) assistance is provided. The method includes: obtaining, from one or more sensors of a device associated with a user, sensor data associated with a scene; determining, based on thePATENTQualcomm Ref. No. 2501466WO3sensor data, one or more contexts for the scene; receiving a query based on user input from the user; generating a prompt based on the query and the one or more contexts; determining an Al assistance processing strategy from a plurality of Al assistance processing strategies based on the one or more contexts, the prompt, one or more device properties, or a combination thereof; and processing, based on the Al assistance strategy, the query to generate an answer to the query.

[0008] In some aspects, a non-transitory computer-readable medium is provided having stored thereon instructions that, when executed by at least one processor, cause the at least one processor to: obtain, from one or more sensors of a device associated with a user, sensor data associated with a scene; determine, based on the sensor data, one or more contexts for the scene; receive a uery based on user input from the user; generate a prompt based on the query and the one or more contexts; determine an Al assistance processing strategy from a plurality of Al assistance processing strategies based on the one or more contexts, the prompt, one or more device properties, or a combination thereof; and process, based on the Al assistance strategy, the query to generate an answer to the query.

[0009] In some aspects, an apparatus for Al assistance is provided. The apparatus includes: means for obtaining, from one or more sensors of a device associated with a user, sensor data associated with a scene; means for determining, based on the sensor data, one or more contexts for the scene; means for receiving a uery based on user input from the user; means for generating a prompt based on the query and the one or more contexts; means for determining an Al assistance processing strategy from a plurality of Al assistance processing strategies based on the one or more contexts, the prompt, one or more device properties, or a combination thereof; and means for processing, based on the Al assistance strategy, the query to generate an answer to the query.

[0010] Some aspects include a device having a processor (or multiple processors) configured to perform one or more operations of any of the methods summarized above. In some cases, the processor(s) can include a neural processing unit (NPU), a neural signal processor (NSP). a digital signal processor (DSP), a graphics processing unit (GPU), a central processing unit (CPU), any combination thereof, and / or other processor(s). Further aspects include processing devices for use in a device configured with processor-PATENTQualcomm Ref. No. 2501466WO4executable instructions to perform operations of any of the methods summarized above. Further aspects include a non-transitory processor-readable storage medium having stored thereon processor-executable instructions configured to cause a processor of a device to perform operations of any of the methods summarized above. Further aspects include a device having means for performing functions of any of the methods summarized above.

[0011] In some aspects, one or more of the apparatuses described herein is, is part of, and / or includes an extended reality (XR) device or system (e.g., a virtual reality7(VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), a mobile device (e.g., a mobile telephone or other mobile device), a wearable device, a wireless communication device, a camera, a personal computer, a laptop computer, a vehicle or a computing device or component of a vehicle, a server computer or server device (e.g., an edge or cloud-based server, a personal computer acting as a server device, a mobile device such as a mobile phone acting as a server device, an XR device acting as a server device, a vehicle acting as a server device, a network router, or other device acting as a server device), another device, or a combination thereof. In some aspects, the apparatus includes a camera or multiple cameras for capturing one or more images. In some aspects, the apparatus further includes a display for displaying one or more images, notifications, and / or other displayable data. In some aspects, the apparatuses described above can include one or more sensors (e.g., one or more inertial measurement units (IMUs), such as one or more gy roscopes, one or more gyrometers, one or more accelerometers, any combination thereof, and / or other sensor.

[0012] The foregoing has outlined rather broadly the features and technical advantages of examples according to the disclosure in order that the detailed description that follows may be better understood. Additional features and advantages will be described hereinafter. The conception and specific examples disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. Characteristics of the concepts disclosed herein, both their organization and method of operation, together with associated advantages, will be better understood from the following description when considered in connection with thePATENTQualcomm Ref. No. 2501466WO5accompanying figures. Each of the figures is provided for the purposes of illustration and description, and not as a definition of the limits of the claims.

[0013] While aspects are described in the present disclosure by illustration to some examples, those skilled in the art will understand that such aspects may be implemented in many different arrangements and scenarios. Techniques described herein may be implemented using different platform types, devices, systems, shapes, sizes, and / or packaging arrangements. For example, some aspects may be implemented via integrated chip implementations or other non-module-component based devices (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail / purchasing devices, medical devices, and / or artificial intelligence devices). Aspects may be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components, and / or system-level components. Devices incorporating described aspects and features may include additional components and features for implementation and practice of claimed and described aspects. For example, transmission and reception of wireless signals may include one or more components for analog and digital purposes (e.g., hardware components including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, adders, and / or summers). It is intended that aspects described herein may be practiced in a wide variety of devices, components, systems, distributed arrangements, and / or end-user devices of varying size, shape, and constitution.

[0014] Other obj ects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art based on the accompanying drawings and detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this patent, any or all drawings, and each claim.

[0015] The foregoing, together with other features and aspects, will become more apparent upon referring to the following specification, claims, and accompanying drawings.PATENTQualcomm Ref. No. 2501466WO6BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Illustrative aspects of the present application are described in detail below with reference to the following figures:

[0017] FIG. 1 is a diagram illustrating an example of an electronic device used to determine event data and control one or more components and / or operations of the electronic device based on the event data, in accordance with some aspects of the disclosure.

[0018] FIG. 2A and FIG. 2B are diagrams illustrating example system processes for mapping events associated with an environment and controlling settings of a device based on mapped events, in accordance with some aspects of the disclosure.

[0019] FIG. 3A and FIG. 3B are diagrams illustrating example processes for updating an event map, in accordance with some aspects of the disclosure.

[0020] FIG. 4 is a diagram illustrating an example of a system for split Al assistance in smart glasses, in accordance with some aspects of the disclosure.

[0021] FIG. 5 is a diagram illustrating an example of a processing timeline for artificial intelligence assistance that includes a time to first token (TTFT), in accordance with some aspects of the disclosure.

[0022] FIG. 6 is a diagram illustrating examples of use cases for an always sensing camera (e.g., an always on camera) in a device, in accordance with some aspects of the disclosure.

[0023] FIG. 7 is a diagram illustrating an example of a system for a device with an always sensing camera (e.g., an always on camera), in accordance with some aspects of the disclosure.

[0024] FIG. 8 is a diagram illustrating an example of a process for six degrees of freedom (6 DOF) tracking, in accordance with some aspects of the disclosure.

[0025] FIG. 9 is a diagram illustrating an example of a wearable device (e.g., an XR device) capable of capturing images and determining visual context from the images, in accordance with some aspects of the disclosure.PATENTQualcomm Ref. No. 2501466WO7

[0026] FIG. 10 is a diagram illustrating an example of a process for Al assistance, in accordance with some aspects of the disclosure.

[0027] FIG. 11 is a diagram illustrating an example of a system for selecting Al assistant strategies for processing a query from a user, in accordance with some aspects of the disclosure.

[0028] FIG. 12 is a flow diagram illustrating an example of a process for optimized split Al in smart glasses, in accordance with some aspects of the disclosure.

[0029] FIG. 13 is a diagram illustrating an example of a system for implementing certain aspects described herein.DETAILED DESCRIPTION

[0030] Certain aspects of this disclosure are provided below for illustration purposes. Alternate aspects may be devised without departing from the scope of the disclosure. Additionally, well-known elements of the disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the disclosure. Some of the aspects described herein can be applied independently and some of them may be applied in combination as would be apparent to those of skill in the art. In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of aspects of the application. However, it will be apparent that various aspects may be practiced without these specific details. The figures and description are not intended to be restrictive.

[0031] The ensuing description provides example aspects only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the example aspects will provide those skilled in the art with an enabling description for implementing an example aspect. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the application as set forth in the appended claims.

[0032] The terms “exemplary” and / or “example” are used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary7” and / or “example” is not necessarily to be construed as preferred or advantageous over otherPATENTQualcomm Ref. No. 2501466WO8aspects. Likewise, the term “aspects of the disclosure’' does not require that all aspects of the disclosure include the discussed feature, advantage or mode of operation.

[0033] Electronic devices (e.g., mobile phones, wearable devices (e.g., smart watches, smart bracelets, smart glasses, etc.), tablet computers, extended reality (XR) devices (e.g.. virtual reality’ (VR) devices, augmented reality’ (AR) devices, mixed reality (MR) devices, and the like), connected devices, laptop computers, etc.) can implement cameras to detect and / or recognize events of interest. For example, electronic devices can implement cameras that can operate at a reduced power mode and / or can operate as lower-power cameras (e.g., lo er than a capacity of the cameras and / or any other cameras on the electronic device) to detect and / or recognize events of interest on demand, an ongoing, or a periodic basis. In some examples, a lower-power camera can include a camera operating in a reduced or lower power mode / consumption (e.g., relative to the power mode / consumption capabilities of the camera and / or another camera w ith higher power mode / consumption capabilities). In some cases, a lower-power camera can employ’ lower-power settings (e.g., lower power modes, lower power operations, lower power hardware, low er power camera pipeline, etc.) to allow for persistent imaging w ith limited or reduced pow er consumption as compared to other cameras and / or camera pipelines, such as a main camera and / or main camera pipeline. The lower-power settings employed by the lower-power camera can include, for example and without limitation, a low er resolution, a lower amount of image sensors (and / or an image sensor(s) having a lower power consumption than other image sensors on the electronic device), a lower framerate, on-chip static random-access memory’ (SRAM) rather than dynamic random-access memory (DRAM) which may generally draw more power than SRAM, island voltage rails, oscillators (e.g., rather than phase lock loops (PLLs) which may have a higher power draw) for lock sourcing, and / or other hardw-are / softw are components / settings that result in lower pow er consumption.

[0034] As noted above, the cameras can be used to detect events of interest. Example events of interest can include gestures (e.g., hand gestures, etc.), an action (e.g., by a device, person, and / or animal), a presence or occurrence of one or more objects, etc. An object associated with an event of interest can include and / or refer to, for example and without limitation, a face, a hand, one or more fingers, a portion of a human body, a code (e.g., a quick response (QR) code, a barcode, etc ), a document, a scene or environment,PATENTQualcomm Ref. No. 2501466WO9a link, a machine-readable code, etc. The lower-power cameras can implement lower-power hardware and / or energy efficient image processing software used to detect events of interest. The lower-power cameras can remain on or “wake up’’ to watch movement and / or objects in a scene and detect events in the scene while using less battery power than other devices such as higher power / resolution cameras.

[0035] For example, a camera can watch movement and / or activity in a scene to discover objects. In some examples, the camera can employ lower-power settings for lower or limited power consumption as previously described and as compared to the camera or another camera employing higher-power settings. To illustrate, an XR device can implement a camera that periodically discovers an XR controller and / or other tracked objects, a mobile phone can implement a camera that periodically checks for a code (e.g., QR code) or document to scan, a smart home assistant can implement a camera that periodically checks for a user presence, etc. Upon discovering an object, the camera can trigger one or more actions such as, for example, object detection, object recognition, authentication (e.g., facial authentication, etc.), and / or image processing tasks, among other actions. In some cases, the cameras can “wake up” other devices and / or components such as other cameras, sensors, processing hardware, etc.

[0036] As mentioned, artificial intelligence (Al) assistants, associated with an electronic device (e.g., smart glasses), can have access to rich, always-available visual context derived from these captured images. Al assistance is a popular use case for smart glasses (e.g., an XR head-mounted device). Running some large models (e.g., large machine learning models, such as large language models) for the Al assistance can require more computational resources than are available on the smart glasses. Split assistance on smart glasses allows for such workloads to be offloaded onto a companion device, such as a smart phone. Image, text, and / or audio can be captured on the glasses and then sent to the companion device, where the large models can be loaded and ready to process user inputs (e.g., a user query). Generated text responses (e.g., to a user query) from these models can be converted into audio, and then sent back to the smart glasses (e.g., for playback to a user).

[0037] For split assistance on a device (e.g., smart glasses), there is a balancing between processing Al assistant workloads (e.g., machine learning models, such asPATENTQualcomm Ref. No. 2501466WO10language models) locally and remotely. Running Al assistant workloads, such as large language models (LLMs), on smart glasses can be power-prohibitive, both thermally and battery-life wise. However, it is possible to run smaller Al assistance workloads, such as small language models (SLMs) on smart glasses, but these SLMs offer less intelligence as compared to LLMs. Offloading Al assistant workloads onto a companion device (e.g., a smart phone) can introduce additional latency in terms of time to first token (TTFT).

[0038] As such, improved systems and techniques for optimized split Al in smart glasses can be beneficial.

[0039] In one or more aspects of the present disclosure, systems, apparatuses, methods (also referred to as processes), and computer-readable media (collectively referred to herein as “systems and techniques”) are described herein that provide solutions for optimized split Al in smart glasses.

[0040] Various aspects relate generally to artificial intelligence (Al) assistance. Some aspects more specifically relate to systems and techniques that provide solutions for an Al assistant mode selector that selects among various different Al assistant strategies for an electronic device (e.g., smart glasses). In one or more examples, an Al assistant strategy involves executing the Al assistant workload remotely (e.g., on a companion device, such as a smart phone). This strategy allows for low-power on the smart glasses, a longer latency, and a higher intelligence model. In some examples, an Al assistant strategy’ involves executing a high-intelligence Al assistant workload locally (e.g., on the smart glasses themselves). This strategy' allows for high power on the smart glasses, a shorter latency, and a higher intelligence model. In one or more examples, an Al assistant strategy' involves executing a low-intelligence Al assistant workload locally. This strategy7allows for medium power on the smart glasses, a shorter latency, and a lower intelligence model. In some examples, an Al assistant strategy involves parallel execution of a low-intelligence Al assistant workload locally and an Al assistant workload remotely. This strategy7allows for a medium power on the smart glasses, a high responsiveness, and a delayed intelligence.

[0041] In one or more examples, for the Al assistant mode selector, each context (e.g., associated with a captured image) can have a pre-determined weighting that is associated with each Al assistance strategy7(e.g., a first Al assistance strategy can have a first weight,PATENTQualcomm Ref. No. 2501466WO11a second Al assistance strategy can have a second weight, a third Al assistance strategy can have a third weight, and so on, where the weights for the various Al assistance strategies can be same or different). In some examples, the Al assistant strategy with the highest weighted sum can be chosen by the Al assistant mode selector. In one or more examples, a local SLM can be used (e.g., by the Al assistant mode selector on the device) to choose an appropriate Al assistant strategy based on one or more contexts, one or more device properties (e.g., battery capacity, Wi-Fi conditions (e.g., signal to noise ratio), and / or poses of the cameras), and a prompt (e.g., which is generated from a user query).

[0042] In one or more aspects, during operation of a method for Al assistance, one or more sensors (e.g., one or more image sensors, light detection and ranging (LiDAR) sensors, radar sensors, etc.) of a device associated with a user can obtain sensor data associated with a scene (e.g., a plurality of images of the scene, LiDAR sensor data, radar sensor data, etc ). One or more processors of the device can determine, based on the sensor data, one or more contexts for the scene. One or more processors of the device can receive a query based on user input from the user. One or more processors of the device can generate a prompt based on the query7and the one or more contexts. One or more processors of the device can determine an Al assistance processing strategy7from a plurality of Al assistance processing strategies based on the one or more contexts, the prompt, and / or one or more device properties (e.g., device properties received by the one or more processors of the device). The query can be processed, based on the Al assistance strategy7, to generate an answer to the query.

[0043] In one or more examples, each Al assistance processing strategy of the plurality of Al assistance processing strategies can be associated with one or more locations for the Al assistance processing and one or more different machine learning models. In some examples, the one or more locations can include a location on the device and / or a location remote from the device. In one or more examples, the one or more different machine learning models can include a first machine learning model and / or a second machine learning model, wherein the second machine learning model has fewer parameters than the first machine learning model.

[0044] In some examples, each context of the one or more contexts and / or each device property of the one or more device properties can have a respective weight associatedPATENTQualcomm Ref. No. 2501466WO12with each Al assistance processing strategy of the plurality of Al assistance processing strategies. In one or more examples, determining the Al assistance processing strategy of the plurality of Al assistance processing strategies can be further based on the Al assistance processing strategy having a highest weighted sum of weights of the plurality of Al assistance processing strategies.

[0045] In one or more examples, the Al assistance processing strategy of the plurality of Al assistance processing strategies can be determined using a small language model (SLM). In some examples, the one or more device properties can include a battery' capacity of the device, a network connection signal to noise ratio, and / or a six degrees of freedom (6 DoF) pose for each of the one or more sensors (e.g.. each image sensor of the one or more image sensors) of the device. As noted previously, the one or more sensors can include one or more image sensors, in which case the sensor data associated with the scene includes a plurality of images of the scene. In one or more examples, each image of the plurality of images can be obtained at a respective time. In one or more examples, the one or more images sensors can include one or more always-on (AON) image sensors.

[0046] In one or more examples, the one or more processors of the device can determine, based on the one or more contexts, one or more events. The one or more processors of the device can monitor, based on data (e.g., image data, LiDAR data, radar data, etc.) from the one or more sensors, the scene for the one or more events. The one or more processors of the device can detect, based on monitoring the scene for the one or more events, at least one event of the one or more events.

[0047] In some examples, the one or more contexts can be stored within a log (e.g., a journal). In one or more examples, at least a portion of the sensor data associated with the one or more contexts (e.g., one or more images of the plurality of images associated with the one or more contexts) can be stored within the log. In some examples, the device is an XR device. In one or more examples, the XR device is a head-mounted device.

[0048] Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. In one or more examples, the systems and techniques can provide a benefit of a tradeoff for a power, performance, and intelligence optimized solution for split Al assistance in smart glasses.PATENTQualcomm Ref. No. 2501466WO13

[0049] Additional aspects of the present disclosure are described in more detail below. Various aspects of the systems and techniques described herein will be discussed below with respect to the figures.

[0050] As used herein, the phrase ‘‘based on” shall not be construed as a reference to a closed set of information, one or more conditions, one or more factors, or the like. In other words, the phrase '‘based on A” (where ‘'A” may be information, a condition, a factor, or the like) shall be construed as “based at least on A” unless specifically recited differently.

[0051] FIG. 1 is a diagram illustrating an example of an electronic device 100 used to map events and control one or more components and / or operations of the electronic device 100 based on mapped events, in accordance with some examples of the present disclosure. In some examples, the electronic device 100 can include an electronic device configured to provide one or more functionalities such as, for example, imaging functionalities, extended reality (XR) functionalities (e.g., localization / tracking, detection, classification, mapping, content rendering, etc.), image processing functionalities, device management and / or control functionalities, gaming functionalities, autonomous driving or navigation functionalities, computer vision functionalities, robotic functions, automation, computer vision, etc.

[0052] For example, in some cases, the electronic device 100 can be an XR device (e.g., ahead-mounted display, a heads-up display device, smart glasses, etc.) configured to detect, localize, and map the location of the XR device, provide XR functionalities, and map events as described herein to control one or more operations / states of the XR device. In some cases, the electronic device 100 can implement one or more applications such as, for example and without limitation, an XR application, an application for managing and / or controlling components and / or operations of the electronic device 100. a smart home application, a video game application, a device control application, an autonomous driving application, a navigation application, a productivity application, a social media application, a communications application, a modeling application, a media application, an electronic commerce application, a browser application, a design application, a map application, and / or any other application.PATENTQualcomm Ref. No. 2501466WO14

[0053] In the illustrative example shown in FIG. 1, the electronic device 100 can include one or more image sensors, such as image sensors 102 and 104, an audio sensor 106 (e.g., an ultrasonic sensor, a microphone, etc.), an inertial measurement unit (IMU) 108, and one or more compute components 110. In some cases, the electronic device 100 can optionally include one or more other / additional sensors such as, for example and without limitation, a radar, a light detection and ranging (LIDAR) sensor, a touch sensor, a pressure sensor (e.g., a barometric air pressure sensor and / or any other pressure sensor), a gyroscope, an accelerometer, a magnetometer, and / or any other sensor. In some examples, the electronic device 100 can include additional components such as, for example, a light-emitting diode (LED) device, a storage device, a cache, a communications interface, a display, a memory device, etc. An example architecture and example hardware components that can be implemented by the electronic device 100 are further described below with respect to FIG. 7.

[0054] The electronic device 100 can be part of, or implemented by, a single computing device or multiple computing devices. In some examples, the electronic device 100 can be part of an electronic device (or devices) such as a camera system (e.g., a digital camera, an IP camera, a video camera, a security camera, etc.), a telephone system (e.g., a smartphone, a cellular telephone, a conferencing system, etc.), a laptop or notebook computer, a tablet computer, a set-top box, a smart television, a display device, a gaming console, an XR device such as an HMD, a drone, a computer in a vehicle, an loT (Intemet-of-Things) device, a smart wearable device, or any other suitable electronic device(s).

[0055] In some implementations, the image sensor 102, the image sensor 104. the audio sensor 106, the IMU 108, and / or the one or more compute components 110 can be part of the same computing device. For example, in some cases, the image sensor 102, the image sensor 104, the audio sensor 106, the IMU 108, and / or the one or more compute components 110 can be integrated with or into a camera system, a smartphone, a laptop, a tablet computer, a smart wearable device, an XR device such as an HMD, an loT device, a gaming system, and / or any other computing device. In other implementations, the image sensor 102, the image sensor 104, the audio sensor 106, the IMU 108, and / or the one or more compute components 110 can be part of, or implemented by, two or more separate computing devices.PATENTQualcomm Ref. No. 2501466WO15

[0056] The one or more compute components 110 of the electronic device 100 can include, for example and without limitation, a central processing unit (CPU) 112, a graphics processing unit (GPU) 114, a digital signal processor (DSP) 116, and / or an image signal processor (ISP) 118. In some examples, the electronic device 100 can include other processors such as, for example, a computer vision (CV) processor, a neural network processor (NNP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), etc. The electronic device 100 can use the one or more compute components 110 to perform various computing operations such as, for example, extended reality operations (e.g., tracking, localization, object detection, classification, pose estimation, mapping, content anchoring, content rendering, etc.), device control operations, image / video processing, graphics rendering, event mapping, machine learning, data processing, modeling, calculations, computer vision, and / or any other operations.

[0057] In some cases, the one or more compute components 110 can include other electronic circuits or hardware, computer software, firmware, or any combination thereof, to perform any of the various operations described herein. In some examples, the one or more compute components 110 can include more or less compute components than those shown in FIG. 1. Moreover, the CPU 112, the GPU 114, the DSP 116, and the ISP 118 are merely illustrative examples of compute components provided for explanation purposes.

[0058] The image sensor 102 and / or the image sensor 104 can include any image and / or video sensor or capturing device, such as a digital camera sensor, a video camera sensor, a smartphone camera sensor, an image / video capture device on an electronic apparatus such as a television or computer, a camera, etc. In some cases, the image sensor 102 and / or the image sensor 104 can be part of a camera or computing device such as a digital camera, a video camera, an IP camera, a smartphone, a smart television, a game system, etc. Moreover, in some cases, the image sensor 102 and the image sensor 104 can include multiple image sensors, such as rear and front sensor devices, and can be part of a dual-camera or other multi-camera assembly (e.g., including two camera, three cameras, four cameras, or other number of cameras).PATENTQualcomm Ref. No. 2501466WO16

[0059] In some examples, the image sensor 102 can be part of a camera, such as a camera that implements or is capable of implementing lower-power camera settings as previously described, and the image sensor 104 can be part of a camera, such as a camera that implements or is capable of implementing higher-power camera settings (e.g., as compared to the camera associated with the image sensor 102). In some examples, a camera associated with the image sensor 102 can implement lower-power hardware (e.g., as compared to a camera associated with the image sensor 104) and / or more energy efficient image processing software (e g., as compared to a camera associated with the image sensor 104) used to detect events and / or process captured image data. In some cases, the camera can implement lower power settings and / or modes than the camera associated with the image sensor 104 such as, for example, a lower framerate, a lower resolution, a smaller number of image sensors, a lower-power mode, lower-power imaging mode, etc. In some examples, the camera can implement less and / or lower-power image sensors than a higher-power camera, can use lower-power memory such as on-chip static random-access memory (SRAM) rather than dynamic random-access memory (DRAM), can use island voltage rails to reduce leakage, can use ring oscillators as clock sources rather than phased-locked loops (PLLs), and / or other lower-power processing hardware / components .

[0060] In some cases, the cameras associated with image sensor 102 and / or image sensor 104 can remain on or "‘wake up” to watch movement and / or events in a scene and / or detect events in the scene while using less battery power than other devices such as higher power / resolution cameras. For example, a camera associated with image sensor 102 can persistently watch or wake up (for example, by a proximity sensor or wake up periodically) to watch movement and / or activity in a scene to discover objects in the scene. In some cases, upon discovering an event, the camera can trigger one or more actions such as, for example, object detection, object recognition, facial authentication, image processing tasks, among other actions. In some cases, the cameras associated with image sensor 102 and / or image sensor 104 can also “wake up” other devices such as other sensors, processing hardware, etc.

[0061] In some examples, each image sensor 102 and 104 can capture image data and generate frames based on the image data and / or provide the image data or frames to the one or more compute components 110 for processing. A frame can include a video framePATENTQualcomm Ref. No. 2501466WO17of a video sequence or a still image. A frame can include a pixel array representing a scene. For example, a frame can be a red-green-blue (RGB) frame having red, green, and blue color components per pixel; a luma, chroma-red, chroma-blue (Y CbCr) frame having a luma component and two chroma (color) components (chroma-red and chroma-blue) per pixel; or any other suitable type of color or monochrome picture.

[0062] In some examples, the one or more compute components 110 can perform image / video processing, event mapping, XR processing, device management / control, and / or other operations as described herein using data from the image sensor 102, the image sensor 104, the audio sensor 106, the IMU 108, and / or any other sensors and / or component. For example, in some cases, the one or more compute components 110 can perform event mapping, device control / management, tracking, localization, object detection, object classification, pose estimation, shape estimation, scene mapping, content anchoring, content rendering, image processing, modeling, content generation, gesture detection, gesture recognition, and / or other operations based on data from the image sensor 102, the image sensor 104, the audio sensor 106, the IMU 108, and / or any other component. In some examples, the one or more compute components 110 can use data from the image sensor 102, the image sensor 104, the audio sensor 106, the IMU 108, and / or any other component, to generate event data (e.g., an event map correlating detected events to particular environments and / or regions / portions of the environments) and adjust a state (e g., power mode, setting, etc.) and / or operation of one or more components such as, for example, the image sensor 102, the image sensor 104, the audio sensor 106, the IMU 108, the one or more compute components 110, and / or any other components of the electronic device 100. In some examples, the one or more compute components 110 can detect and map events in a scene and / or control an operation / state of the electronic device 100 (and / or one or more components thereof), based on data from the image sensor 102, the image sensor 104, the audio sensor 106, the IMU 108, and / or any other component.

[0063] In some examples, the one or more compute components 110 can implement one or more software engines and / or algorithms such as, for example, a feature extractor 120, a keyframe matcher 122, a mapper 124. and a controller 126. as described herein. In some cases, the one or more compute components 110 can implement one or more additional components and / or algorithms such as a machine learning model (s), aPATENTQualcomm Ref. No. 2501466WO18computer vision algorithm(s), a neural network(s), and / or any other algorithm and / or component.

[0064] In some examples, the feature extractor 120 can extract visual features from one or more frames obtained by a camera device, such as a camera device associated with image sensor 102. The feature extractor 120 can implement a detector and / or algorithm to extract the visual features such as, for example and without limitation, a scaleinvariant feature transform (SIFT), speeded up robust features (SURF), Oriented FAST and rotated BRIEF (ORB), and / or any other detector / algorithm.

[0065] In some examples, the keyframe matcher 122 can compare features of the one or more frames obtained by the camera device (e.g., the visual features extracted by the feature extractor 120) to features of keyframes in event data (e.g., an event map) generated by the mapper 124. In some cases, the event data can include an event map that correlates detected events of interest (and / or associated data such as keyframes, extracted image features, event counts, etc.) with one or more specific environments (and / or portions / regions of the specific environments) in which such detected events occurred (and / or were detected). In some examples, the key frames in the event data can include keyframes created based on frames associated with detected events of interest. The mapper 124 can determine whether to create a new keyframe (or replace an existing keyframe) associated with an event, and record (or update) an event count associated with that keyframe in the event data. The event data can include a number of keyframes corresponding to one or more locations / environments where events of interest have been observed (e.g.. detected from one or more frames obtained by the camera device) and event counts associated with those keyframes. The controller 126 can use the event data to modulate one or more settings (e.g., framerate, resolution, power mode, number of image sensors invoked, binning mode, imaging mode, etc.) of the camera device (e.g., one or more settings of the image sensor 102 and / or one or more other hardware and / or software components) based on a match between an incoming frame from the camera device and a keyframe in the event data.

[0066] In some cases, the event data can include an event map, classification data, keyframe data, features extracted from frames, classification map data, event statistics, and / or a dictionary with entries containing visual features of key frames and the numberPATENTQualcomm Ref. No. 2501466WO19of occurrences of detected events of interest that coincide with each of the key frames (and / or the number of matches to the keyframe). For example, the event data can include a dictionary with an entry indicating n number of face detection events are associated with one or more visual features corresponding to keyframe x. In some examples, the mapper 124 can compute a count (e.g., for recorded keyframes) for each event. In some cases, the count can include a count and / or an average of occurrences of the event of interest within a certain period of time. In some cases, the count can include a total count of occurrences of the event of interest. In some examples, the mapper 124 can determine a total count based on a sum of the count of events of interest across keyframes that are associated with an environment(s) corresponding to the events and / or a region / portion of the environment(s). The count for an event can be used to determine a prior probability of that event for a given keyframe associated with that event. For example, the count of detection events for keyframes x and y (or the number of matches to keyframes x and y) can be used to determine that a first measure of detection events are associated with keyframe x and a second measure of detection events are associated with keyframe y.

[0067] The controller 126 can use the probabilities to adjust one or more settings of the camera device (e.g., the camera device associated with image sensor 102) when the camera device is in an environment associated with a keyframe in the event data (e.g., based on a match between a frame captured in that environment and a keyframe in the event data and associated with that environment). In some cases, the controller 126 can alternatively or additionally use the probabilities to adjust one or more settings of other components of the electronic device 100, such as another camera device (e.g., a camera device associated with image sensor 104, a camera device employing and / or having capabilities to employ higher-power camera settings than the camera device associated with image sensor 102, etc.), a processing component and / or pipeline, etc., when the electronic device 100 is or is not in an environment associated with a keyframe in the event data.

[0068] The mapper 124 can determine whether to create a new entry in the event data based on one or more factors. For example, in some cases, the mapper 124 can determine whether to create a new entry in the event data depending on whether a current frame captured by the camera device (e.g., the camera device associated with image sensor 102) matches an existing keyframe in the event data, whether a camera event of interest wasPATENTQualcomm Ref. No. 2501466WO20detected in the current frame, a time elapsed since a last keyframe was created, a time elapsed since a last keyframe match, etc. In some cases, the mapper 124 can employ a periodic culling process to eliminate map entries with lower likelihoods of events (e.g., with likelihoods having a probability' value at or below a threshold).

[0069] In some examples, the controller 126 can modulate one or more settings of the camera device (e.g., the camera device associated with image sensor 102) such as, for example and without limitation, increasing or decreasing a framerate, a resolution, a power mode, an imaging mode, a number of image sensors invoked to capture one or more images associated with an event of interest, processing actions and / or a processing pipeline for processing captured images and / or detecting events in captured images, etc. For example, in some cases, the electronic device 100 may be statistically more likely to encounter certain events of interest in certain environments. In some cases, to avoid wasting unnecessary power when the electronic device 100 is located in an environment where the electronic device 100 has a lower likelihood of encountering an event of interest, the controller 126 can turn off the camera device or modulate one or more settings of the camera device to reduce power usage by the camera device when the electronic device 100 is in the environment associated with the lower likelihood of encountering an event of interest. When the electronic device 100 is located in an environment where the electronic device 100 has a higher likelihood of encountering an event of interest, the controller 126 can turn on the camera device or modulate one or more settings of the camera device to increase power usage by, and / or performance of, the camera device when the electronic device 100 is in the environment associated with the higher likelihood of encountering an event of interest.

[0070] In some cases, the controller 126 can modulate one or more settings of a camera device(s) (e.g., image sensor 102, image sensor 104) based on the camera event prior probabilities for a currently (or recently within a threshold period) matched keyframe. In some examples, the controller 126 can incorporate a configurable framerate for each camera event of interest. In some examples, when the mapper 124 indicates a matched keyframe, the controller 126 can set the framerate of the camera device to a framerate equal to the configurable framerate times the prior probability for the matched keyframe entry in the event data. In some examples, the camera device can maintain this framerate for a configurable period of time or until the next matched keyframe. In somePATENTQualcomm Ref. No. 2501466WO21cases, when / if there are no current (or recently within a threshold period) matched keyframes, the camera device can implement a default framerate, such as a lower framerate that results in lower power consumption during periods of unlikely detection events.

[0071] In some examples, the electronic device 100 (and / or the controller 126 on the electronic device 100) can monitor (and implement the techniques described herein for) various types of events. Non-limiting examples of detection events can include face detection, scene detection (e.g., sunset, room, etc.), human group detection, animal / pet detection, code (e.g., QR code, etc.) detection, document detection, infrared LED detection (e.g., as with a six degrees of freedom (6DOF) motion tracker), plane detection, text detection, device detection (e.g., controller, screen, gadget, etc ), gesture detection (e.g., smile detection, emotion detection, hand waving, etc.), among others.

[0072] In some cases, the mapper 124 can employ a periodic decay normalization process by which event counts are decreased by a configurable amount across map entries. This can keep the count values numerically bound and allow for more rapid adjustment of priors when event / key frame correlations change.

[0073] In some cases, the camera device (e.g., image sensor 102) can implement a decaying default setting, such as a framerate. For example, a reduced framerate can be associated with an increased event detection latency (e g., a higher framerate can result in a lower latency). For a default framerate (e.g.. a framerate when no key frames have been matched), the controller 126 may default the framerate of the camera device to a lower framerate (e.g., which can result in a higher detection latency) when the event data contains a smaller number (e.g., below a threshold) of recorded events. For such cases, the controller 126 can implement a default framerate that begins at a threshold level but decays as more events are added to the event data (e.g., as the electronic device learns which locations / environments are most associated with events of interest).

[0074] In some cases, the mapper 124 can use non-camera events for the mapper decision-making. For example, the mapper 124 can use non-camera events such as a creation of new key frames, culling / elimination of stale keyframes, etc. In some cases, the controller 126 can modulate non-camera workloads / resources based on keyframes and / or non-camera events. For example, the controller 126 can implement one or more audioPATENTQualcomm Ref. No. 2501466WO22algorithms (e.g., beamforming, etc.) modulated by camera device keyframes and / or audio-based presence detection. As another example, the controller 126 can implement location services (e.g., Global Navigation Satellite System (GNSS), Wi-Fi, etc.), application data, user inputs, and / or other data, modulated by camera device keyframes and / or use of location services.

[0075] In some cases, the IMU 108 can detect an acceleration, angular rate, and / or orientation of the electronic device 100 and generate measurements based on the detected acceleration. In some cases, the IMU 108 can detect and measure the orientation, linear velocity, and / or angular rate of the electronic device 100. For example, the IMU 108 can measure a movement and / or a pitch, roll, and yaw of the electronic device 100. In some examples, the electronic device 100 can use measurements obtained by the IMU 108 and / or data from one or more of the image sensor 102, the image sensor 104, the audio sensor 106, etc., to calculate a pose of the electronic device 100 within 3D space. In some cases, the electronic device 100 can additionally or alternatively use sensor data from the image sensor 102, the image sensor 104, the audio sensor 106, and / or any other sensor to perform tracking, pose estimation, mapping, generating event data entries, and / or other operations as described herein.

[0076] The components shown in FIG. 1 with respect to the electronic device 100 are illustrative examples provided for explanation purposes. In other examples, the electronic device 100 can include more or less components than those shown in FIG. 1. While the electronic device 100 is shown to include certain components, one of ordinary skill will appreciate that the electronic device 100 can include more or fewer components than those shown in FIG. 1. For example, the electronic device 100 can include, in some instances, one or more memory' devices (e.g., RAM, ROM, cache, and / or the like), one or more networking interfaces (e g., wired and / or wireless communications interfaces and the like), one or more display devices, caches, storage devices, and / or other hardware or processing devices that are not show n in FIG. 1. An illustrative example of a computing device and / or hardware components that can be implemented with the electronic device 100 are described below with respect to FIG. 7.

[0077] FIG. 2A is a diagram illustrating an example system process 200 for mapping events associated with an environment and controlling settings (e.g., power states,PATENTQualcomm Ref. No. 2501466WO23operations, parameters, etc.) of a camera device based on mapped events. In this example, the image sensor 102 can capture a frame 210 of a scene and / or an event in an environment where the image sensor 102 is located. In some examples, the image sensor 102 can monitor an environment and / or look for events of interest in the environment to capture a frame of any event of interest in the environment. An event of interest can include, for example and without limitation, a gesture (e.g., a hand gesture, etc.), an emotion (e.g., a smile, etc.), an activity or action (e.g., by a person, a device, an animal, etc.), an occurrence or present of an object, etc. An object associated with an event of interest can include, represent, and / or refer to, for example and without limitation, a face, a scene (e.g., a sunset, a park, a room, etc.), a person, a group of people, an animal, a document, a code (e.g.. a QR code, a barcode, etc.) on an object (e.g., a device, a document, a display, a structure such as a door or wall, a sign, etc.), a light, a pattern, a link on an object, a plane in a physical space (e.g., a plane on a surface, etc.), text, infrared (IR) light-emitting diode (LED) detection, and / or any other object.

[0078] The electronic device 100 can use an image processing module 212 to perform one or more image processing operations on the frame 210. In some examples, the one or more image processing operations can include object detection to detect one or more camera event detection triggers 214 based on the frame 210. For example, the image processing module 212 can extract features from the frame and use the extracted features for object detection. The image processing module 212 can detect the one or more camera event detection triggers 214 based on the extracted features. The one or more camera event detection triggers 214 can include one or more events of interest as previously described. In some examples, the one or more image processing operations can include a camera processing pipeline associated with the image sensor 102. In some cases, the one or more image processing operations can detect and / or recognize one or more camera event detection triggers 214. The image processing module 212 can provide the one or more camera event detection triggers 214 to the feature extractor 120, the mapper 124, the controller 126, and / or an application 204 on the electronic device 100. The feature extractor 120, the mapper 124, the controller 126, and / or the application 204 can use the one or more camera event detection triggers 214 to perform one or more actions as described herein, such as application operations, camera setting adjustments, object detection, object recognition, etc. For example, the one or more camera event detectionPATENTQualcomm Ref. No. 2501466WO24triggers 214 can be configured to trigger one or more actions by the feature extractor 120, the mapper 124, the controller 126, and / or the application 204, as explained herein.

[0079] In some examples, the application 204 can include any application on the electronic device 100 that can use information about events detected in an environment. For example, the application 204 can include an authentication application (e.g., facial authentication application, etc.), an XR application, a navigation application, an application for ordering or purchasing items, a video game application, a photography application, a device management application, a web application, a communications application (e.g., a messaging application, a video and / or voice application, etc.), a media playback application, a social media network application, a browser application, a scanning application, etc. The application 204 can use the one or more camera event detection triggers 214 to perform one or more actions. For example, if the application 204 is an XR application, the application 204 can use the one or more camera event detection triggers 214 to discover an event in the environment for use by the application 204, such as for example, a device (e.g., a controller or other input device, etc.), a hand, a boundary, a person, etc. As another example, the application 204 can use the one or more camera event detection triggers 214 to scan a document, link, or code and perform an action based on the document, link, or code. As yet another example, if the application 204 is a smart home assistant application, the application 204 can use the one or more camera event detection triggers 214 to discover a presence of a person and / or object to trigger a smart home device operation / action.

[0080] Moreover, the feature extractor 120 can analyze the frame 210 to extract visual features in the frame 210. In some examples, the feature extractor 120 can perform object recognition to extract visual features in the frame 210 and classify an event associated with the extracted features. In some cases, the feature extractor 120 can implement an algorithm to extract visual features from the frame 210 and determine a descriptor(s) of the extracted features. Non-limiting examples of a feature extractor / detector algorithm can include SIFT, SURF, ORB, and the like.

[0081] The feature extractor 120 can provide features extracted from the frame 210 and an associated descriptor(s) to the keyframe matcher 122 and the mapper 124. The associated descriptor(s) can identify and / or describe the features extracted from the framePATENTQualcomm Ref. No. 2501466WO25210 and / or an event detected from the extracted features. In some examples, the associated descriptor(s) can include a tag, label, identifier, and / or any other descriptor.

[0082] The keyframe matcher 122 can use the features and / or descriptor(s) from the feature extractor 120 to determine whether the electronic device 100 is located (or determine a likelihood that the electronic device 100 is located) in an environment (or a region / location within an environment) where events of interest have been previously detected. In some examples, the keyframe matcher 122 can use event data 202 containing key frames to determine whether the electronic device 100 is located (or determine a likelihood that the electronic device 100 is located) in an environment (or a region / location within an environment) where events of interest have been previously detected. In some examples, the event data 202 can include keyframes corresponding to frames capturing detected events of interest. In some cases, the event data 202 can include a number of key frames corresponding to locations / environments where events of interest have been observed (e.g., detected from one or more frames obtained by the image sensor 102 or the image sensor 104) and event counts associated with those keyframes.

[0083] In some cases, the event data 202 can include an event map, event entries, classification data, one or more keyframes, a classification map, event statistics, extracted features, feature descriptors, and / or any other data. In some examples, the event data 202 can include a dictionary with entries containing visual features of keyframes and the number of occurrences of detected events of interest (e.g., event counts) that coincide with each of the key frames (and / or the number of matches to the keyframe). For example, the event data 202 can include a dictionary with an entry indicating n number of QR code detection events (e.g., n number of previous detections of a QR code) are associated with one or more visual features corresponding to keyframe x. In some cases, the keyframe matcher 122 can employ a periodic decay normalization process by which event counts are decreased by a configurable amount across one or more event data entries. For example, the keyframe matcher 122 can employ a periodic decay normalization process by which event counts are decreased by a configurable amount across all event data entries. This can keep the count values numerically bound and allow for more rapid adjustment of priors when event / key frame correlations change.PATENTQualcomm Ref. No. 2501466WO26

[0084] In some examples, the keyframe matcher 122 can compare the features extracted from the frame 210 with features of key frames in the event data 202. In some examples, the keyframe matcher 122 can compare a descriptor(s) of the features extracted from the frame 210 with descriptors of features of keyframes in the event data 202. Since the keyframes in the event data 202 correspond to an environment or a location in an environment where an event of interest has previously been detected, a match between the features extracted from the frame 210 (and / or an associated descriptor) and features of a keyframe in the event data 202 (and / or an associated descriptor) can indicate that the electronic device 100 is located (or a likelihood that the electronic device 100 is located) in an environment or a location in the environment where an event of interest has previously been detected. In some examples, the keyframe matcher 122 can correlate the frame 210 and / or the features extracted from the frame 210 to a particular environment (and / or a location / region within the particular environment) based on a match between the features extracted from the frame 210 and the features key frames in the event data 202. Such correlation can indicate that the electronic device 100 is located in the particular environment (and / or the location / region within the particular environment). Information about that the electronic device 100 being located in an environment or a location in the environment where an event of interest has previously been detected can be used to determine a likelihood that an event of interest will be detected again when the electronic device 100 is located in the environment or the location in the environment.

[0085] For example, in some cases, the likelihood that an event of interest will be observed / detected in an environment may increase or decrease depending on whether the event of interest has previously been observed / detected in that environment and / or the number of times that the event of interest has previously been observed / detected in that environment. To illustrate, a determination that an event of interest has been observed / detected frequently in a particular room can suggest a higher likelihood that the event of interest will be observed / detected again when electronic device 100 is in the particular room than a determination that no events of interest have previously been observed / detected in the particular room. Thus, a determination that the electronic device 100 is located in an environment or a location in the environment where an event of interest has previously been detected can be used to determine a likelihood that an event of interest will be detected again when the electronic device 100 is located in thePATENTQualcomm Ref. No. 2501466WO27environment or the location in the environment. As previously explained, in some examples, the determination that the electronic device 100 is located in an environment or location where an event of interest has previously been detected can be based on a match between features extracted from the frame 210 and features of one or more key frames in the event data that are correlated with the particular environment or location.

[0086] As further described herein, the likelihood that the event of interest will be detected again when the electronic device 100 is located in the environment or the location in the environment can be used to control device and / or processing settings (e.g., power modes, operations, device configurations, processing configurations, processing pipelines, etc.) to reduce power consumption and increase power savings when the electronic device 100 is located in an environment (or location thereof) associated with a lower likelihood of an event detection. Similarly, the likelihood that the event of interest will be detected again when the electronic device 100 is located in the environment or the location in the environment can be used to control device and / or processing settings to increase a performance and / or operating state of the electronic device 100 when the electronic device 100 is located in an environment (or location thereof) associated with a higher likelihood of an event detection, such as increasing an event detection performance, an imaging and / or image processing performance (e.g., image / imaging quality, resolution, scaling, framerate, etc.), etc.

[0087] The keyframe matcher 122 can provide the mapper 124 a result of the comparison of the features extracted from the frame 210 and the features of key frames in the event data 202. For example, the keyframe matcher 122 can provide the mapper 124 an indication that the features extracted from the frame 210 match features of a keyframe in the event data 202 or do not match features of any keyframes in the event data 202. The mapper 124 can use the information from the keyframe matcher 122 to determine whether to create a new keyframe (or replace an existing keyframe) associated with an event, and record (or update) an event count associated with that keyframe. For example, if the features extracted from the frame 210 match features of a keyframe in the event data 202, the mapper 124 can increase a count of detected events associated with that keyframe in the event data 202. In some examples, the mapper 124 can record or update an entry with a count of detected events associated with that keyframe.PATENTQualcomm Ref. No. 2501466WO28

[0088] In some cases, if the features extracted from the frame 210 do not match features of any key frames in the event data 202. the mapper 124 can create a new keyframe in the event data 202, which (e.g., the new keyframe) can correspond to the frame 210. For example, as previously described, the electronic device 100 previously detected (e.g., via the image processing module 212) the one or more camera event detection triggers 214 based on the frame 210, which can indicate that an event of interest has been detected in the frame 210. Accordingly, if the features extracted from the frame 210 do not match features of any keyframes in the event data 202, the mapper 124 can add a new keyframe in the event data 202 corresponding to the frame 210. The new keyframe can associate the features from the frame 210 with a detected event of interest and / or an associated environment (and / or location thereol). The mapper 124 can include in the event data 202 an event detection count associated with the new keyframe, and can increment the count anytime a new frame (or features thereof) match the features associated with the new keyframe.

[0089] The mapper 124 can use the count of detected events associated with the keyframe (e.g., the new keyframe or an existing keyframe) corresponding to the frame 210 and features associated with the frame 210, to determine or update an event prior probability 216 associated with that keyframe. For example, in some cases, the mapper 124 can use a total and / or average count of events associated with the keyframe corresponding to the frame 210, and associated environment, and / or features associated with the frame 210, to determine or update an event prior probability 216 associated with that keyframe. The event prior probability 216 can include a value representing an estimated likelihood / probability of detecting an event of interest in an environment or location in an environment associated with the frame 210 and / or the features of the frame 210. For example, if the frame 210 was captured from a particular room and the visual features in the frame 210 correspond to the particular room or an area / object in the particular room, the event prior probability 216 can indicate an estimated likelihood of detecting an event of interest when the electronic device 100 is in the particular room (and / or in the area of the particular room) and / or when visual features in a frame captured in the particular room (or an area of the particular room) match visual features in a keyframe in the event data 202 that is associated with that particular room and / or the area / object in the particular room.PATENTQualcomm Ref. No. 2501466WO29

[0090] In some examples, the event prior probability 216 can be at least partly based on the count of detected events of interest recorded for a keyframe associated with the event prior probability 216. In some cases, the likelihood / probability value in the event prior probability 216 associated with a keyframe can increase as the count of detected events of interest associated with that keyframe increases. In some cases, the likelihood / probability value in the event prior probability’ 216 can be further based on one or more other factors such as. for example, an amount of time between the detected events of interest associated with the keyframe, an amount of time since the last detected event of interest associated with the keyframe and / or the last n number of detected events of interest associated with the keyframe, a type and / or characteristic of a detected event(s) of interest associated wi th the keyframe, a number of frames captured in an environment associated with the keyframe that have yielded a positive detection result relative to a number of frames captured in that environment that have yielded a negative detection result, one or more characteristics of the environment (e.g., a size of the environment, a number or density of potential events of interest in the environment, a common activity performed in the environment, etc.) associated with the keyframe, a frequency of use (and / or an amount of time of use) of the electronic device 100 in the environment associated with the keyframe (e.g., higher usage with lower positive detection results can be used to reduce a likelihood / probability value and vice versa), and / or any other factors.

[0091] For example, an increase or decrease in time between detected events of interest in an environment associated with the keyframe can be used to increase or decrease a likelihood / probability' value in the event prior probability 216. As another example, an increase or decrease in the number of frames captured in the environment that have yielded a positive detection relative to the number of frames captured in that environment that have yielded a negative detection result can be used to increase or decrease the likelihood / probability value in the event prior probability 216. As yet another example, a number of detected events of interest in an environment relative to an amount of use of the electronic device 100 in that environment can be used to decrease or increase the likelihood / probability value in the event prior probability 216 (e.g., more use with less detected events of interest can result in a lower likelihood / probability' value than less use with more detected events of interest or the same amount of detected events of interest).PATENTQualcomm Ref. No. 2501466WO30

[0092] The mapper 124 can provide the event prior probability 216 associated with the matched keyframe to the controller 126. The controller 126 can use the event prior probability 216 to control / adjust one or more settings associated with the image sensor 102 and / or the image processing associated with the image sensor 102. For example, the controller 126 can use the event prior probability 216 to adjust one or more settings to decrease a power consumption of the electronic device 100 when the event prior probability 216 indicates a lower likelihood / probability of an event of interest in a current environment of the electronic device 100, or adjust one or more settings to increase a performance and / or processing capabilities of the electronic device 100 (e.g., a performance of the image sensor 102) when the event prior probability 216 indicates a higher likelihood / probability of an event of interest in the current environment of the electronic device 100.

[0093] To illustrate, when the event prior probability 216 indicates a higher likelihood / probability of an event of interest in the current environment of the electronic device 100, the controller 126 can increase a framerate, resolution, scale factor, image stabilization, power mode, and / or other settings associated with the image sensor 102; invoke additional image sensors; implement an image processing pipeline and / or operations associated with higher performance, complexity, functionalities, and / or processing capabilities; invoke or initialize a higher-power or main camera device (e.g., image sensor 104); turn on an active depth transmitter system such as a structured light system or flood illuminator, dual camera system for depth and stereo or a time-of-flight camera component; etc.

[0094] On the other hand, when the event prior probability 216 indicates a lower likelihood / probability of an event of interest in the current environment of the electronic device 100, the controller 126 can turn off the image sensor 102; decrease a framerate, resolution, scale factor, image stabilization, and / or other settings associated with the image sensor 102; invoke a lower number of image sensors; implement an image processing pipeline and / or operations associated with lower power consumption, performance, complexity, functionalities, and / or processing capabilities; turn off an active depth transmitter system such as a structured light system or flood illuminator, dual camera system, a time-of-flight camera component; etc. This way, the controller 126 can increase power savings, performance, and / or capabilities of the electronic device 100 andPATENTQualcomm Ref. No. 2501466WO31associated components based on the likelihood / probability of a presence / occurrence of an event of interest in the current environment of the electronic device 100.

[0095] In some cases, the controller 126 can also modulate non-camera settings 220 based on the event prior probability 216 (e g., based on the likelihood / probability of a presence / occurrence of an event of interest in the current environment of the electronic device 100). For example, the controller 126 can (e.g., based on the likelihood / probability of a presence / occurrence of an event of interest in the current environment of the electronic device 100) turn on / off, increase / decrease a power mode, and / or increase / decrease a processing capability and / or complexity of one or more components, algorithms, services, etc., such as an audio algorithm(s) (e.g.. beamforming, etc.), location services (e.g., GNSS or GPS, WIFI, etc ), a tracking algorithm, an audio device (e.g., audio sensor 106), a non-camera workload, an additional processor, etc.

[0096] In some cases, the electronic device 100 can also leverage non-camera events to map events associated with an environment and control device settings (e.g., power states, operations, parameters, etc.) based on mapped events. For example, with reference to FIG. 2B, the electronic device 100 can use data 234 from non-camera sensors 232 to update the event data 202 (e.g., add new keyframes and associated detection counts, update existing keyframes and / or detection counts, remove existing keyframes and / or detection counts) and / or compute the event prior probability 236 for a matched keyframe.

[0097] The non-camera sensors 232 can include, for example and without limitation, an audio sensor (e.g., audio sensor 106), an IMU (e.g., IMU 108), a radar, a GNSS or GPS sensor / receiver, a wireless receiver (e.g., WIFI, cellular, etc.), etc. The data 234 from the non-camera sensor 232 can include, for example and without limitation, information about a location / position of the electronic device 100, a distance between the electronic device 100 and one or more objects, a location of one or more objects within an environment, a movement of the electronic device 100, sound captured in an environment, a time of one or more events, etc.

[0098] In some examples, the data 234 from the non-camera sensors 232 can be used to supplement data associated with updates (e.g., keyframes and / or associated data) to the event data 202. For example, the data 234 from the non-camera sensors 232 can be used to add timestamps of events associated with a keyframe added, updated or removed in thePATENTQualcomm Ref. No. 2501466WO32event data 202; indicate a location / position of the detected event associated with the keyframe; indicate a location / position of the electronic device 100 before, during, and / or after a detected event: an indication of movement of the electronic device 100 during the detected event, indicate a proximity of the electronic device 100 to the detected event, an indication of audio features associated with the detected event and / or environment, an indication of one or more characteristics of the environment (e.g., location, geometry, configuration, activity, objects, etc.), etc. The mapper 124 can use the data 234 in conjunction with features / descriptors and / or counts associated with keyframes in the event data 202 to help determine the likelihood / probability value in the event prior probability 236 of a matched keyframe; provide more granular information (e.g., location, activity, movement, time, etc.) about the environment, the electronic device 100, and / or the detected event associated with a matched keyframe; cull / eliminate stale keyframes; determine whether to add, update, or remove a keyframe (and / or associated information) to / in / fromthe event data 202; verify detected events; etc.

[0099] In some cases, the controller 126 can also use the data 234 from the noncamera sensors 232 to determine how or what settings to adjust / modulate as previously described. For example, the controller 126 can use the data 234 in conjunction with the event prior probability 236 to determine what setting of the image sensor 102 to adjust (and / or how), what settings from one or more other devices on the electronic device 100 to adjust (and / or how), which of the non-camera settings 220 to adjust (and / or how), etc. For example, as previously explained, in some cases, when the event prior probability indicates a higher likelihood of an event of interest occurring in a current environment of the electronic device 100, the controller 126 can increase a framerate of the image sensor 102 and / or activate a higher-power or main camera device with higher framerate capabilities (e.g., as compared to a camera device associated with image sensor 102). In this example, if the data 234 indicates a threshold amount of motion associated with the detected event of a matched keyframe and / or the electronic device 100, the controller 126 can increase the framerate of the image sensor 102 and / or the higher-power or main camera device more than if the data 234 indicates the amount of motion is below the threshold.

[0100] As another example, if the data 234 indicates that the electronic device 100 is approaching a location in an environment within a proximity of a location of a prior eventPATENTQualcomm Ref. No. 2501466WO33of interest, the controller 126 can activate a higher-power or main camera device (e.g., a camera device having higher-power capabilities / settings as compared to a camera device associated with image sensor 102) and / or increase a setting of the image sensor 102 prior to the electronic device 100 reaching the location of the prior event of interest. Similarly, if the data 234 indicates that the electronic device 100 is moving away from the environment, the controller 126 can modulate one or more settings (e.g., turn off the image sensor 102 or another device, reduce a power mode of the image sensor 102 or another device, reduce a processing complexity and / or power consumption, etc.) to reduce a power consumption by the electronic device 100 even if an event of interest is determined to have a higher likelihood / probability (e.g., based on the event prior probability) of occurring in the environment. As yet another example, if the event prior probability indicates a higher likelihood of an event of interest occurring in the environment of the electronic device 100 and the data 234 indicates a threshold amount of motion by the electronic device 100 and / or one or more objects in the environment, the controller 126 can activate, and / or increase a complexity / performance of, an image stabilization setting / operation to ensure better image stabilization of any frames capturing an event of interest in the environment.

[0101] FIG. 3A is a diagram illustrating an example process 300 for updating event data (e.g., event data 202). In this example, at block 302, the electronic device 100 can extract features from a frame captured by a camera device of the electronic device 100 (e.g., a camera device associated with image sensor 102, a camera device associated with image sensor 104).

[0102] At block 304, the electronic device 100 can determine, based on the extracted features, if an event of interest is detected in the frame. At block 306, if an event of interest is not detected in the frame, the electronic device 100 does not add anew keyframe to the event data. If an event of interest is detected in the frame, at block 308, the electronic device 100 can optionally determine if a timer has expired since a keyframe was created (e.g., was added to the event data) by the electronic device 100 and / or at block 312, the electronic device 100 can determine if a match has been identified between a keyframe in the event data and visual features extracted from a frame.PATENTQualcomm Ref. No. 2501466WO34

[0103] The timer can be programmable. In some cases, the timer (e.g., the amount of time configured to trigger expiration of the timer) can be determined based on one or more factors such as, for example, a usage history and / or pattern associated with the electronic device 100, a pattern of detection events (e.g., a pattern associated with previous detections of one or more events of interest), types of events of interest configured to trigger a detection event (e.g., trigger a detection of an event of interest), one or more characteristics of one or more environments, etc. In some examples, the timer can be set to prevent a larger number of keyframes from being created and / or to prevent key frames from being created too frequently.

[0104] For example, assume the electronic device 100 detects a QR code in a frame capturing the QR code from a restaurant menu on a refrigerator in a kitchen, and creates a keyframe associated with the QR code detected in the restaurant menu on the refrigerator. The detection of the QR code and the creation of the keyframe can indicate that the electronic device 100 is in a same room (e.g., the kitchen) as the QR code and is likely to be in that same room for at least a period of time. In this example, the timer can prevent the electronic device 100 from creating additional keyframes of events associated with that environment while the electronic device 100 is likely to remain in that environment. Accordingly, the timer can reduce the volume of keyframes created within a period of time, a power consumption from creating additional keyframes within the period of time, and a use of resources in creating the additional keyframes within the period of time.

[0105] If the timer has not expired, the process 300 can return to block 306, where the electronic device 100 determines not to add a new keyframe to the event data. If the timer has expired, at block 310, the electronic device 100 can restart or reset the timer. At block 312, the electronic device 100 can determine whether the features extracted from a frame at block 302 match features of a keyframe in the event data. For example, the electronic device 100 can compare the features extracted from the frame at block 302 and / or an associated descriptor with features in keyframes in the event data and / or associated descriptors.

[0106] At block 314, if the electronic device 100 finds a match between the features extracted from the frame at block 302 and features of a keyframe in the event data, thePATENTQualcomm Ref. No. 2501466WO35electronic device 100 can increment a count of detection events (e.g., a count of previous detections of one or more events of interest) associated with the matching keyframe in the event data. At block 316, if the electronic device 100 does not find a match betw een the features extracted from the frame at block 302 and features of any keyframes in the event data, the electronic device 100 can create a new' entry in the event data for the detection event (e.g., a detection of an event of interest) associated with the features extracted from the frame at block 302. In some examples, the new entry can include a keyframe containing the features extracted from the frame at block 302 and an event count indicating the number of occurrences of the event of interest associated with the detection event. In some examples, the new entry can also include a descriptor of the features associated with the keyframe.

[0107] In some cases, the process 300 may not implement a timer and / or check if a timer has expired as described with respect to block 308 and block 310. For example, with reference to FIG. 3B. in some cases, after determining that an event of interest has been detected at block 304, the electronic device 100 can proceed to block 312 to determine if the features extracted from the frame at block 302 match features of a keyframe in the event data.

[0108] In other cases, the process 300 may implement a timer but checking if the timer has expired may be performed at a different point in the process. For example, in some cases, the electronic device 100 can check if the timer has expired prior (e.g., as described with respect to block 308) determining whether an event of interest has been detected (e.g., as described with respect to block 304). In some examples, if the timer has expired, the electronic device 100 can restart the timer (e.g., as described with respect to block 310) before determining whether an event of interest has been detected (e.g., as described with respect to block 304) or after determining that an event of interest has been detected.

[0109] As previously mentioned, Al assistants, associated with an electronic device (e.g., smart glasses), can have access to rich, always-available visual context derived from images captured by one or more image sensors associated with the device. Al assistance is a popular use case for smart glasses (e.g., an XR head-mounted device). Running some large models (e.g., large machine learning models, such as LLMs) for the Al assistancePATENTQualcomm Ref. No. 2501466WO36may require more computational resources than are available on the smart glasses. Split assistance on smart glasses allows for such workloads to be offloaded onto a companion device (e.g., a smart phone). Image, text, and / or audio can be captured on the smart glasses and then sent to the companion device, where the large models can be loaded and ready to process user inputs (e.g., a user query). Generated text responses (e.g., to a user query) from these models may be converted into audio, and then sent back to the smart glasses (e.g., for playback to a user).

[0110] FIG. 4 shows an example system for split Al assistance in smart glasses. In particular, FIG. 4 is a diagram illustrating an example of a system 400 for split Al assistance in smart glasses 410, such as an XR head-mounted device (HMD). In FIG. 4. the smart glasses 410 and a smart phone 420 are shown. Both the smart glasses 410 and the smart phone 420 can be associated with a user. The smart phone 420 can be a companion device to the smart glasses 410. The smart glasses 410 can communicate with the smart phone 420 via a radio frequency (RF) signal (e.g., a six gigahertz signal 445), such as via a Bluetooth or Wi-Fi signal.

[0111] In FIG. 4, the smart glasses 410 are shown to include a camera / microphone 415, a speaker 425, an image pipeline 430, an image / audio encoder 435, and a decoder 440. The smart phone 420 is shown to include a decoder 450, an encoder 460, a speech-to-text (STT) engine 455, an audio generation engine 465, a decoding / token generation engine 470, and a prompt / image encoding engine 475.

[0112] A key performance indicator for an Al assistant interaction (e.g., with a user) is a time to first token (TTFT) on the output of the Al assistant. The TTFT corresponds to how7reactive the Al assistances are perceived by the users. FIG. 5 shows an example of a TTFT of an Al assistant. In particular. FIG. 5 is a diagram illustrating an example of a processing timeline 500 for artificial intelligence assistance that includes a TTFT 580. In FIG. 5, the horizontal axis of the processing timeline 500 denotes time. At the beginning of the processing timeline 500, a model is loaded 510. Images (e.g., captured by an image sensor of the device) are gathered 520 and a prompt is composed 530. During execution (e.g., by one or more processors) of a large language model (LLM) 570, the images (e.g., along with visual context associated with the images) and the prompt are encoded 540, a first token is decoded 550, and ongoing decoding 560 occurs. The TTFTPATENTQualcomm Ref. No. 2501466WO37580 is shown to be the time required for the encoding 540 of the images and prompt and the time required for the decoding of the first token 550.

[0113] In one or more aspects, an XR device (e.g., an AR device) can implement a low power camera (e.g., an always sensing camera, which may be referred to as an always-on camera) to augment the real (physical) world with virtual content, such as for room designing, virtual shopping, table top AR games, tum-by-tum navigation assistance, food and health monitoring, AR video calls, and / or virtual meetings. The device can accomplish this augmentation by mapping the physical world, localizing itself in that physical world, and positioning and / or rendering virtual content on a near-eye display (e.g., on the device) that is visible to the user (e.g., wearing the device). Many XR devices (e.g., AR devices) can utilize hand and / or fingertip tracking to allow for users to control interfaces in the augmented reality.

[0114] In one or more examples, mobile devices (e.g., XR devices, which are head mounted, and / or handheld devices) have been increasingly leveraging specialized ultralow power camera hardware for an always sensing camera (ASC). FIG. 6 shows examples of use cases for devices leveraging an ASC. In particular, FIG.6 is a diagram illustrating examples 600 of use cases 610, 620, 630 for an always sensing camera (e.g., an always on camera) in a device (e.g., a mobile device). In FIG. 6, a first use case 610 is for an always-on face unlock. For this use case 610, the always sensing camera can unlock the mobile device for usage by the user when the always sensing camera detects a face of the user within a captured image (e.g., captured by the always sensing camera).

[0115] A second use case 620 is for a vision-based context hub. For this use case 620, a camera 640 (e.g., an always sensing camera) and sensors (e.g., including a temperature sensor), which are implemented within a device (e.g., ahead-mounted device worn by a user), can obtain sensor data (e.g., including images and / or temperature data). The always sensing camera hardware can determine context (e.g., for a prompt) based on the sensor data.

[0116] A third use case 630 is for always-on gesture detection. For this use case 630. a camera (e.g., an always sensing camera), implemented within a device (e.g., a mobile device associated with a user), can capture images. The always sensing camera hardware can detect gestures (e.g., of the user) based on the captured images.PATENTQualcomm Ref. No. 2501466WO38

[0117] FIG. 7 shows an example system of a device with an always sensing camera. In particular, FIG. 7 is a diagram illustrating an example of a system 700 for a device with an always sensing camera (e.g., an always on camera, such as camera 640 of FIG. 6). In FIG. 7, the system 700 is shown to include an AON camera sensor 710 (e.g., an AON camera), a non-AON camera sensor 720 (e.g., a non-AON camera), a system on a chip (SOC) 730, and an off-chip dynamic random-access memory (DRAM) 740. The SOC 730 is shown to include an AON camera processing engine 750, amain camera processing engine 760, a graphic processing engine 770, a video processing engine 780, a CPU 790, and a DRAM subsystem 795.

[0118] In one or more examples, this specialized camera processing (e.g., by the AON camera processing engine 750, such as always sensing camera hardware) may be implemented as a parallel camera processing path (e.g., as shown in FIG 7) with optimizations, such as using a low-resolution and low-power sensor (e.g., the AON camera sensor 710), using on-chip static random-access memory (SRAM) rather than DRAM (e.g., the DRAM subsystem 795), using island voltage rails to reduce leakage, and / or using ring oscillators for clock sources rather than phase lock loops (PLLs).

[0119] In one or more aspects, it can be important for XR devices (e.g.. AR devices) to be able to track their own location in the physical world. In one or more examples, this tracking is inside-out 6 DOF tracking. This type of tracking is referred to as “inside-out” because the device can track itself without any external beacons or transmitters. The term 6 DOF refers to the device being able to track its own position in terms of three rotational vectors (e.g.. pitch, yaw. and roll) as well as three translational vectors (e.g., up / down. left / right, and forward / back).

[0120] In one or more examples, visual inertial odometry may be employed to accomplish inside-out 6 DOF tracking of a device. Visual inertial odometry is performed by fusing together visual data (e.g., obtained by one or more image sensors) with inertial data (e.g., obtained by gyroscopes and accelerometers) to measure a distance the device moved within the physical world. Visual inertial odometry is often also used to simultaneously determine a position (e.g., localize) of the device in the physical world as well as a map of the physical world.PATENTQualcomm Ref. No. 2501466WO39

[0121] FIG. 8 shows an example process for visual inertial odometry. In particular, FIG. 8 is a diagram illustrating an example of a process 800 for inside-out 6 DOF tracking using visual inertial odometry 810. In FIG. 8, during operation of the process 800, one or more processors perform visual inertial odometry 810 by fusing together camera frames 820 with accelerometer data 830 and gyroscope data 840 to determine a distance the device moved within the physical world. The one or more processors, based on the visual inertial odometry 810, can determine a position and / or orientation 850 of the device and can update a map 860 of the real world.

[0122] In one or more examples, the visual data(e.g., camera frames 820) is important in this process 800. Determining the position of the device can be based entirely on double-integration of the acceleration (e.g., a process called dead reckoning), which is subject to cumulative error (e.g., referred to as drift) as time proceeds. With the visual data (e.g., camera frames 820), visual landmarks can be used to calibrate the odometry and to eliminate this cumulative error. For this reason. XR devices (e.g.. AR devices) can generally be assumed to have one or more world-facing camera sensors, a 3D map of the environment, and an understanding of the device position in that environment.

[0123] FIG. 9 shows an example device determining visual context from images. In particular, FIG. 9 is a diagram illustrating an example 900 of a device 910 (e.g., a wearable device, such as an XR device, for example an AR device) capable of capturing images and determining visual context from the images. In FIG. 9, the device 910 may include an always sensing camera (e g., an AON image sensor). The device 910 (e.g., the AON camera sensor) is shown to capture images of a scene. One image includes a QR code 920, and another image includes a dog 930.

[0124] One or more processors (e.g., within always sensing camera hardware) of the device 910. based on the images, can detect objects within the images (e.g.. the QR code 920 and the dog 930). The one or more processors (e g., within the ways sensing camera hardware) of the device 910, based on the detected objects, can determine visual context for the scene. For example, the one or more processors can determine, based on the detection of the QR code 920, a restaurant as context for the scene (e.g., because the QR code 920 is associated with a menu for the restaurant). For another example, the one orPATENTQualcomm Ref. No. 2501466WO40more processors can determine, based on the detection of the dog 930. a dog park as context for the scene (e.g., because dogs are associated with dog parks).

[0125] FIGS. 10 shows an example process for Al assistance. In particular, FIG. 10 is a diagram illustrating an example of a process 1000 for Al assistance. In FIG. 10, the horizontal axis denotes time. During operation of the process 1000 of FIG. 10, at a first time duration 1010, an always sensing camera (e.g., AON image sensor) of a device (e.g., an XR device, such as an AR device) can obtain one or more images (e.g., including a kitchen sink and a salad bowl) of a scene. One or more processors (e.g., of always sensing camera hardware) of the device can detect, based on the images, objects (e.g., a kitchen sink and a salad bowl) within the one or more images. The one or more processors (e.g.. of always sensing camera hardware), based on the detected images, can determine context of the scene. For example, the one or more processors can determine a kitchen as context for the scene based on the detected kitchen sink and salad bowl. The one or more processors can store the context (e.g., kitchen) along with a timestamp (e.g., corresponding to a specific time and day of when the one or more images w ere obtained) within a journal 1050a (e.g., a log).

[0126] At a second time duration 1020, the always sensing camera (e.g., AON image sensor) of the device (e.g., an XR device, such as an AR device) can obtain one or more images (e.g., including a cookbook with a recipe) of a scene. One or more processors (e.g., of alw ays sensing camera hardw are) of the device can detect, based on the images, objects (e.g., a cookbook with a recipe) within the one or more images. The one or more processors (e.g., of always sensing camera hardware), based on the detected images, can determine context of the scene. For example, the one or more processors can determine a recipe as context for the scene based on the detected cookbook with a recipe. The one or more processors can store the context (e g., recipe) along with a timestamp (e.g., corresponding to a specific time and day of when the one or more images were obtained) within the journal 1050a (e.g., to produce an updated journal 1050b).

[0127] At a third time duration 1030, one or more processors of the device (e.g., an XR device, such as an AR device) can receive a query’ 1060 from the user of the device. In one or more examples, the query 1060 is "How much dough do I need for a dozenPATENTQualcomm Ref. No. 2501466WO41cookies?'’ The one or more processors, based on the context (e.g., kitchen and recipe) within the journal 1050b and the query 1060, can determine an Al prompt 1070.

[0128] At a fourth time duration 1040, the one or more processors, based on the prompt 1070, can determine a response 1080 to the query 1060. In one or more examples, the response 1080 is “6 cups.” The response 1080 is “6 cups” because the context including a kitchen and a recipe can lead to the interpretation of the term “dough” in the query 1060 as referring to a mixture of flour and water.

[0129] In one or more aspects, as mentioned, Al assistance is a popular use case for smart glasses (e.g., an XR head-mounted device). Running large models (e.g., large machine learning models, such as LLMs) for the Al assistance can require more compute resources than are available on the smart glasses. Split assistance on smart glasses allows for such workloads to be offloaded onto a companion device (e.g., a smart phone).

[0130] For split assistance on a device (e.g., smart glasses), there is a balancing between processing Al assistant workloads locally and remotely. Running Al assistant workloads (e.g., LLMs) on smart glasses can be power-prohibitive. However, it is possible to run smaller Al assistance workloads (e.g., SLMs) on smart glasses, but these SLMs offer less intelligence than LLMs. Offloading Al assistant workloads onto a companion device (e.g., a smart phone) can introduce additional latency in terms of TTFT. Therefore, improved systems and techniques for optimized split Al in smart glasses can be useful.

[0131] In one or more aspects, the systems and techniques provide solutions for optimized split Al in smart glasses. In one or more examples, the systems and techniques provide an Al assistant mode selector that can select among various different Al assistant strategies for an electronic device (e.g., smart glasses). In some examples, the Al assistant mode selector (e.g., a modal Al assistant apparatus) can select from among a plurality of Al assistant strategies based on a user prompt, one or more contexts (e.g., one or more ASC-based contexts), and / or one or more device properties (e.g., device contexts).

[0132] In one or more examples, an Al assistant strategy involves executing the Al assistant workload remotely (e.g., on a companion device, such as a smart phone). This strategy' allows for low-power on the smart glasses, a longer latency, and a higherPATENTQualcomm Ref. No. 2501466WO42intelligence model. For example, when this Al assistant strategy is chosen, the prompt can be uploaded to chat GPT in a cloud with open Al.

[0133] In some examples, another Al assistant strategy involves executing a high-intelligence Al assistant workload locally (e.g., on the smart glasses themselves). This strategy’ allows for high power on the smart glasses, a shorter latency, and a higher intelligence model. For example, this Al assistant strategy may be chosen when the smart glasses have a lot of available power to utilize and a shorter latency’ is desired.

[0134] In one or more examples, another Al assistant strategy involves executing a low-intelligence Al assistant workload locally. This strategy allows for medium power on the smart glasses, a shorter latency, and a lower intelligence model. For example, this Al assistant strategy can be chosen for simple queries that do not require a high level of intelligence to answer.

[0135] In some examples, another Al assistant strategy involves parallel execution of a low-intelligence Al assistant workload locally and an Al assistant workload remotely. This strategy allows for a medium power on the smart glasses, a high responsiveness, and a delayed intelligence. In one or more examples, an SLM can be run locally (e.g., on the device), while an LLM can be run remotely (e.g., a location remote from the device, such as in a cloud). For these examples, the SLM can generate an answer to the query locally. The query’ can be sent from the device to the LLM remotely to generate an answer remotely. The answer from the LLM can then be sent back to the device. Locally, on the device, the SLM can receive the answer from the LLM, and merge the answer from the LLM with the answer that the SLM generated to generate a final answer to the query.

[0136] In some examples, an SLM can be run locally (e.g., on the device), while an LLM as well as the SLM can be run remotely. For these examples, the SLM can generate an answer to the query locally. The query' can be sent from the device to a remote location (e.g., a cloud) to generate an answer remotely. The remote location (e.g., a cloud) can have a copy of the SLM that is loaded onto the device. The SLM in the remote location (e.g., a cloud) can generate an answer to the query. An LLM in the remote location (e.g.. a cloud) can also generate an answer to the query'. The two answers generated remotely can be sent to the device. The SLM on the device can merge all of the answers together to generate a final answer to the query.PATENTQualcomm Ref. No. 2501466WO43

[0137] FIG. 11 shows an example system for selecting Al assistant strategies. In particular, FIG. 11 is a diagram illustrating an example of a system 1100 for selecting Al assistant strategies 1160 (e.g., Al assistant strategy #1, Al assistant strategy #2,...Al assistant strategy7#n) for processing a query 1170 from a user. In FIG. 11, an image sensor(s) 1110 (e.g., a camera sensor(s), such as an AON image sensor(s)), an ASC event monitoring engine 1120, and a modal Al assistant 1140 are shown. A device (e.g., an XR device, such as an HMD) associated with a user can include the image sensor(s) 1110, the ASC event monitoring engine 1120, and the modal Al assistant 1140. The ASC event monitoring engine 1120 is shown to include an event journal 1130. The modal Al assistant 1140 is shown to include an assistant mode selector 1150.

[0138] During operation of the system 1100 for Al assistance, the image sensor(s) 1110 of the device associated with a user can obtain a plurality7of images of a scene. In one or more examples, each image of the plurality of images can be obtained at a respective time. In some cases, the system 1100 may include other sensors, such as one or more LiDAR sensors, one or more radar sensors, etc. The ASC event monitoring engine 1120 can determine, based on the plurality7of images (or other sensor data, such as LiDAR data, radar data, etc.), one or more contexts for the scene.

[0139] The ASC event monitoring engine 1120 can determine, based on the one or more contexts, one or more events. The ASC event monitoring engine 1120 can monitor, based on image data from the image sensor(s) 1110, the scene for the one or more events. The ASC event monitoring engine 1120 can detect, based on monitoring the scene for the one or more events, at least one event of the one or more events.

[0140] In one or more examples, the one or more contexts can be stored within the event journal 1130 (e.g., a log). In some examples, one or more images of the plurality of images associated with the one or more contexts can be stored within the event journal 1130.

[0141] The modal Al assistant 1140 can receive the one or more contexts from the ASC event monitoring engine 1120. The modal Al assistant 1140 can receive a query 1170 based on user input from the user. The modal Al assistant 1140 can generate a prompt based on the query71170 and the one or more contexts. The modal Al assistant 1140 can receive one or more device properties 1180 (e.g., other device context). In onePATENTQualcomm Ref. No. 2501466WO44or more examples, the one or more device properties 1180 can include a battery' capacity of the device, network (e.g.. Bluetooth or Wi-Fi) connection conditions (e.g.. signal to noise ratio), and / or a 6 DoF pose for each image sensor 1110 of the one or more image sensors 1110 of the device.

[0142] The assistant mode selector 1150 of the modal Al assistant 1140 can determine an Al assistance processing strategy 1160 of a plurality of Al assistance processing strategies 1160 based on the one or more contexts, the prompt, and / or the one or more device properties 1180. The modal Al assistant 1140 can process, based on the chosen Al assistance strategy 1160, the query 1170 to generate an answer to the query 1170.

[0143] In one or more examples, each Al assistance processing strategy 1160 of the plurality' of Al assistance processing strategies 11 0 can be associated with one or more locations (e.g., a location local on the device and / or a location remote from the device) for the Al assistance processing and one or more different machine learning models (e.g.. an LLM and / or an SLM). In one or more examples, the one or more different machine learning models can include a first machine learning model (e.g., an LLM) and / or a second machine learning model (e.g., an SLM), where the second machine learning model has fewer parameters than the first machine learning model.

[0144] In one or more aspects, each context of the one or more contexts and / or each device property' of the one or more device properties can have a respective weight associated with each Al assistance processing strategy 1160 of the plurality of Al assistance processing strategies 1160. In one or more examples, the context of “driving” can indicate navigational urgency and, as such, can have a high weighting for low-latency strategies. In some examples, the device property' 1180 (e.g., device context) of “low battery” can indicate a need to prioritize power and, as such, can have a high weighting for low-power strategies. In one or more examples, the device property 1180 (e.g., device context) of “poor Wi-Fi conditions” may indicate the need to prioritize local processing (versus remote processing). In some examples, the context of “human face detected” may indicate the need for a low-latency initial response (e.g., a vocal filler or similar) in order to get a word in on the conversation, but allow for some short-term relaxation of intelligence (e.g., such an indicator may have a high weighting for a hybrid strategy, which uses an SLM for low latency and a remote LLM for long-term higher intelligence).PATENTQualcomm Ref. No. 2501466WO45

[0145] In some examples, determining the Al assistance processing strategy 1160 of the plurality of Al assistance processing strategies 1160 can be further based on the Al assistance processing strategy 1160 having a highest weighted sum of weights of the plurality of Al assistance processing strategies 1160.

[0146] In some aspects, a local SLM may be given the one or more contexts, the one or more device properties 1180 (e.g., device context), and the prompt, and asked to choose the most appropriate Al assistance processing strategy 1160. As such, the Al assistance processing strategy' 1160 of the plurality' of Al assistance processing strategies 1160 can be determined using a SLM locally on the device.

[0147] FIG. 12 is a flow chart illustrating an example of a process 1200 for Al assistance. The process 1200 can be performed by a computing device (e.g., a computing device or computing system 1300 of FIG. 13) or by acomponentor system (e.g., a chipset, one or more processors central processing units (CPUs), digital signal processors (DSPs), graphics processing units (GPUs), any combination thereof, and / or other type of processor(s), or other component or system) of the computing device. The operations of the process 1200 may be implemented as software components that are executed and run on one or more processors (e.g.. processor 1310 of FIG. 13, or other processor(s)). Further, the transmission and reception of signals by the computing device in the process 1200 may be enabled, for example, by one or more antennas and / or one or more transceivers (e.g., wireless transceivers )).

[0148] At block 1202, the computing device (or component thereof) can obtain, from one or more sensors (e.g., one or more image sensors, LiDAR sensors, radar sensors, etc.) of a device associated with a user, sensor data associated with a scene (e.g., a plurality' of images of a scene, LiDAR data representing an environment of the scene such as depth information, radar data representing the environment of the scene, etc.). For instance, the one or more sensors includes one or more image sensors, in which case the sensor data associated with the scene includes a plurality' of images of the scene. In some cases, each image of the plurality' of images is obtained at a respective time. In some aspects, the one or more images sensors includes one or more always-on image sensors. In some aspects, the computing device can be the device associated with the user or can be a computingPATENTQualcomm Ref. No. 2501466WO46system or component of the device. In some examples, the device is an extended reality (XR) device, such as a head-mounted device.

[0149] At block 1204, the computing device (or component thereof) can determine, based on the sensor data, one or more contexts for the scene. In some aspects, the computing device (or component thereof) can determine, based on the one or more contexts, one or more events. In some cases, the computing device (or component thereof) can monitor, based on data from the one or more sensors (e.g., image data from the one or more image sensors), the scene for the one or more events. In some aspects, the computing device (or component thereof) can detect, based on monitoring the scene for the one or more events, at least one event of the one or more events. In some examples, the computing device (or component thereof) can store the one or more contexts within a log. In some cases, the computing device (or component thereof) can store at least a portion of the sensor data associated with the one or more contexts (e.g., one or more images of the plurality of images associated with the one or more contexts) within the log.

[0150] At block 1206, the computing device (or component thereof) can receive a query based on user input from the user.

[0151] At block 1208, the computing device (or component thereof) can generate a prompt based on the query and the one or more contexts.

[0152] At block 1210, the computing device (or component thereof) can determine an Al assistance processing strategy from a plurality of Al assistance processing strategies based on the one or more contexts, the prompt, one or more device properties, or a combination thereof. In some cases, the computing device (or component thereof) can receive the one or more device properties. In some aspects, the one or more device properties include a battery capacity of the device, a network connection signal to noise ratio, a six degrees of freedom (6 DoF) pose for each of the one or more sensors (e.g., for each image sensor of the one or more image sensors) of the device, any combination thereof, and / or other device properties.

[0153] In some aspects, each Al assistance processing strategy of the plurality of Al assistance processing strategies is associated with one or more locations for the AlPATENTQualcomm Ref. No. 2501466WO47assistance processing and one or more different machine learning models. In some cases, the one or more locations include a location on the device or a location remote from the device. In some aspects, the one or more different machine learning models include a first machine learning model and / or a second machine learning model, where the second machine learning model has fewer parameters than the first machine learning model. In some aspects, the computing device (or component thereof) can determine the Al assistance processing strategy of the plurality of Al assistance processing strategies using a small language model (SLM).

[0154] In some aspects, each context of the one or more contexts and / or each device property of the one or more device properties has a respective weight associated with each Al assistance processing strategy of the plurality of Al assistance processing strategies. In such aspects, the computing device (or component thereof) can determine the Al assistance processing strategy of the plurality of Al assistance processing strategies further based on the Al assistance processing strategy having a highest weighted sum of weights of the plurality of Al assistance processing strategies.

[0155] At block 1212, the computing device (or component thereof) can process, based on the Al assistance strategy, the query to generate an answer to the query.

[0156] In some cases, the computing device of process 1200 may include various components, such as one or more input devices, one or more output devices, one or more processors, one or more microprocessors, one or more microcomputers, one or more cameras, one or more sensors, and / or other component(s) that are configured to carry out the steps of processes described herein. In some examples, the computing device may include a display, one or more network interfaces configured to communicate and / or receive the data, any combination thereof, and / or other component(s). The one or more network interfaces may be configured to communicate and / or receive wired and / or wireless data, including data according to the 3G, 4G, 5G, and / or other cellular standard, data according to the Wi-Fi (802.1 lx) standards, data according to the Bluetooth™ standard, data according to the Internet Protocol (IP) standard, and / or other types of data.

[0157] The components of the computing device of process 1200 can be implemented in circuitry. For example, the components can include and / or can be implemented using electronic circuits or other electronic hardware, which can include one or morePATENTQualcomm Ref. No. 2501466WO48programmable electronic circuits (e g., microprocessors, graphics processing units (GPUs), digital signal processors (DSPs), central processing units (CPUs), and / or other suitable electronic circuits), and / or can include and / or be implemented using computer software, firmware, or any combination thereof, to perform the various operations described herein. The computing device may further include a display (as an example of the output device or in addition to the output device), a network interface configured to communicate and / or receive the data, any combination thereof, and / or other component(s). The network interface may be configured to communicate and / or receive Internet Protocol (IP) based data or other type of data.

[0158] The process 1200 is illustrated as a logical flow diagram, the operations of which represent a sequence of operations that can be implemented in hardware, computer instructions, or a combination thereof. In the context of computer instructions, the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations can be combined in any order and / or in parallel to implement the processes.

[0159] Additionally, the process 1200 may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g.. executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. As noted above, the code may be stored on a computer-readable or machine-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. The computer-readable or machine-readable storage medium may be non-transitory.

[0160] FIG. 13 is a block diagram illustrating an example of a computing system 1300. which may be employed for optimized split Al in smart glasses. In particular, FIG.13 illustrates an example of computing system 1300, which can be for example any computing device making up internal computing system, a remote computing system, aPATENTQualcomm Ref. No. 2501466WO49camera, or any component thereof in which the components of the system are in communication with each other using connection 1305. Connection 1305 can be a physical connection using a bus, or a direct connection into processor 1310, such as in a chipset architecture. Connection 1305 can also be a virtual connection, networked connection, or logical connection.

[0161] In some aspects, computing system 1300 is a distributed system in which the functions described in this disclosure can be distributed within a datacenter, multiple data centers, a peer network, etc. In some aspects, one or more of the described system components represents many such components each performing some or all of the function for which the component is described. In some aspects, the components can be physical or virtual devices.

[0162] Example system 1300 includes at least one processing unit (CPU or processor) 1310 and connection 1305 that communicatively couples various system components including system memory 1315, such as read-only memory (ROM) 1320 and random access memory (RAM) 1325 to processor 1310. Computing system 1300 can include a cache 1312 of high-speed memory connected directly with, in close proximity to, or integrated as part of processor 1310.

[0163] Processor 1310 can include any general purpose processor and a hardware service or software sen ice, such as services 1332, 1334, and 1336 stored in storage device 1330, configured to control processor 1310 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor 1310 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

[0164] To enable user interaction, computing system 1300 includes an input device 1345. which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system 1300 can also include output device 1335, which can be one or more of a number of output mechanisms. In some instances, multimodal systems can enable a user to provide multiple types of input / output to communicate with computing system 1300.PATENTQualcomm Ref. No. 2501466WO50

[0165] Computing system 1300 can include communications interface 1340, which can generally govern and manage the user input and system output. The communication interface may perform or facilitate receipt and / or transmission wired or wireless communications using wired and / or wireless transceivers, including those making use of an audio jack / plug, a microphone jack / plug, a universal serial bus (USB) port / plug, an Apple™ Lightning™ port / plug, an Ethernet port / plug, a fiber optic port / plug, a proprietary wired port / plug, 3G, 4G, 5G and / or other cellular data network wireless signal transfer, a Bluetooth™ wireless signal transfer, a Bluetooth™ low energy (BLE) wireless signal transfer, an IBEACON™ wireless signal transfer, a radio-frequency identification (RFID) wireless signal transfer, near-field communications (NFC) wireless signal transfer, dedicated short range communication (DSRC) wireless signal transfer. 802.11 Wi-Fi wireless signal transfer, wireless local area network (WLAN) signal transfer, Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Infrared (IR) communication wireless signal transfer, Public Switched Telephone Network (PSTN) signal transfer, Integrated Services Digital Network (ISDN) signal transfer, ad-hoc network signal transfer, radio wave signal transfer, microwave signal transfer, infrared signal transfer, visible light signal transfer, ultraviolet light signal transfer, wireless signal transfer along the electromagnetic spectrum, or some combination thereof.

[0166] The communications interface 1340 may also include one or more range sensors (e g., LiDAR sensors, laser range finders, RF radars, ultrasonic sensors, and infrared (IR) sensors) configured to collect data and provide measurements to processor 1310, whereby processor 1310 can be configured to perform determinations and calculations needed to obtain various measurements for the one or more range sensors. In some examples, the measurements can include time of flight, wavelengths, azimuth angle, elevation angle, range, linear velocity and / or angular velocity7, or any combination thereof. The communications interface 1340 may also include one or more receivers or transceivers that are used to determine a location of the computing system 1300 based on receipt of one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the US-based GPS, the Russia-based Global Navigation Satellite System (GLONASS), the China-based BeiDou Navigation Satellite System (BDS), and the Europe-based Galileo GNSS. There is noPATENTQualcomm Ref. No. 2501466WO51restriction on operating on any particular hardware arrangement, and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

[0167] Storage device 1330 can be a non-volatile and / or non-transitory and / or computer-readable memory device and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, a floppy disk, a flexible disk, a hard disk, magnetic tape, a magnetic strip / stripe, any other magnetic storage medium, flash memory, memristor memory', any other solid-state memory, a compact disc read only memory (CD-ROM) optical disc, a rewritable compact disc (CD) optical disc, digital video disk (DVD) optical disc, a blu-ray disc (BDD) optical disc, a holographic optical disk, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a Memory' Stick® card, a smartcard chip, a EMV chip, a subscriber identity module (SIM) card, a mini / micro / nano / pico SIM card, another integrated circuit (IC) chip / card. random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory' (EPROM), electrically erasable programmable read-only memory (EEPROM), flash EPROM (FLASHEPROM), cache memory (e.g., Level 1 (LI) cache, Level 2 (L2) cache, Level 3 (L3) cache. Level 4 (L4) cache, Level 5 (L5) cache, or other (L#) cache), resistive random-access memory (RRAM / ReRAM), phase change memory (PCM), spin transfer torque RAM (STT-RAM), another memory' chip or cartridge, and / or a combination thereof.

[0168] The storage device 1330 can include software services, servers, services, etc., that when the code that defines such software is executed by the processor 1310, it causes the system to perform a function. In some aspects, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor 1310, connection 1305, output device 1335, etc., to carry out the function. The term ‘‘computer-readable medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and / or data. A computer-readable medium may include a non-PATENTQualcomm Ref. No. 2501466WO52transitory medium in which data can be stored and that does not include carrier waves and / or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory. memory' or memory devices. A computer-readable medium may have stored thereon code and / or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may’ be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, or the like.

[0169] Specific details are provided in the description above to provide a thorough understanding of the aspects and examples provided herein, but those skilled in the art will recognize that the application is not limited thereto. Thus, while illustrative aspects of the application have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art. Various features and aspects of the above-described application may be used individually or jointly. Further, aspects can be utilized in any number of environments and applications beyond those described herein without departing from the broader scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. For the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate aspects, the methods may be performed in a different order than that described.

[0170] For clarity' of explanation, in some instances the present technology may be presented as including individual functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software. Additional components may be used other than those shown in the figures and / or described herein. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order notPATENTQualcomm Ref. No. 2501466WO53to obscure the aspects in unnecessary' detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the aspects.

[0171] Further, those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality7is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0172] Individual aspects may be described above as a process or method which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0173] Processes and methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions can include, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or a processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, source code. Examples of computer-PATENTQualcomm Ref. No. 2501466WO54readable media that may be used to store instructions, information used, and / or information created during methods according to described examples include magnetic or optical disks, flash memory. USB devices provided with non-volatile memory, networked storage devices, and so on.

[0174] In some aspects the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bitstream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

[0175] Those of skill in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof, in some cases depending in part on the particular application, in part on the desired design, in part on the corresponding technology, etc.

[0176] The various illustrative logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and can take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary7tasks (e.g., a computer-program product) may be stored in a computer-readable or machine-readable medium. A processor(s) may perform the necessary7tasks. Examples of form factors include laptops, smart phones, mobile phones, tablet devices or other small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

[0177] The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functions described in the disclosure.PATENTQualcomm Ref. No. 2501466WO55

[0178] The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general purposes computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, performs one or more of the methods, algorithms, and / or operations described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may comprise memory or data storage media, such as random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory' (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory', magnetic or optical data storage media, and the like. The techniques additionally, or alternatively, may be realized at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer, such as propagated signals or waves.

[0179] The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, an application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term “processor,” as used herein may refer to any of the foregoingPATENTQualcomm Ref. No. 2501466WO56structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein.

[0180] One of ordinary skill will appreciate that the less than (“<”) and greater than (“>”) symbols or terminology' used herein can be replaced with less than or equal to (“<”) and greater than or equal to (“>”) symbols, respectively, without departing from the scope of this description.

[0181] Where components are described as being '‘configured to” perform certain operations, such configuration can be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof.

[0182] The phrase “coupled to” or “communicatively coupled to” refers to any component that is physically connected to another component either directly or indirectly, and / or any component that is in communication with another component (e.g., connected to the other component over a wired or wireless connection, and / or other suitable communication interface) either directly or indirectly.

[0183] Claim language or other language reciting “at least one of’ a set and / or “one or more” of a set indicates that one member of the set or multiple members of the set (in any combination) satisfy the claim. For example, claim language reciting '‘at least one of A and B” or “at least one of A or B” means A, B, or A and B. In another example, claim language reciting “at least one of A, B, and C” or “at least one of A, B, or C” means A, B, C, or A and B, or A and C, or B and C, A and B and C, or any duplicate information or data (e.g., A and A, B and B, C and C, A and A and B, and so on), or any other ordering, duplication, or combination of A, B, and C. The language “at least one of’ a set and / or “one or more” of a set does not limit the set to the items listed in the set. For example, claim language reciting “at least one of A and B” or “at least one of A or B” may mean A, B. or A and B, and may additionally include items not listed in the set of A and B. The phrases “at least one” and “one or more” are used interchangeably herein.

[0184] Claim language or other language reciting “at least one processor configured to,” “at least one processor being configured to,” “one or more processors configured to,”PATENTQualcomm Ref. No. 2501466WO57“one or more processors being configured to,'’ or the like indicates that one processor or multiple processors (in any combination) can perform the associated operation(s). For example, claim language reciting “at least one processor configured to: X, Y, and Z” means a single processor can be used to perform operations X, Y, and Z; or that multiple processors are each tasked with a certain subset of operations X, Y, and Z such that together the multiple processors perform X, Y. and Z; or that a group of multiple processors work together to perform operations X, Y, and Z. In another example, claim language reciting “at least one processor configured to: X, Y, and Z” can mean that any single processor may only perform at least a subset of operations X, Y, and Z.

[0185] Where reference is made to one or more elements performing functions (e.g., steps of a method), one element may perform all functions, or more than one element may collectively perform the functions. When more than one element collectively performs the functions, each function need not be performed by each of those elements (e.g., different functions may be performed by different elements) and / or each function need not be performed in whole by only one element (e.g., different elements may perform different sub-functions of a function). Similarly, where reference is made to one or more elements configured to cause another element (e.g., an apparatus) to perform functions, one element may be configured to cause the other element to perform all functions, or more than one element may collectively be configured to cause the other element to perform the functions.

[0186] Where reference is made to an entity (e.g., any entity or device described herein) performing functions or being configured to perform functions (e.g., steps of a method), the entity may be configured to cause one or more elements (individually or collectively) to perform the functions. The one or more components of the entity' may include at least one memory, at least one processor, at least one communication interface, another component configured to perform one or more (or all) of the functions, and / or any combination thereof. Where reference to the entity performing functions, the entity may be configured to cause one component to perform all functions, or to cause more than one component to collectively perform the functions. When the entity is configured to cause more than one component to collectively perform the functions, each function need not be performed by each of those components (e.g., different functions may be performed by different components) and / or each function need not be performed in wholePATENTQualcomm Ref. No. 2501466WO58by only one component (e.g., different components may perform different sub-functions of a function).

[0187] The various illustrative logical blocks, modules, engines, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, firmware, or combinations thereof. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, engines, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

[0188] The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general purposes computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. Any features described as engines, modules, or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, performs one or more of the methods described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may comprise memory or data storage media, such as random access memory' (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like. The techniques additionally, or alternatively, may be realized at least in part by a computer-readable communication medium that carries or communicates program code in the formPATENTQualcomm Ref. No. 2501466WO59of instructions or data structures and that can be accessed, read, and / or executed by a computer, such as propagated signals or waves.

[0189] The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, an application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general purpose processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated software modules or hardware modules configured for encoding and decoding, or incorporated in a combined video encoder-decoder (CODEC).

[0190] Illustrative aspects of the disclosure include:

[0191] Aspect 1. An apparatus for artificial intelligence (Al) assistance, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: obtain, from one or more sensors of a device associated with a user, sensor data associated with a scene; determine, based on the sensor data, one or more contexts for the scene; receive a query based on user input from the user; generate a prompt based on the query and the one or more contexts; determine an Al assistance processing strategy' from a plurality7of Al assistance processing strategies based on the one or more contexts, the prompt, one or more device properties, or a combination thereof; and process, based on the Al assistance strategy, the query to generate an answer to the query.

[0192] Aspect 2. The apparatus of Aspect 1, wherein each Al assistance processing strategy of the plurality of Al assistance processing strategies is associated with one orPATENTQualcomm Ref. No. 2501466WO60more locations for the Al assistance processing and one or more different machine learning models.

[0193] Aspect 3. The apparatus of Aspect 2, wherein the one or more locations comprise at least one of a location on the device or a location remote from the device.

[0194] Aspect 4. The apparatus of any of Aspects 2 or 3, wherein the one or more different machine learning models comprise at least one of a first machine learning model or a second machine learning model, wherein the second machine learning model has fewer parameters than the first machine learning model.

[0195] Aspect 5. The apparatus of any of Aspects 1 to 4, wherein at least one of each context of the one or more contexts or each device property of the one or more device properties has a respective weight associated with each Al assistance processing strategy of the plurality of Al assistance processing strategies.

[0196] Aspect 6. The apparatus of Aspect 5, wherein the at least one processor is configured to determine the Al assistance processing strategy of the plurality of Al assistance processing strategies further based on the Al assistance processing strategy having a highest weighted sum of weights of the plurality of Al assistance processing strategies.

[0197] Aspect 7. The apparatus of any of Aspects 1 to 6, wherein the at least one processor is configured to determine the Al assistance processing strategy of the plurality of Al assistance processing strategies using a small language model (SLM).

[0198] Aspect 8. The apparatus of any of Aspects 1 to 7, wherein the one or more device properties comprise at least one of a battery capacity’ of the device, a network connection signal to noise ratio, or a six degrees of freedom (6 DoF) pose for each of the one or more sensors of the device.

[0199] Aspect 9. The apparatus of any of Aspects 1 to 9, wherein the at least one processor is configured to: determine, based on the one or more contexts, one or more events; monitor, based on data from the one or more sensors, the scene for the one or more events; and detect, based on monitoring the scene for the one or more events, at least one event of the one or more events.PATENTQualcomm Ref. No. 2501466WO61

[0200] Aspect 10. The apparatus of any of Aspects 1 to 10, wherein the at least one processor is configured to store the one or more contexts within a log.

[0201] Aspect 11. The apparatus of Aspect 10, wherein the at least one processor is configured to store at least a portion of the sensor data one or more images of the plurality of images that is associated with the one or more contexts within the log.

[0202] Aspect 12. The apparatus of any of Aspects 1 to 11. wherein the device is an extended reality (XR) device.

[0203] Aspect 13. The apparatus of Aspect 12, wherein the XR device is a headmounted device.

[0204] Aspect 14. The apparatus of any of Aspects 1 to 13. wherein one or more sensors includes one or more image sensors, and wherein the sensor data associated with the scene includes a plurality of images of the scene.

[0205] Aspect 15. The apparatus of any of Aspect 14, wherein each image of the plurality of images is obtained at a respective time.

[0206] Aspect 16. The apparatus of any of Aspects 14 or 15, wherein the one or more images sensors includes one or more always-on image sensors.

[0207] Aspect 17. A method for artificial intelligence (Al) assistance, the method comprising: obtaining, from one or more sensors of a device associated with a user, sensor data associated with a scene; determining, based on the sensor data, one or more contexts for the scene; receiving a query based on user input from the user; generating a prompt based on the query and the one or more contexts; determining an Al assistance processing strategy’ from a plurality of Al assistance processing strategies based on the one or more contexts, the prompt, one or more device properties, or a combination thereof and processing, based on the Al assistance strategy’, the query to generate an answer to the query.

[0208] Aspect 18. The method of Aspect 17, wherein each Al assistance processing strategy' of the plurality of Al assistance processing strategies is associated with one or more locations for the Al assistance processing and one or more different machine learning models.PATENTQualcomm Ref. No. 2501466WO62

[0209] Aspect 19. The method of Aspect 18, wherein the one or more locations comprise at least one of a location on the device or a location remote from the device.

[0210] Aspect 20. The method of any of Aspects 18 or 19, wherein the one or more different machine learning models comprise at least one of a first machine learning model or a second machine learning model, wherein the second machine learning model has fewer parameters than the first machine learning model.

[0211] Aspect 21. The method of any of Aspects 17 to 20, wherein at least one of each context of the one or more contexts or each device property of the one or more device properties has a respective weight associated w ith each Al assistance processing strategy of the plurality of Al assistance processing strategies.

[0212] Aspect 22. The method of Aspect 21, wherein determining the Al assistance processing strategy of the plurality of Al assistance processing strategies is further based on the Al assistance processing strategy having a highest weighted sum of weights of the plurality of Al assistance processing strategies.

[0213] Aspect 23. The method of any of Aspects 17 to 22, wherein the Al assistance processing strategy of the plurality of Al assistance processing strategies is determined using a small language model (SLM).

[0214] Aspect 24. The method of any of Aspects 17 to 23, wherein the one or more device properties comprise at least one of a battery capacity of the device, a network connection signal to noise ratio, or a six degrees of freedom (6 DoF) pose for each of the one or more sensors of the device.

[0215] Aspect 25. The method of any of Aspects 17 to 24. further comprising: determining, based on the one or more contexts, one or more events; monitoring, based on data from the one or more sensors, the scene for the one or more events; and detecting, based on monitoring the scene for the one or more events, at least one event of the one or more events.

[0216] Aspect 26. The method of any of Aspects 17 to 25, further comprising storing the one or more contexts within a log.PATENTQualcomm Ref. No. 2501466WO63

[0217] Aspect 27. The method of Aspect 26, further comprising storing at least a portion of the sensor data that is associated with the one or more contexts within the log.

[0218] Aspect 28. The method of any of Aspects 17 to 27, wherein the device is an extended reality (XR) device.

[0219] Aspect 29. The method of Aspect 28, wherein the XR device is a headmounted device.

[0220] Aspect 30. The apparatus of any of Aspects 17 to 29, wherein the one or more sensors includes one or more image sensors, and wherein the sensor data associated with the scene includes a plurality of images of the scene.

[0221] Aspect 31. The method of Aspect 30, wherein each image of the plurality of images is obtained at a respective time.

[0222] Aspect 32. The method of any of Aspects 30 or 31, wherein the one or more images sensors includes one or more always-on image sensors.

[0223] Aspect 33. A non-transitory computer-readable medium having stored thereon instructions that, when executed by at least one processor, cause the at least one processor to perform operations according to any of Aspects 17 to 32.

[0224] Aspect 34. An apparatus for Al assistance, the apparatus including one or more means for performing operations according to any of Aspects 17 to 32.

[0225] The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean “one and only one’’ unless specifically so stated, but rather “one or more.”

Claims

PATENTQualcomm Ref. No. 2501466WO64CLAIMSWhat is claimed is:

1. An apparatus for artificial intelligence (Al) assistance, the apparatus comprising:at least one memory; andat least one processor coupled to the at least one memory and configured to: obtain, from one or more sensors of a device associated with a user, sensor data associated with a scene;determine, based on the sensor data, one or more contexts for the scene; receive a query based on user input from the user;generate a prompt based on the query and the one or more contexts; determine an Al assistance processing strategy7from a plurality of Al assistance processing strategies based on the one or more contexts, the prompt, one or more device properties, or a combination thereof; andprocess, based on the Al assistance strategy, the query to generate an answer to the query'.

2. The apparatus of claim 1, wherein each Al assistance processing strategy of the plurality of Al assistance processing strategies is associated with one or more locations for the Al assistance processing and one or more different machine learning models.

3. The apparatus of claim 2, wherein the one or more locations comprise at least one of a location on the device or a location remote from the device.

4. The apparatus of claim 2, wherein the one or more different machine learning models comprise at least one of a first machine learning model or a second machine learning model, wherein the second machine learning model has fewer parameters than the first machine learning model.

5. The apparatus of claim 1, wherein at least one of each context of the one or more contexts or each device property' of the one or more device properties has a respectivePATENTQualcomm Ref. No. 2501466WO65weight associated with each Al assistance processing strategy of the plurality of Al assistance processing strategies.

6. The apparatus of claim 5, wherein the at least one processor is configured to determine the Al assistance processing strategy of the plurality of Al assistance processing strategies further based on the Al assistance processing strategy having a highest weighted sum of weights of the plurality of Al assistance processing strategies.

7. The apparatus of claim 1, wherein the at least one processor is configured to determine the Al assistance processing strategy of the plurality of Al assistance processing strategies using a small language model (SLM).

8. The apparatus of claim 1, wherein the one or more device properties comprise at least one of a battery capacity of the device, a network connection signal to noise ratio, or a six degrees of freedom (6 DoF) pose for each of the one or more sensors of the device.

9. The apparatus of claim 1, wherein the at least one processor is configured to: determine, based on the one or more contexts, one or more events; monitor, based on data from the one or more sensors, the scene for the one or more events; anddetect, based on monitoring the scene for the one or more events, at least one event of the one or more events.

10. The apparatus of claim 1. wherein the at least one processor is configured to store the one or more contexts wi thin a log.

11. The apparatus of claim 10, wherein the at least one processor is configured to store at least a portion of the sensor data that is associated with the one or more contexts within the log.

12. The apparatus of claim 1, wherein the device is an extended reality (XR) device.

13. The apparatus of claim 12, wherein the XR device is a head-mounted device.PATENTQualcomm Ref. No. 2501466WO6614. The apparatus of claim 1, wherein the one or more sensors includes one or more image sensors, and wherein the sensor data associated with the scene includes a plurality of images of the scene.

15. The apparatus of claim 14, wherein each image of the plurality of images is obtained at a respective time.

16. The apparatus of claim 14, wherein the one or more images sensors includes one or more always-on image sensors.

17. A method for artificial intelligence (Al) assistance, the method comprising: obtaining, from one or more sensors of a device associated wi th a user, sensor data associated with a scene;determining, based on the sensor data, one or more contexts for the scene; receiving a query based on user input from the user;generating a prompt based on the query and the one or more contexts; determining an Al assistance processing strategy' from a plurality' of Al assistance processing strategies based on the one or more contexts, the prompt, one or more device properties, or a combination thereof; andprocessing, based on the Al assistance strategy, the uery to generate an answer to the query.

18. The method of claim 17, yvherein each Al assistance processing strategy of the plurality of Al assistance processing strategies is associated with one or more locations for the Al assistance processing and one or more different machine learning models.

19. The method of claim 18, wherein the one or more locations comprise at least one of a location on the device or a location remote from the device.

20. The method of claim 18, yvherein the one or more different machine learning models comprise at least one of a first machine learning model or a second machinePATENTQualcomm Ref. No. 2501466WO67learning model, wherein the second machine learning model has fewer parameters than the first machine learning model.

Images