Systems, computer programs, devices, and methods that enable coordination across multiple devices of the mobile ecosystem. In one embodiment, smart glasses detect when a user is about to eat food or take a drink and capture the consumable and portion. The data is recorded in a “morsel track” for health activity analysis. Low-fidelity captures provide preliminary recognition, while higher-fidelity captures are selectively invoked for definitive classification. Machine-learning logic generates predicted metabolic responses, such as real-time glucose trends, based on the recorded events. Predicted responses may dynamically adjust the operation of continuous glucose monitors, heart-rate sensors, or other biomedical devices. In some embodiments, the system triggers a pharmaceutical dispenser, such as an insulin pump, inhaler, or transdermal patch, to provide closed-loop therapeutic intervention in real time.
A61M 5/172 - Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters electrical or electronic
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
Systems, computer programs, devices, and methods that enable coordination across multiple devices of the mobile ecosystem. In one embodiment, smart glasses detect when a user is about to eat food or take a drink and capture the consumable and portion. The data is recorded in a "morsel track" for health activity analysis. Low-fidelity captures provide preliminary recognition, while higher-fidelity captures are selectively invoked for definitive classification. Machine-learning logic generates predicted metabolic responses, such as real-time glucose trends, based on the recorded events. Predicted responses may dynamically adjust the operation of continuous glucose monitors, heart-rate sensors, or other biomedical devices. In some embodiments, the system triggers a pharmaceutical dispenser, such as an insulin pump, inhaler, or transdermal patch, to provide closed-loop therapeutic intervention in real time.
Systems, apparatus, and methods for a gesture-based augmented reality and/or extended reality (AR/XR) user interface. Conventional image processing scales quadratically based on image resolution. Processing complexity directly corresponds to memory size, power consumption, and heat dissipation. As a result, existing smart glasses solutions have short run-times (<1 hr) and may have battery weight and heat dissipation issues that are uncomfortable for continuous wear. The disclosed solution provides a system and method for low-power image processing via the use of scalable processing. In one specific implementation, gesture detection is divided into multiple stages. Each stage conditionally enables subsequent stages for more complex processing. By scaling processing complexity at each stage, high complexity processing can be performed on an “as-needed” basis.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/28 - Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 40/18 - Eye characteristics, e.g. of the iris
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
H04N 23/65 - Control of camera operation in relation to power supply
4.
FOUNDATION MODEL PIPELINE FOR REAL-TIME EMBEDDED DEVICES
Systems, computer programs, devices, and methods that enable LLM-based user interfaces within real-time and/or embedded devices. Providing user-specific context to a generically trained LLM may enable a variety of new usages and scenarios. For example, adaptive prompt augmentation may enable a user device to augment user-generated prompts with additional user context in the form of machine-generated prompts. In some variants, machine-generated prompts may be further refined to accommodate e.g., foundation model constraints, etc. APIs for user-specific data structures can be used to e.g., optimize for habitual behaviors, user idiosyncrasies, etc. Agentic query construction may enable a user device to operate with autonomy and decision-making capabilities, beyond prompt-response interactions. Stitching (or dreaming) may be used to identify pattern-based associations within high dimensional space (embedding vectors).
Systems, computer programs, devices, and methods that enable LLM-based user interfaces within real-time and/or embedded devices. Providing user-specific context to a generically trained LLM may enable a variety of new usages and scenarios. For example, adaptive prompt augmentation may enable a user device to augment user-generated prompts with additional user context in the form of machine-generated prompts. In some variants, machine-generated prompts may be further refined to accommodate e.g., foundation model constraints, etc. APIs for user-specific data structures can be used to e.g., optimize for habitual behaviors, user idiosyncrasies, etc. Agentic query construction may enable a user device to operate with autonomy and decision-making capabilities, beyond prompt-response interactions. Stitching (or dreaming) may be used to identify pattern-based associations within high dimensional space (embedding vectors).
Systems, computer programs, devices, and methods that enable LLM-based user interfaces within real-time and/or embedded devices. Providing user-specific context to a generically trained LLM may enable a variety of new usages and scenarios. For example, adaptive prompt augmentation may enable a user device to augment user-generated prompts with additional user context in the form of machine-generated prompts. In some variants, machine-generated prompts may be further refined to accommodate e.g., foundation model constraints, etc. APIs for user-specific data structures can be used to e.g., optimize for habitual behaviors, user idiosyncrasies, etc. Agentic query construction may enable a user device to operate with autonomy and decision-making capabilities, beyond prompt-response interactions. Stitching (or dreaming) may be used to identify pattern-based associations within high dimensional space (embedding vectors).
Systems, computer programs, devices, and methods that enable LLM-based user interfaces within real-time and/or embedded devices. Providing user-specific context to a generically trained LLM may enable a variety of new usages and scenarios. For example, adaptive prompt augmentation may enable a user device to augment user-generated prompts with additional user context in the form of machine-generated prompts. In some variants, machine-generated prompts may be further refined to accommodate e.g., foundation model constraints, etc. APIs for user-specific data structures can be used to e.g., optimize for habitual behaviors, user idiosyncrasies, etc. Agentic query construction may enable a user device to operate with autonomy and decision-making capabilities, beyond prompt-response interactions. Stitching (or dreaming) may be used to identify pattern-based associations within high dimensional space (embedding vectors).
Systems, computer programs, devices, and methods that enable ML-based vision processing for low-power, embedded, and/or real-time applications. In one exemplary embodiment, smart glasses use classifiers that are based on machine-learned (ML) patch relationships. The ML patch features are determined during an offline training process. The ML patch features are grouped into weak classifiers, strong classifiers, and detectors to progressively improve prediction accuracy. An object detection architecture uses triggering logic, search management, and a classification neural network to enable event-based searching, interest-based searching, and/or dynamic search control. In some cases, pre-processing may also be used to minimize the neural network complexity (e.g., pre-processing for scaling, rotations, translations, etc.).
Systems, computer programs, devices, and methods that enable ML-based vision processing for low-power, embedded, and/or real-time applications. In one exemplary embodiment, smart glasses use classifiers that are based on machine-learned (ML) patch relationships. The ML patch features are determined during an offline training process. The ML patch features are grouped into weak classifiers, strong classifiers, and detectors to progressively improve prediction accuracy. An object detection architecture uses triggering logic, search management, and a classification neural network to enable event-based searching, interest-based searching, and/or dynamic search control. In some cases, pre-processing may also be used to minimize the neural network complexity (e.g., pre-processing for scaling, rotations, translations, etc.).
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
G06T 3/60 - Rotation of whole images or parts thereof
10.
MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS
Systems, computer programs, devices, and methods that enable ML-based vision processing for low-power, embedded, and/or real-time applications. In one exemplary embodiment, smart glasses use classifiers that are based on machine-learned (ML) patch relationships. The ML patch features are determined during an offline training process. The ML patch features are grouped into weak classifiers, strong classifiers, and detectors to progressively improve prediction accuracy. An object detection architecture uses triggering logic, search management, and a classification neural network to enable event-based searching, interest-based searching, and/or dynamic search control. In some cases, pre-processing may also be used to minimize the neural network complexity (e.g., pre-processing for scaling, rotations, translations, etc.).
G06T 7/70 - Determining position or orientation of objects or cameras
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
11.
MACHINE-LEARNING ALGORITHMS FOR LOW-POWER APPLICATIONS
Systems, computer programs, devices, and methods that enable ML-based vision processing for low-power, embedded, and/or real-time applications. In one exemplary embodiment, smart glasses use classifiers that are based on machine-learned (ML) patch relationships. The ML patch features are determined during an offline training process. The ML patch features are grouped into weak classifiers, strong classifiers, and detectors to progressively improve prediction accuracy. An object detection architecture uses triggering logic, search management, and a classification neural network to enable event-based searching, interest-based searching, and/or dynamic search control. In some cases, pre-processing may also be used to minimize the neural network complexity (e.g., pre-processing for scaling, rotations, translations, etc.).
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
12.
NETWORK INFRASTRUCTURE FOR USER-SPECIFIC GENERATIVE INTELLIGENCE
Network infrastructure for user-specific generative intelligence. Providing user-specific context to a generically trained LLM introduces a variety of complications (privacy, resource utilization, training costs, etc.). Various aspects of the present disclosure provide novel user-specific data structures, privacy and access control, layers of data, and session management, within a network infrastructure for generative intelligence. For example, user-specific embedding vectors may be used to provide user context to a generically trained foundation model. In some variants, edge devices capture multiple modalities of user context (images, audio; not just text). Privacy and access control mechanisms also allow a user to control information that is captured and sent to the foundation model. Session management further decouples a user's conversational state from the foundation model's session state. These concepts and others may be used to emulate e.g., a chatbot based virtual assistant that responds based on user context.
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
13.
NETWORK INFRASTRUCTURE FOR USER-SPECIFIC GENERATIVE INTELLIGENCE
Network infrastructure for user-specific generative intelligence. Providing user-specific context to a generically trained LLM introduces a variety of complications (privacy, resource utilization, training costs, etc.). Various aspects of the present disclosure provide novel user-specific data structures, privacy and access control, layers of data, and session management, within a network infrastructure for generative intelligence. For example, user-specific embedding vectors may be used to provide user context to a generically trained foundation model. In some variants, edge devices capture multiple modalities of user context (images, audio; not just text). Privacy and access control mechanisms also allow a user to control information that is captured and sent to the foundation model. Session management further decouples a user's conversational state from the foundation model's session state. These concepts and others may be used to emulate e.g., a chatbot based virtual assistant that responds based on user context.
Network infrastructure for user-specific generative intelligence. Providing user-specific context to a generically trained LLM introduces a variety of complications (privacy, resource utilization, training costs, etc.). Various aspects of the present disclosure provide novel user-specific data structures, privacy and access control, layers of data, and session management, within a network infrastructure for generative intelligence. For example, user-specific embedding vectors may be used to provide user context to a generically trained foundation model. In some variants, edge devices capture multiple modalities of user context (images, audio; not just text). Privacy and access control mechanisms also allow a user to control information that is captured and sent to the foundation model. Session management further decouples a user's conversational state from the foundation model's session state. These concepts and others may be used to emulate e.g., a chatbot based virtual assistant that responds based on user context.
G06F 16/587 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
15.
NETWORK INFRASTRUCTURE FOR USER-SPECIFIC GENERATIVE INTELLIGENCE
Network infrastructure for user-specific generative intelligence. Providing user-specific context to a generically trained LLM introduces a variety of complications (privacy, resource utilization, training costs, etc.). Various aspects of the present disclosure provide novel user-specific data structures, privacy and access control, layers of data, and session management, within a network infrastructure for generative intelligence. For example, user-specific embedding vectors may be used to provide user context to a generically trained foundation model. In some variants, edge devices capture multiple modalities of user context (images, audio; not just text). Privacy and access control mechanisms also allow a user to control information that is captured and sent to the foundation model. Session management further decouples a user's conversational state from the foundation model's session state. These concepts and others may be used to emulate e.g., a chatbot based virtual assistant that responds based on user context.
Network infrastructure for user-specific generative intelligence. Providing user-specific context to a generically trained LLM introduces a variety of complications (privacy, resource utilization, training costs, etc.). Various aspects of the present disclosure provide novel user-specific data structures, privacy and access control, layers of data, and session management, within a network infrastructure for generative intelligence. For example, user-specific embedding vectors may be used to provide user context to a generically trained foundation model. In some variants, edge devices capture multiple modalities of user context (images, audio; not just text). Privacy and access control mechanisms also allow a user to control information that is captured and sent to the foundation model. Session management further decouples a user's conversational state from the foundation model's session state. These concepts and others may be used to emulate e.g., a chatbot based virtual assistant that responds based on user context.
Novel applications for anamorphic lenses in smart glasses. Anamorphic lenses preserve straightness of motion and lines (i.e., “linearity”). As a practical matter, existing computer vision models can be used with anamorphic images without re-training or intermediate conversion steps (unlike fisheye lenses). The contents of the present disclosure provide substantial improvements for applications that have different FOV requirements along different axis. Solutions for ergonomic hand placement (relative to gaze), non-square photosites, binning and eye-tracking are discussed throughout.
H04N 25/46 - Extracting pixel data from image sensors by controlling scanning circuits, e.g. by modifying the number of pixels sampled or to be sampled by combining or binning pixels
Novel applications for anamorphic lenses in smart glasses. Anamorphic lenses preserve straightness of motion and lines (i.e., “linearity”). As a practical matter, existing computer vision models can be used with anamorphic images without re-training or intermediate conversion steps (unlike fisheye lenses). The contents of the present disclosure provide substantial improvements for applications that have different FOV requirements along different axis. Solutions for ergonomic hand placement (relative to gaze), non-square photosites, binning and eye-tracking are discussed throughout.
Novel applications for anamorphic lenses in smart glasses. Anamorphic lenses preserve straightness of motion and lines (i.e., “linearity”). As a practical matter, existing computer vision models can be used with anamorphic images without re-training or intermediate conversion steps (unlike fisheye lenses). The contents of the present disclosure provide substantial improvements for applications that have different FOV requirements along different axis. Solutions for ergonomic hand placement (relative to gaze), non-square photosites, binning and eye-tracking are discussed throughout.
Novel applications for anamorphic lenses in smart glasses. Anamorphic lenses preserve straightness of motion and lines (i.e., “linearity”). As a practical matter, existing computer vision models can be used with anamorphic images without re-training or intermediate conversion steps (unlike fisheye lenses). The contents of the present disclosure provide substantial improvements for applications that have different FOV requirements along different axis. Solutions for ergonomic hand placement (relative to gaze), non-square photosites, binning and eye-tracking are discussed throughout.
Systems, apparatus, and methods for augmenting vision with region-of-interest based processing. In one specific example, smart glasses may use an eye-tracking camera to monitor the user's gaze and determine the user's gaze point. When triggered, the camera assembly captures a high-resolution image. The high-resolution image may be cropped to a much smaller region-of-interest (ROI) image based on computer-vision analysis of the user's gaze point. For example, if the smart glasses detect a human face at the gaze point, then the ROI is cropped to the human face. In this manner, the smart glasses may leverage specific capabilities of the smart glasses to augment the user experience; for example, telephoto lenses provide long distance vision, or computer-assisted search may direct the user to interesting activity. Other aspects may include e.g., external database assisted operation and/or ongoing cataloging throughout the day.
Systems, apparatus, and methods for augmenting vision with region-of-interest based processing. In one specific example, smart glasses may use an eye-tracking camera to monitor the user's gaze and determine the user's gaze point. When triggered, the camera assembly captures a high-resolution image. The high-resolution image may be cropped to a much smaller region-of-interest (ROI) image based on computer-vision analysis of the user's gaze point. For example, if the smart glasses detect a human face at the gaze point, then the ROI is cropped to the human face. In this manner, the smart glasses may leverage specific capabilities of the smart glasses to augment the user experience; for example, telephoto lenses provide long distance vision, or computer-assisted search may direct the user to interesting activity. Other aspects may include e.g., external database assisted operation and/or ongoing cataloging throughout the day.
Systems, apparatus, and methods for augmenting vision with region-of-interest based processing. In one specific example, smart glasses may use an eye-tracking camera to monitor the user's gaze and determine the user's gaze point. When triggered, the camera assembly captures a high-resolution image. The high-resolution image may be cropped to a much smaller region-of-interest (ROI) image based on computer-vision analysis of the user's gaze point. For example, if the smart glasses detect a human face at the gaze point, then the ROI is cropped to the human face. In this manner, the smart glasses may leverage specific capabilities of the smart glasses to augment the user experience; for example, telephoto lenses provide long distance vision, or computer-assisted search may direct the user to interesting activity. Other aspects may include e.g., external database assisted operation and/or ongoing cataloging throughout the day.
G10L 13/08 - Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
24.
APPARATUS AND METHODS FOR AUGMENTING VISION WITH REGION-OF-INTEREST BASED PROCESSING
Systems, apparatus, and methods for augmenting vision with region-of-interest based processing. In one specific example, smart glasses may use an eye-tracking camera to monitor the user's gaze and determine the user's gaze point. When triggered, the camera assembly captures a high-resolution image. The high-resolution image may be cropped to a much smaller region-of-interest (ROI) image based on computer-vision analysis of the user's gaze point. For example, if the smart glasses detect a human face at the gaze point, then the ROI is cropped to the human face. In this manner, the smart glasses may leverage specific capabilities of the smart glasses to augment the user experience; for example, telephoto lenses provide long distance vision, or computer-assisted search may direct the user to interesting activity. Other aspects may include e.g., external database assisted operation and/or ongoing cataloging throughout the day.
Systems, apparatus, and methods for augmenting vision with region-of-interest based processing. In one specific example, smart glasses may use an eye-tracking camera to monitor the user's gaze and determine the user's gaze point. When triggered, the camera assembly captures a high-resolution image. The high-resolution image may be cropped to a much smaller region-of-interest (ROI) image based on computer-vision analysis of the user's gaze point. For example, if the smart glasses detect a human face at the gaze point, then the ROI is cropped to the human face. In this manner, the smart glasses may leverage specific capabilities of the smart glasses to augment the user experience; for example, telephoto lenses provide long distance vision, or computer-assisted search may direct the user to interesting activity. Other aspects may include e.g., external database assisted operation and/or ongoing cataloging throughout the day.
Systems, apparatus, and methods for a gesture-based augmented reality and/or extended reality (AR/XR) user interface. Conventional image processing scales quadratically based on image resolution. Processing complexity directly corresponds to memory size, power consumption, and heat dissipation. As a result, existing smart glasses solutions have short run-times (<1 hr) and may have battery weight and heat dissipation issues that are uncomfortable for continuous wear. The disclosed solution provides a system and method for low-power image processing via the use of scalable processing. In one specific implementation, gesture detection is divided into multiple stages. Each stage conditionally enables subsequent stages for more complex processing. By scaling processing complexity at each stage, high complexity processing can be performed on an “as-needed” basis.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/28 - Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 40/18 - Eye characteristics, e.g. of the iris
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
H04N 23/65 - Control of camera operation in relation to power supply
27.
Systems, apparatus, and methods for gesture-based augmented reality, extended reality
Systems, apparatus, and methods for a gesture-based augmented reality and/or extended reality (AR/XR) user interface. Conventional image processing scales quadratically based on image resolution. Processing complexity directly corresponds to memory size, power consumption, and heat dissipation. As a result, existing smart glasses solutions have short run-times (<1 hr) and may have battery weight and heat dissipation issues that are uncomfortable for continuous wear. The disclosed solution provides a system and method for low-power image processing via the use of scalable processing. In one specific implementation, gesture detection is divided into multiple stages. Each stage conditionally enables subsequent stages for more complex processing. By scaling processing complexity at each stage, high complexity processing can be performed on an “as-needed” basis.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/28 - Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 40/18 - Eye characteristics, e.g. of the iris
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
H04N 23/65 - Control of camera operation in relation to power supply
Methods and apparatus for scalable processing. Conventional image sensors read image data in a sequential row-by-row manner. However, image data may be more efficiently processed at different scales. For example, computer vision processing at a first scale may be used to determine whether subsequent processing with more resolution is helpful. Various embodiments of the present disclosure readout image data according to different scales; scaled readouts may be processed using scale specific computer vision algorithms to determine next steps. In addition to scaled readouts of image data, some variants may also provide commonly used data and/or implement pre-processing steps.
Methods and apparatus for scalable processing. Conventional image sensors read image data in a sequential row-by-row manner. However, image data may be more efficiently processed at different scales. For example, computer vision processing at a first scale may be used to determine whether subsequent processing with more resolution is helpful. Various embodiments of the present disclosure readout image data according to different scales; scaled readouts may be processed using scale specific computer vision algorithms to determine next steps. In addition to scaled readouts of image data, some variants may also provide commonly used data and/or implement pre-processing steps.
H04N 25/445 - Extracting pixel data from image sensors by controlling scanning circuits, e.g. by modifying the number of pixels sampled or to be sampled by partially reading an SSIS array by skipping some contiguous pixels within the read portion of the array
G06T 1/20 - Processor architecturesProcessor configuration, e.g. pipelining
Methods and apparatus for scalable processing. Conventional image sensors read image data in a sequential row-by-row manner. However, image data may be more efficiently processed at different scales. For example, computer vision processing at a first scale may be used to determine whether subsequent processing with more resolution is helpful. Various embodiments of the present disclosure readout image data according to different scales; scaled readouts may be processed using scale specific computer vision algorithms to determine next steps. In addition to scaled readouts of image data, some variants may also provide commonly used data and/or implement pre-processing steps.
Systems, apparatus, and methods for a gesture-based augmented reality and/or extended reality (AR/XR) user interface. Conventional image processing scales quadratically based on image resolution. Processing complexity directly corresponds to memory size, power consumption, and heat dissipation. As a result, existing smart glasses solutions have short run-times (<1 hr) and may have battery weight and heat dissipation issues that are uncomfortable for continuous wear. The disclosed solution provides a system and method for low-power image processing via the use of scalable processing. In one specific implementation, gesture detection is divided into multiple stages. Each stage conditionally enables subsequent stages for more complex processing. By scaling processing complexity at each stage, high complexity processing can be performed on an “as-needed” basis.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/28 - Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 40/18 - Eye characteristics, e.g. of the iris
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
H04N 23/65 - Control of camera operation in relation to power supply
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Smart glasses; smart rings; smart watches; smartwatches; virtual reality glasses; virtual reality goggles; virtual reality headsets; wearable activity trackers; wearable computers in the nature of smartglasses; wearable computers in the nature of smartwatches; augmented reality headsets; augmented reality goggles; augmented reality glasses; input devices for computers; input devices for smartphones; input devices for tablets; input devices for edge devices, namely, dongles; smartphones; semiconductor chip sets; semiconductor chips; system on a chip (soc); downloadable chatbot software using artificial intelligence for virtual assistance using contextual user data; downloadable computer programs using artificial intelligence for virtual assistance emulating session persistence in natural language models and large language models; downloadable computer software using artificial intelligence for use in virtual assistance using contextual user data; recorded computer application software for mobile devices, wearable devices, smart devices, augmented reality devices, and extended reality devices, namely, software for interacting with chatbot software; downloadable augmented reality software for interacting with artificial intelligence software; downloadable augmented reality software for use in mobile devices for integrating electronic data with real world environments for the purpose of integrating with artificial intelligence software Providing on-line non-downloadable software using artificial intelligence for interacting with chatbot software; providing on-line non-downloadable software using artificial intelligence for virtual assistance emulating session persistence in natural language models and large language models; Software as a service (SAAS) services featuring software using artificial intelligence for virtual assistance using contextual user data; Software as a service (SAAS) services featuring software integrating user data for use with artificial intelligence
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Smart glasses; smart rings; smart watches; smartwatches; virtual reality glasses; virtual reality goggles; virtual reality headsets; wearable activity trackers; wearable computers in the nature of smartglasses; wearable computers in the nature of smartwatches; augmented reality headsets; augmented reality goggles; augmented reality glasses; input devices for computers; input devices for smartphones; input devices for tablets; input devices for edge devices, namely, dongles; smartphones; semiconductor chip sets; semiconductor chips; system on a chip (soc); downloadable chatbot software using artificial intelligence for virtual assistance using contextual user data; downloadable computer programs using artificial intelligence for virtual assistance emulating session persistence in natural language models and large language models; downloadable computer software using artificial intelligence for use in virtual assistance using contextual user data; recorded computer application software for mobile devices, wearable devices, smart devices, augmented reality devices, and extended reality devices, namely, software for interacting with chatbot software; downloadable augmented reality software for interacting with artificial intelligence software; downloadable augmented reality software for use in mobile devices for integrating electronic data with real world environments for the purpose of integrating with artificial intelligence software Providing on-line non-downloadable software using artificial intelligence for interacting with chatbot software; providing on-line non-downloadable software using artificial intelligence for virtual assistance emulating session persistence in natural language models and large language models; Software as a service (SAAS) services featuring software using artificial intelligence for virtual assistance using contextual user data; Software as a service (SAAS) services featuring software integrating user data for use with artificial intelligence
34.
Systems, apparatus, and methods for gesture-based augmented reality, extended reality
Systems, apparatus, and methods for a gesture-based augmented reality and/or extended reality (AR/XR) user interface. Conventional image processing scales quadratically based on image resolution. Processing complexity directly corresponds to memory size, power consumption, and heat dissipation. As a result, existing smart glasses solutions have short run-times (<1 hr) and may have battery weight and heat dissipation issues that are uncomfortable for continuous wear. The disclosed solution provides a system and method for low-power image processing via the use of scalable processing. In one specific implementation, gesture detection is divided into multiple stages. Each stage conditionally enables subsequent stages for more complex processing. By scaling processing complexity at each stage, high complexity processing can be performed on an “as-needed” basis.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/28 - Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 40/18 - Eye characteristics, e.g. of the iris
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
H04N 23/65 - Control of camera operation in relation to power supply
35.
Systems, apparatus, and methods for gesture-based augmented reality, extended reality
Systems, apparatus, and methods for a gesture-based augmented reality and/or extended reality (AR/XR) user interface. Conventional image processing scales quadratically based on image resolution. Processing complexity directly corresponds to memory size, power consumption, and heat dissipation. As a result, existing smart glasses solutions have short run-times (<1 hr) and may have battery weight and heat dissipation issues that are uncomfortable for continuous wear. The disclosed solution provides a system and method for low-power image processing via the use of scalable processing. In one specific implementation, gesture detection is divided into multiple stages. Each stage conditionally enables subsequent stages for more complex processing. By scaling processing complexity at each stage, high complexity processing can be performed on an “as-needed” basis.
