A general-purpose cognitive enhancement platform, system, method, and storage medium

Abstract

This invention discloses a general cognitive enhancement platform, system, method, and storage medium. The method includes: maintaining a scene registry, which stores the definition of at least one cognitive enhancement scene. Each scene definition includes a scene identifier, functional modules, a processing engine, and a pipeline mode; responding to requests to activate a specific cognitive enhancement scene; loading the corresponding functional modules and processing engine according to the scene definition in the scene registry and configuring the pipeline mode; processing input data through the processing engine to generate enhanced content via the functional modules; and presenting the enhanced content to the user. This invention also provides independent vision correction methods and systems, and real-time translation methods and systems. This invention achieves a scalable architecture for cognitive enhancement applications through a scene registry, solving the technical problems of closed architecture, lack of scalability, and repetitive functional development in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a general-purpose, scalable cognitive enhancement platform, system, method, and storage medium. Background Technology

[0002] With the development of augmented reality (AR), artificial intelligence (AI), and wearable computing technologies, people's demand for using technology to enhance their perception and cognitive abilities is growing. Existing technologies typically provide solutions for single, specific application scenarios, such as AR glasses used only for translation or electronic assistive devices (eSight, IrisVision, etc.) designed solely to assist people with low vision.

[0003] These single-purpose solutions have several fundamental flaws: First, the architecture is closed and cannot be expanded. Each application is an independent, vertically integrated system with a fixed software and hardware architecture, making it difficult to easily expand to support new cognitive enhancement scenarios. If users need both translation and vision correction functions, they often need to purchase and wear two different devices, resulting in a fragmented experience and high costs.

[0004] Second, redundant development of functions leads to inefficiency. Different cognitive augmentation applications (such as translation, vision assistance, and object recognition) have significant overlap in their underlying technologies. For example, they all require acquiring images from cameras, performing AI model inference, and rendering results on a display screen. In existing technologies, these common functions are repeatedly developed in each isolated application, resulting in a huge waste of resources.

[0005] Third, there is a lack of unified interaction and experience. Users need to switch between different devices and applications, which incurs a high learning cost and prevents them from obtaining a consistent and seamless enhanced experience.

[0006] Fourth, the unique challenges in the field of vision correction. Globally, approximately 2.2 billion people suffer from visual impairment, and the traditional solution is to wear optical lenses (glasses or contact lenses). However, human vision is constantly changing, leading users to either frequently replace their glasses (expensive and requiring adaptation to new lenses) or endure the problem of incorrect prescriptions. More importantly, vision changes are continuous and gradual; even when glasses become ill-fitting, users may not notice for a long time, leading to further deterioration of vision. While existing electronic vision aids can help people with low vision, they are closed, single-purpose devices that cannot be integrated with other cognitive enhancement functions and are expensive.

[0007] Therefore, there is an urgent need in this field for a new technological paradigm, namely a universal and scalable cognitive enhancement platform system.

[0008] This invention was proposed to fill this technological gap. Summary of the Invention

[0009] The main objective of this invention is to provide a universal cognitive enhancement platform, system, method, and storage medium based on a scenario registry, aiming to solve the technical problems of closed, unscalable, and repetitive development of functions in existing cognitive enhancement application architectures.

[0010] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A general-purpose cognitive enhancement platform and system, comprising: At least one input device, including at least one of a camera, a microphone, and / or a system interface, for acquiring data from the external world; A display screen is used to present enhanced content to the user; A processor; A memory that stores the scene registry and computer-executable instructions; The scene registry stores the definition of at least one cognitive augmentation scene. Each scene definition includes a scene identifier, a functional module, a processing engine, and a pipeline pattern. The processing engine includes an AI model and / or a rule-based processing module. A preferred implementation of the functional module is a software agent based on a multi-agent architecture. When the instructions are executed by the processor, the system is configured to: respond to requests to activate a specific cognitive augmentation scene; load the corresponding functional modules and processing engine according to the scene definition in the scene registry and configure the pipeline mode; process the input data through the processing engine by calling the functional modules to generate augmented content; and present the augmented content to the user through the display screen.

[0011] Furthermore, the system also includes at least one of the following hardware modules: electrochromic lens, adaptive optics lens, wavefront aberration sensor, eye-tracking camera, ambient light sensor, inertial measurement unit, and bone conduction module; and the system also includes a hardware abstraction layer that manages the hardware modules through a standardized interface.

[0012] Furthermore, the system also includes a multi-scenario resource scheduler, which dynamically allocates processor computing resources to multiple concurrently running cognitive augmentation scenarios based on the priorities and resource configuration files defined in the scenario registry.

[0013] Furthermore, the system also includes an event bus, through which functional modules communicate asynchronously. The event bus is used to transmit scene lifecycle events and user interaction events.

[0014] Furthermore, the processor includes a neural network processing unit or a graphics processing unit for performing AI model inference, wherein the AI ​​model is optimized for edge deployment through model quantization and / or knowledge distillation techniques.

[0015] Furthermore, the system also includes a security module configured to trigger a life safety-level failure protection mechanism when a serious fault affecting user safety is detected, forcing the system to enter a pure optical perspective mode and disabling all digital rendering.

[0016] Furthermore, the system is also configured to execute a speculative cascading workflow, including: rapidly generating and presenting a draft version of enhanced content on the device using a lightweight AI model; simultaneously sending input data to the cloud to generate a refined version of enhanced content using a high-precision AI model; and seamlessly replacing the draft version with the refined version when the semantic consistency between the refined version and the draft version is lower than a preset threshold.

[0017] Furthermore, the system also includes a semantic caching module, which stores historical processing results in a vector database. When the similarity between a newly input semantic vector and an entry in the cache exceeds a preset threshold, the cached processing result is returned directly to reduce latency.

[0018] Furthermore, the system also includes an illusion suppression module, which is used to reverse process the enhanced content generated by the AI ​​model back to the original domain, and calculate the similarity between the reverse processing result and the original input. When the similarity is lower than a preset threshold, an alarm or downgrade processing is triggered.

[0019] Furthermore, the system also includes a five-level fallback chain module and a circuit breaker module. The five-level fallback chain module is configured to automatically downgrade the cognitive enhancement function from the current service level to a preset lower service level when a performance degradation or error is detected. The circuit breaker module has three states: closed, open, and half-open, to prevent frequent switching between unstable service levels.

[0020] Furthermore, the system also includes a model registry and a model abstraction layer. The model registry is used to store metadata of multiple available AI models, including model identifiers, model capability types, model locations, and model states. The model abstraction layer is used to dynamically load and replace AI models through a unified capability interface. Dynamic replacement is achieved through the following steps: decision to replace, initialization of the new model, routing of new requests to the new model, waiting for the old model to complete the accepted request, and unloading the old model.

[0021] Furthermore, the system also includes a cross-platform adaptation module, wherein: on the Android platform, enhanced content is presented through accessibility services and an accessibility overlay; on the iOS platform, enhanced content is presented using differentiated degradation strategies based on different application scenarios.

[0022] Furthermore, when multiple cognitive enhancement scenarios run concurrently, the system is configured to: use the enhanced content generated by the scenario configured in global transformation mode as the base frame, and overlay the enhanced content generated by the scenario configured in selective transformation mode onto the base frame for composite display.

[0023] Furthermore, the system is integrated into a pair of smart glasses, which includes a video perspective display system or an optical perspective display system.

[0024] A general cognitive enhancement method includes the following steps: A scene registry is provided for storing the definition of at least one cognitive augmentation scene, wherein each scene definition includes a scene identifier, at least one functional module required by the scene, at least one processing engine required by the scene, and a pipeline pattern for configuring the cognitive augmentation function; wherein the processing engine includes an AI model and / or a rule-based processing module. Receive a request to activate a specific cognitive enhancement scenario; Based on the definition corresponding to a specific cognitive enhancement scenario in the scenario registry, load at least one functional module and at least one processing engine required by the scenario, and configure the pipeline mode; By loading at least one functional module, at least one processing engine is invoked to process the input data to generate enhanced content; Enhanced content will be presented to users based on the pipeline model.

[0025] Furthermore, the functional modules are software agents based on a multi-agent architecture, with each software agent responsible for an independent cognitive enhancement functional domain and communicating asynchronously through an event bus.

[0026] Furthermore, the pipeline modes include a selective transformation mode and a global transformation mode; wherein, in the selective transformation mode, only a portion of the input data is processed to generate the enhanced content, and selective occlusion and lossless pass-through between the enhanced content and the unprocessed original content is achieved through GPU Stencil Buffer technology or other rendering occlusion techniques; in the global transformation mode, all of the input data is processed to generate the enhanced content.

[0027] Furthermore, when multiple cognitive enhancement scenes run concurrently, the presentation steps include: using the enhanced content generated by the scene configured in global transformation mode as the base frame, and overlaying the enhanced content generated by the scene configured in selective transformation mode onto the base frame for composite display.

[0028] Furthermore, the scenario definition also includes resource configuration files and default priorities; the method also includes: providing a multi-scenario resource scheduler, which dynamically allocates computing resources to multiple concurrently running cognitive augmentation scenarios according to the priorities and resource configuration files defined in the scenario registry; and instantly increasing the priority of a scenario when an interaction event between a user and a scenario is detected.

[0029] Furthermore, the method also includes: providing a hardware abstraction layer, which manages and controls at least one hardware module in a pluggable manner through a standardized hardware module interface; and wherein the scenario definition also includes hardware module identifiers required by the scenario.

[0030] Furthermore, the hardware module includes at least one of the following: an electrochromic lens, an adaptive optics lens, a wavefront aberration sensor, a zoom display, a light field display, or a holographic display.

[0031] Furthermore, the software agent communicates asynchronously through an event bus, which is used to transmit events related to the scene lifecycle, including scene activation events and scene deactivation events.

[0032] Furthermore, the method also includes: providing a model registry for storing metadata of multiple available AI models, the metadata including model identifier, model capability type, model location and model status; and providing a model abstraction layer for dynamically loading and replacing AI models through a unified capability interface, wherein dynamic replacement is achieved through the following steps: decision replacement, initialization of a new model, routing of new requests to the new model, waiting for the old model to complete the accepted request, and unloading the old model.

[0033] Furthermore, the method also includes a speculative cascaded workflow, which includes the following steps: quickly generating a draft version of the enhanced content on the edge device using a lightweight model and immediately presenting it to the user; simultaneously sending input data to a cloud server, where a high-precision model generates a refined version of the enhanced content; upon receiving the refined version, calculating its semantic consistency score with the draft version; and when the semantic consistency score is lower than a preset threshold, seamlessly replacing the draft version with the refined version using a fade-in / fade-out animation.

[0034] Furthermore, the method also includes an intelligent routing and semantic caching mechanism, wherein: an AI model router dynamically selects the optimal AI model to process the input data based on the language, complexity, and current network status of the input content; a semantic caching module uses a vector database to store historical processing results, and when the semantic similarity between a new input and an entry in the cache exceeds a preset threshold, it directly returns the cached result without calling the AI ​​model.

[0035] Furthermore, the method also includes a five-level fallback chain and a circuit breaker for automatically downgrading the cognitive enhancement function from the current service level to a preset lower service level when a performance degradation or error is detected; wherein the circuit breaker has three states: closed, open, and half-open, to prevent frequent switching between unstable service levels.

[0036] Furthermore, the method also includes an illusion suppression mechanism, which verifies the reliability of the AI ​​model output through the following steps: reverse processing the enhanced content generated by the AI ​​model to generate a reverse result; calculating the semantic similarity score between the reverse result and the original input data; when the semantic similarity score is lower than a preset threshold, marking the enhanced content as suspicious and triggering a downgrade process.

[0037] Furthermore, the method also includes a cross-platform adaptation strategy, wherein: on the Android platform, enhanced content is presented through accessibility services and an accessibility overlay; on the iOS platform, enhanced content is presented using differentiated degradation strategies based on different application scenarios.

[0038] A vision correction system includes: at least one camera for capturing image frames representing the external world; a display screen for displaying the images to a user; a processor; and a memory storing personalized visual aberration data of the user and computer-executable instructions; when the instructions are executed by the processor, the system is configured to: dynamically acquire corresponding image transformation parameters based on the user's visual state parameters; apply an image transformation algorithm to the image frames based on the image transformation parameters and the user's personalized visual aberration data to generate pre-distorted image frames; and display the pre-distorted image frames on the display screen.

[0039] A method for real-time correction of a user's vision, implemented through a vision correction system, includes the following steps: Acquire personalized visual aberration data of users, which represents the optical defects of users' eyes; Capture image frames representing the external world from the camera; Based on the user's visual state parameters, the corresponding image transformation parameters are dynamically obtained. Based on the parameters and the user's personalized visual aberration data, the image transformation algorithm is applied to the image frame to generate a pre-distorted image frame. The pre-distortion aims to compensate for the optical defects of the user's eyes. Display the pre-distorted image frame on the screen in front of the user.

[0040] Furthermore, dynamically obtaining the corresponding image transformation parameters includes: dynamically selecting or interpolating the corresponding point spread function parameters from a multi-pupil point spread function lookup table containing multiple point spread functions.

[0041] Furthermore, the image transformation algorithm is either frequency domain deconvolution or spatial domain pre-distortion, where the frequency domain deconvolution operation uses a Wiener filter or constrained least squares method to suppress noise amplification.

[0042] Furthermore, the multi-pupil point spread function lookup table contains point spread functions corresponding to multiple discrete pupil sizes ranging from 2 mm to 8 mm, and when the real-time monitored user pupil size is between two discrete values, the corresponding point spread function parameters are obtained by interpolation calculation.

[0043] Furthermore, the method also includes a continuous vision assessment step for dynamically updating the user's personalized visual aberration data through active measurement and / or passive monitoring; wherein active measurement is achieved through an integrated wavefront aberration sensor, and passive monitoring is achieved by analyzing user eye movement data captured by an eye-tracking camera, the eye movement data including at least one of blink frequency, saccade pattern, and pupillary response.

[0044] Furthermore, the method also includes an active vision protection step, which adjusts the light intensity, color temperature, or spectral distribution displayed to the user through a software algorithm and / or a hardware module; wherein the protection step includes at least one of the following: strong light protection, blue light filtering, welding strong light protection, night driving anti-glare and ultraviolet protection.

[0045] Furthermore, the method also includes a visual fatigue monitoring step, which determines the degree of visual fatigue by monitoring the user's blinking frequency. When the blinking frequency is lower than a preset threshold, a visual rest reminder is automatically triggered.

[0046] Furthermore, the method also includes: controlling an adaptive optics lens through a hardware abstraction layer (HAL) to provide basic focal length adjustment; and an image transformation algorithm on this basis to perform fine correction for higher-order aberrations.

[0047] Furthermore, the method also includes a step for resolving convergence-accommodation conflict, which is achieved by controlling a zoom display, a light field display, or a holographic display to dynamically adjust the display focal plane according to the user's depth of gaze.

[0048] Furthermore, the method also includes a binocular parallax correction step, which compensates for the parallax of the image frames for the left and right eyes respectively based on the physical positional deviation between the camera and the user's pupil.

[0049] Furthermore, the method also includes a progressive adaptation mode that starts with a lower correction intensity when the user first uses it, and gradually increases the correction intensity to a full correction level over a preset adaptation period.

[0050] Furthermore, the personalized visual aberration data covers at least one of myopia, hyperopia, astigmatism, and higher-order aberrations, thereby enabling the method to correct a full range of refractive errors.

[0051] Furthermore, when the aforementioned vision correction method is deployed as a cognitive enhancement scenario in a cognitive enhancement platform with a scenario registry, the corresponding scenario definition in the scenario registry registers the method as a vision correction scenario and specifies the functional modules, image transformation algorithms, and global transformation pipeline modes required for vision correction.

[0052] A real-time translation system includes: at least one input device for acquiring content to be translated; a display screen for presenting the translated content to a user; a processor; and a memory storing an AI translation model and computer-executable instructions; when the instructions are executed by the processor, the system is configured to: acquire the content to be translated from the input device; perform context-aware translation using the AI ​​translation model to generate a translated text, wherein the context includes contextual information understood from a scene; and present the translated text to the user via the display screen in a manner spatially associated with the original content.

[0053] A method for real-time content translation, implemented through a real-time translation system, includes the following steps: Obtain the content to be translated from an input source; Context-aware translation is performed using an AI translation model to generate translated text, where the context includes contextual information understood from the scene; The translation is presented to the user in a way that is associated with the original content space.

[0054] Furthermore, presenting the translation to the user in a way that is related to the original content space includes rendering and overlaying the translation on top of the original content while preserving the original layout and font style through a layout replication module.

[0055] Furthermore, the typesetting replication module identifies text bounding boxes through a convolutional neural network, performs perspective transformation using inertial measurement unit data, and renders the translation after removing the original text using image restoration technology.

[0056] Furthermore, the input source is the UI element tree or document object model of the operating system, thereby achieving text extraction with zero optical character recognition; and the method also supports multiple presentation modes, including at least one of one-click perspective mode, split-screen comparison mode, overlay mode and new file mode.

[0057] Furthermore, the method also includes a real-time oral dialogue translation process, which sequentially executes speech activity detection, speech-to-text, translation, and text-to-speech through a streaming pipeline to achieve a real-time translation experience similar to simultaneous interpretation.

[0058] Furthermore, the AI ​​translation model is a large-scale language model, and context-aware translation includes at least one of the following capabilities: ambiguity resolution based on scene context, terminology consistency maintenance based on full-text context, and cultural adaptation based on target language cultural norms.

[0059] Furthermore, the method also includes a spatial anchoring step, which uses the three-dimensional coordinate system of the augmented reality framework to anchor the rendering position of the translation to a fixed spatial position in the physical world, so that the translation maintains a spatial correspondence with the original text when the user's head moves.

[0060] Furthermore, the real-time oral dialogue translation process also includes a speaker separation step, which can distinguish the voices of multiple speakers and translate them separately to support multi-person dialogue scenarios.

[0061] Furthermore, the overall processing latency of the method is controlled within a preset real-time threshold, wherein the text detection, translation, typesetting replication and rendering stages are executed in parallel through pipeline to minimize end-to-end latency.

[0062] Furthermore, when the aforementioned real-time translation method is deployed as a cognitive augmentation scenario in a cognitive augmentation platform with a scenario registry, the corresponding scenario definition in the scenario registry registers the method as a real-time translation scenario and specifies the functional modules, AI translation model, and selective transformation pipeline modes required for translation.

[0063] Furthermore, the method also includes a life safety-level failure protection mechanism. When the system detects any serious fault that may affect user safety, it immediately triggers the highest priority security overlay event, forcing the system to enter a pure optical perspective mode and disabling all digital rendering.

[0064] Furthermore, the method also includes a privacy protection mechanism, wherein: the user's personalized data is encrypted and stored on the edge device and not transmitted to the cloud; the upgrade of the AI ​​model is achieved through federated learning and differential privacy technology to protect user privacy.

[0065] A computer-readable storage medium comprising a computer program that, when executed on an electronic device, causes the electronic device to perform any of the methods described above.

[0066] Compared with the prior art, the beneficial effects of the present invention are as follows: First, high scalability. By introducing the core mechanism of a "scene registry," this invention decouples the definition of cognitive enhancement applications from the code, transforming it into configurable data. Any new cognitive enhancement scenario (such as dyslexia assistance, professional skills enhancement, privacy protection, etc.) can be quickly implemented by adding a new scenario definition to the registry without modifying the platform's core code, greatly improving the platform's scalability and flexibility.

[0067] Second, the architecture offers versatility and high reusability. This invention provides a unified, universal content transformation pipeline, a multi-agent collaboration framework, and a resource scheduler. These underlying modules are scenario-independent and can be reused across all cognitive augmentation scenarios, avoiding redundant development of functions and significantly reducing development costs and time.

[0068] Third, scene adaptability. By including "pipeline modes" (such as "selective transformation" or "global transformation") in the scene definition, the platform can adaptively adjust its data processing flow according to the characteristics of different scenes. For example, a translation scene only needs to process a local text area, while a vision correction scene needs to process the entire visual image. The dual-mode pipeline of this invention can efficiently meet these two very different needs.

[0069] Fourth, hardware-software synergy and future compatibility. By introducing a Hardware Abstraction Layer (HAL), the platform can integrate and manage various existing and future hardware modules (such as electrochromic lenses, adaptive optics lenses, etc.) in a plug-in manner, achieving deep synergy between software algorithms and hardware functions, and ensuring compatibility with future hardware development.

[0070] Fifth, a software-based vision correction paradigm. By replacing traditional physical lens prescriptions with AI image processing algorithms, this invention achieves a completely new vision correction paradigm—users can obtain continuous and accurate vision correction without changing lenses. The system can also monitor vision changes in real time and adjust them seamlessly, completely solving the pain point of "inappropriate prescription" in traditional glasses.

[0071] Sixth, independent subsystem deployability. In addition to providing a unified, general-purpose cognitive enhancement platform, this invention also provides separate vision correction and real-time translation systems. These two subsystems can be deployed and used as independent products without relying on a complete general-purpose platform architecture, reducing deployment complexity and cost in specific scenarios while retaining their respective technological advantages. Attached Figure Description

[0072] Figure 1 This is a schematic diagram of the general cognitive enhancement platform and system architecture provided in the embodiments of the present invention.

[0073] Figure 2 This is a flowchart of a general content transformation pipeline provided in the embodiments of the present invention, including selective transformation mode and global transformation mode.

[0074] Figure 3 This is a schematic diagram of the scenario registry data structure provided in an embodiment of the present invention.

[0075] Figure 4 This is a schematic diagram of the multi-Agent collaboration framework and event bus communication provided in an embodiment of the present invention.

[0076] Figure 5 This is a schematic diagram illustrating the working principle of the multi-scenario resource scheduler provided in this embodiment of the invention.

[0077] Figure 5 It also demonstrates the working principles of the data sharing mechanism and the enhanced regional conflict resolution strategy during concurrent multi-scenario operation.

[0078] Figure 6 This is a schematic diagram illustrating the interaction between the Hardware Abstraction Layer (HAL) and the hardware module provided in this embodiment of the invention.

[0079] Figure 7 This is a flowchart of the real-time vision correction method provided in the embodiments of the present invention.

[0080] Figure 8 This is a schematic diagram illustrating the working principle of the multi-pupil PSF lookup table provided in an embodiment of the present invention.

[0081] Figure 9 This is a schematic diagram of a continuous vision assessment method provided in an embodiment of the present invention, including an active assessment mode and a passive monitoring mode.

[0082] Figure 10 This is a schematic diagram illustrating the collaborative operation of software and adaptive optical lenses provided in an embodiment of the present invention.

[0083] Figure 11 This is a schematic diagram of the convergence-regulation conflict (VAC) solution provided in an embodiment of the present invention.

[0084] Figure 12 This is a flowchart of the typesetting replication process in the real-time translation method provided in this embodiment of the invention.

[0085] Figure 13 This is a schematic diagram of the five-level backoff chain and circuit breaker state transition model provided in the embodiment of the present invention.

[0086] Figure 14 This is a schematic diagram of the speculative cascading workflow provided in an embodiment of the present invention.

[0087] Figure 15 This is a schematic diagram of the intelligent routing and semantic caching mechanism provided in an embodiment of the present invention.

[0088] Figure 16 This is a schematic diagram of the five-step hot-swap process for the model provided in the embodiment of the present invention.

[0089] Figure 17 This is a flowchart of the hallucination suppression mechanism provided in the embodiments of the present invention.

[0090] Figure 18 This is a schematic diagram of the cross-platform adaptation strategy provided in an embodiment of the present invention.

[0091] Figure 19 This is a schematic diagram of the active vision protection system operating in multiple scenarios provided in an embodiment of the present invention.

[0092] Figure 20 This is a schematic diagram of the binocular parallax correction principle provided in an embodiment of the present invention. Detailed Implementation

[0093] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0094] The computing devices described in the embodiments of the present invention include, but are not limited to, smart glasses, augmented reality (AR) headsets, smartphones, tablets, and other electronic devices or edge devices with data processing capabilities.

[0095] This invention can flexibly host and schedule a variety of different cognitive enhancement applications through a unified architecture, just like an application store on a smartphone operating system, providing users with a one-stop, on-demand customized cognitive enhancement service.

[0096] A general-purpose cognitive enhancement platform and system, comprising: At least one input device, including at least one of a camera, a microphone, and / or a system interface, for acquiring data from the external world; A display screen is used to present enhanced content to the user; A processor; A memory that stores the scene registry and computer-executable instructions; The scene registry stores the definition of at least one cognitive augmentation scene. Each scene definition includes a scene identifier, a functional module, a processing engine, and a pipeline pattern. The processing engine includes an AI model and / or a rule-based processing module. A preferred implementation of the functional module is a software agent based on a multi-agent architecture. When the instructions are executed by the processor, the system is configured to: respond to requests to activate a specific cognitive augmentation scene; load the corresponding functional modules and processing engine according to the scene definition in the scene registry and configure the pipeline mode; process the input data through the processing engine by calling the functional modules to generate augmented content; and present the augmented content to the user through the display screen.

[0097] Furthermore, the system also includes at least one of the following hardware modules: electrochromic lens, adaptive optics lens, wavefront aberration sensor, eye-tracking camera, ambient light sensor, inertial measurement unit (IMU), and bone conduction module; and the system also includes a hardware abstraction layer (HAL) to manage the hardware modules through a standardized interface.

[0098] Furthermore, the system also includes a multi-scenario resource scheduler, which dynamically allocates processor computing resources to multiple concurrently running cognitive augmentation scenarios based on the priorities and resource configuration files defined in the scenario registry.

[0099] Furthermore, the system also includes an event bus, through which functional modules communicate asynchronously. The event bus is used to transmit scene lifecycle events and user interaction events.

[0100] Furthermore, the processor includes a neural network processing unit (NPU) or a graphics processing unit (GPU) for performing AI model inference, and the AI ​​model is optimized for edge deployment through model quantization and / or knowledge distillation techniques.

[0101] Furthermore, the system also includes a security module configured to trigger a life safety-level failure protection mechanism when a serious fault affecting user safety is detected, forcing the system to enter a pure optical perspective mode and disabling all digital rendering.

[0102] Furthermore, the system is also configured to execute a speculative cascading workflow, including: rapidly generating and presenting a draft version of enhanced content on the device using a lightweight AI model; simultaneously sending input data to the cloud to generate a refined version of enhanced content using a high-precision AI model; and seamlessly replacing the draft version with the refined version when the semantic consistency between the refined version and the draft version is lower than a preset threshold.

[0103] Furthermore, the system also includes a semantic caching module, which stores historical processing results in a vector database. When the similarity between a newly input semantic vector and an entry in the cache exceeds a preset threshold, the cached processing result is returned directly to reduce latency.

[0104] Furthermore, the system also includes an illusion suppression module, which is used to reverse process the enhanced content generated by the AI ​​model back to the original domain, and calculate the similarity between the reverse processing result and the original input. When the similarity is lower than a preset threshold, an alarm or downgrade processing is triggered.

[0105] Furthermore, the system also includes a five-level fallback chain module and a circuit breaker module. The five-level fallback chain module is configured to automatically downgrade the cognitive enhancement function from the current service level to a preset lower service level when a performance degradation or error is detected. The circuit breaker module has three states: closed, open, and half-open, to prevent frequent switching between unstable service levels.

[0106] Furthermore, the system also includes a model registry and a model abstraction layer. The model registry is used to store metadata of multiple available AI models, including model identifiers, model capability types, model locations, and model states. The model abstraction layer is used to dynamically load and replace AI models through a unified capability interface. Dynamic replacement is achieved through the following steps: decision to replace, initialization of the new model, routing of new requests to the new model, waiting for the old model to complete the accepted request, and unloading the old model.

[0107] Furthermore, the system also includes a cross-platform adaptation module, wherein: on the Android platform, enhanced content is presented through AccessibilityService and an accessibility overlay; on the iOS platform, enhanced content is presented using differentiated degradation strategies based on different application scenarios.

[0108] Furthermore, when multiple cognitive enhancement scenarios run concurrently, the system is configured to: use the enhanced content generated by the scenario configured in global transformation mode as the base frame, and overlay the enhanced content generated by the scenario configured in selective transformation mode onto the base frame for composite display.

[0109] Furthermore, the present invention provides a scenario development toolkit (SDK), which includes scenario definition templates, model interface specifications, proxy development frameworks, and hardware module access guidelines; and provides standardized application programming interfaces (APIs) to enable third-party developers to connect their custom cognitive augmentation scenarios and processing engines to the platform in a standardized manner, forming an open cognitive augmentation platform system.

[0110] Furthermore, the system is integrated into a pair of smart glasses, which includes a video see-through (VST) display system or an optical see-through (OST) display system.

[0111] Furthermore, the system also includes an AI model router, which dynamically selects the optimal AI model to process the input data based on the language, complexity, and current network status of the input content.

[0112] Furthermore, the system also includes a privacy protection module, which is configured to: encrypt and store users' personalized data on the edge device without transmitting it to the cloud; and upgrade the AI ​​model through federated learning and differential privacy technology to protect user privacy.

[0113] Furthermore, the system also includes a scenario development kit (SDK) and a standardized application programming interface (API). The SDK includes scenario definition templates, model interface specifications, agent development frameworks, and hardware module access guidelines; the API enables third-party developers to integrate their custom cognitive augmentation scenarios and processing engines into the system in a standardized manner, forming an open cognitive augmentation ecosystem.

[0114] This invention also provides a general cognitive enhancement method, comprising the following steps: A scene registry is provided for storing the definition of at least one cognitive augmentation scene, wherein each scene definition includes a scene identifier, at least one functional module required by the scene, at least one processing engine required by the scene, and a pipeline pattern for configuring the cognitive augmentation function; wherein the processing engine includes an AI model and / or a rule-based processing module. Receive a request to activate a specific cognitive enhancement scenario; Based on the definition corresponding to a specific cognitive enhancement scenario in the scenario registry, load at least one functional module and at least one processing engine required by the scenario, and configure the pipeline mode; By loading at least one functional module, at least one processing engine is invoked to process the input data to generate enhanced content; Enhanced content will be presented to users based on the pipeline model.

[0115] Furthermore, the functional modules are software agents based on a multi-agent architecture. Each software agent is responsible for an independent cognitive enhancement functional domain and communicates asynchronously through an event bus.

[0116] Furthermore, the pipeline modes include a selective transformation mode and a global transformation mode; wherein, in the selective transformation mode, only a portion of the input data is processed to generate the enhanced content, and selective occlusion and lossless pass-through between the enhanced content and the unprocessed original content is achieved through GPU Stencil Buffer technology or other rendering occlusion techniques; in the global transformation mode, all of the input data is processed to generate the enhanced content.

[0117] Furthermore, when multiple cognitive enhancement scenes run concurrently, the presentation steps include: using the enhanced content generated by the scene configured in global transformation mode as the base frame, and overlaying the enhanced content generated by the scene configured in selective transformation mode onto the base frame for composite display.

[0118] Furthermore, the scenario definition also includes resource configuration files and default priorities; the method also includes: providing a multi-scenario resource scheduler, which dynamically allocates computing resources to multiple concurrently running cognitive augmentation scenarios according to the priorities and resource configuration files defined in the scenario registry; and instantly increasing the priority of a scenario when an interaction event between a user and a scenario is detected.

[0119] Furthermore, the method also includes: providing a hardware abstraction layer (HAL), which manages and controls at least one hardware module in a pluggable manner through a standardized hardware module interface (IHardwareModule); and wherein the scenario definition also includes hardware module identifiers required for the scenario.

[0120] Furthermore, the hardware module includes at least one of the following: an electrochromic lens, an adaptive optics lens, a wavefront aberration sensor, a zoom display, a light field display, or a holographic display.

[0121] Furthermore, the software agent communicates asynchronously through an event bus, which is used to transmit events related to the scene lifecycle, including scene activation events and scene deactivation events.

[0122] Furthermore, the method also includes: providing a model registry for storing metadata of multiple available AI models, the metadata including model identifier, model capability type, model location and model status; and providing a model abstraction layer for dynamically loading and replacing AI models through a unified capability interface, wherein dynamic replacement is achieved through the following steps: decision replacement, initialization of a new model, routing of new requests to the new model, waiting for the old model to complete the accepted request, and unloading the old model.

[0123] Furthermore, the method also includes a speculative cascaded workflow, which includes the following steps: quickly generating a draft version of the enhanced content on the edge device using a lightweight model and immediately presenting it to the user; simultaneously sending input data to a cloud server, where a high-precision model generates a refined version of the enhanced content; upon receiving the refined version, calculating its semantic consistency score with the draft version; and when the semantic consistency score is lower than a preset threshold, seamlessly replacing the draft version with the refined version using a fade-in / fade-out animation.

[0124] Furthermore, the method also includes an intelligent routing and semantic caching mechanism, wherein: an AI model router dynamically selects the optimal AI model to process the input data based on the language, complexity, and current network status of the input content; a semantic caching module uses a vector database to store historical processing results, and when the semantic similarity between a new input and an entry in the cache exceeds a preset threshold, it directly returns the cached result without calling the AI ​​model.

[0125] Furthermore, the method also includes a five-level fallback chain and a circuit breaker for automatically downgrading the cognitive enhancement function from the current service level to a preset lower service level when a performance degradation or error is detected; wherein the circuit breaker has three states: closed, open, and half-open, to prevent frequent switching between unstable service levels.

[0126] Furthermore, the method also includes an illusion suppression mechanism, which verifies the reliability of the AI ​​model output through the following steps: reverse processing the enhanced content generated by the AI ​​model to generate a reverse result; calculating the semantic similarity score between the reverse result and the original input data; when the semantic similarity score is lower than a preset threshold, marking the enhanced content as suspicious and triggering a downgrade process.

[0127] Furthermore, the method also includes a cross-platform adaptation strategy, wherein: on the Android platform, enhanced content is presented through AccessibilityService and TYPE_ACCESSIBILITY_OVERLAY; on the iOS platform, enhanced content is presented using differentiated degradation strategies based on different application scenarios.

[0128] Furthermore, presenting enhanced content to users according to pipeline modes includes: in selective presentation mode, merging enhanced content with unprocessed original content; and in global presentation mode, displaying all enhanced content to users.

[0129] Furthermore, in the selective transformation mode, the method also includes an enhancement area conflict resolution step, in which the display priority order of each area is determined according to scene priority and user preference when the enhancement areas of multiple concurrent scenarios have spatial overlap.

[0130] Furthermore, the method also includes a data sharing mechanism that allows input data and intermediate processing results to be shared between different cognitive enhancement scenarios to avoid redundant data collection and preprocessing.

[0131] Furthermore, the multi-scenario resource scheduler also supports elastic scaling of resource scheduling. When a cognitive augmentation scenario is temporarily inactive, its resource quota can be temporarily borrowed by other active scenarios.

[0132] Furthermore, in selective transformation mode, alpha blending technology is used at the boundary between enhanced content and unprocessed original content to achieve a smooth transition.

[0133] The present invention also provides a vision correction system, comprising: at least one camera for capturing image frames representing the external world; a display screen for displaying images to a user; a processor; and a memory storing personalized visual aberration data of the user and computer-executable instructions; when the instructions are executed by the processor, the system is configured to: dynamically acquire corresponding image transformation parameters based on the user's visual state parameters; apply an image transformation algorithm to the image frames based on the image transformation parameters and the user's personalized visual aberration data to generate pre-distorted image frames; and display the pre-distorted image frames on the display screen.

[0134] Furthermore, the memory also stores a multi-pupil point spread function (PSF) lookup table, which contains point spread functions corresponding to multiple discrete pupil sizes ranging from 2 mm to 8 mm; the system also includes an eye-tracking camera for real-time monitoring of the user's pupil size; when the pupil size is between two discrete values, the processor obtains the corresponding point spread function parameters through interpolation calculation.

[0135] Furthermore, the system also includes a continuous vision assessment module, which is configured to: actively measure via a wavefront aberration sensor, and / or passively monitor by analyzing user eye movement data captured by an eye-tracking camera, in order to dynamically update the user's personalized visual aberration data.

[0136] Furthermore, the system also includes an adaptive optics lens, which is controlled by a hardware abstraction layer (HAL) to provide basic focal length adjustment; the image transformation algorithm performs fine high-order aberration correction on top of the basic focal length adjustment provided by the adaptive optics lens.

[0137] The present invention also provides a method for real-time correction of a user's vision, implemented through a vision correction system, comprising the following steps: Acquire personalized visual aberration data of users, which represents the optical defects of users' eyes; Capture image frames representing the external world from the camera; Based on the user's visual state parameters, the corresponding image transformation parameters are dynamically obtained. Based on the parameters and the user's personalized visual aberration data, the image transformation algorithm is applied to the image frame to generate a pre-distorted image frame. The pre-distortion aims to compensate for the optical defects of the user's eyes. Display the pre-distorted image frame on the screen in front of the user.

[0138] Furthermore, dynamically obtaining the corresponding image transformation parameters includes: dynamically selecting or interpolating the corresponding point spread function parameters from a multi-pupil point spread function lookup table containing multiple point spread functions (PSFs).

[0139] Furthermore, the image transformation algorithm is either frequency domain deconvolution or spatial domain pre-distortion, where the frequency domain deconvolution operation uses a Wiener filter or constrained least squares method to suppress noise amplification.

[0140] Furthermore, the multi-pupil point spread function (PSF) lookup table contains point spread functions corresponding to multiple discrete pupil sizes ranging from 2 mm to 8 mm, and when the real-time monitored user pupil size is between two discrete values, the corresponding point spread function parameters are obtained by interpolation calculation.

[0141] Furthermore, the method also includes a continuous vision assessment step for dynamically updating the user's personalized visual aberration data through active measurement and / or passive monitoring; wherein active measurement is achieved through an integrated wavefront aberration sensor, and passive monitoring is achieved by analyzing user eye movement data captured by an eye-tracking camera, the eye movement data including at least one of blink frequency, saccade pattern, and pupillary response.

[0142] Furthermore, the method also includes an active vision protection step, which adjusts the light intensity, color temperature, or spectral distribution displayed to the user through a software algorithm and / or a hardware module; wherein the protection step includes at least one of the following: strong light protection, blue light filtering, welding strong light protection, night driving anti-glare and ultraviolet protection.

[0143] Furthermore, the method also includes a visual fatigue monitoring step, which determines the degree of visual fatigue by monitoring the user's blinking frequency. When the blinking frequency is lower than a preset threshold, a visual rest reminder is automatically triggered.

[0144] Furthermore, the method also includes: controlling an adaptive optics lens through a hardware abstraction layer (HAL) to provide basic focal length adjustment; and an image transformation algorithm on this basis to perform fine correction for higher-order aberrations.

[0145] Furthermore, the method also includes a step for resolving convergence-accommodation conflict (VAC), which is achieved by controlling a zoom display, a light field display, or a holographic display to dynamically adjust the display focal plane according to the user's depth of gaze.

[0146] Furthermore, the method also includes a binocular parallax correction step, which compensates for the parallax of the image frames for the left and right eyes respectively based on the physical positional deviation between the camera and the user's pupil.

[0147] Furthermore, the method also includes a progressive adaptation mode that starts with a lower correction intensity when the user first uses it, and gradually increases the correction intensity to a full correction level over a preset adaptation period.

[0148] Furthermore, the personalized visual aberration data covers at least one of myopia, hyperopia, astigmatism, and higher-order aberrations, thereby enabling the method to correct a full range of refractive errors.

[0149] Furthermore, when the aforementioned vision correction method is deployed as a cognitive enhancement scenario in a cognitive enhancement platform with a scenario registry, the corresponding scenario definition in the scenario registry registers the method as a vision correction scenario and specifies the functional modules, image transformation algorithms, and global transformation pipeline modes required for vision correction.

[0150] Furthermore, the method also includes: dynamically updating the user's personalized visual aberration data through a continuous vision assessment step, wherein the continuous vision assessment is achieved through active measurement and / or passive monitoring.

[0151] Furthermore, the user's visual state parameters include real-time monitoring of the user's pupil size.

[0152] The present invention also provides a real-time translation system, comprising: at least one input device for acquiring content to be translated; a display screen for presenting the translated content to a user; a processor; and a memory storing an AI translation model and computer-executable instructions; wherein, when the instructions are executed by the processor, the system is configured to: acquire the content to be translated from the input device; perform context-aware translation using the AI ​​translation model to generate a translated text, wherein the context includes contextual information understood from the scene; and present the translated text to the user via the display screen in a manner spatially associated with the original content.

[0153] Furthermore, the system also includes a layout replication module, which is configured to: identify text bounding boxes through a convolutional neural network (CNN), perform perspective transformation through inertial measurement unit (IMU) data, and render the translation after removing the original text through image inpainting technology, thereby presenting the translation in a way that preserves the original layout and font style.

[0154] Furthermore, the system also includes a real-time oral dialogue translation module, which is configured to sequentially perform speech activity detection (VAD), speech-to-text (STT), translation, and text-to-speech (TTS) via a streaming pipeline to achieve a real-time translation experience similar to simultaneous interpretation.

[0155] Furthermore, the input device includes the operating system's UI element tree interface or document object model (DOM) interface, thereby enabling zero optical character recognition (OCR) text extraction.

[0156] The present invention also provides a method for real-time translation of content, implemented through a real-time translation system, comprising the following steps: Obtain the content to be translated from an input source; Context-aware translation is performed using an AI translation model to generate translated text, where the context includes contextual information understood from the scene; The translation is presented to the user in a way that is associated with the original content space.

[0157] Furthermore, presenting the translation to the user in a way that is related to the original content space includes rendering and overlaying the translation on top of the original content while preserving the original layout and font style through a layout replication module.

[0158] Furthermore, the typesetting replication module identifies text bounding boxes using a convolutional neural network (CNN), performs perspective transformation using inertial measurement unit (IMU) data, and renders the translation after removing the original text using image inpainting technology.

[0159] Furthermore, the input source is the UI element tree or document object model (DOM) of the operating system, thereby enabling zero optical character recognition (OCR) text extraction; and the method also supports multiple presentation modes, including at least one of one-click perspective mode, split-screen comparison mode, overlay mode and new file mode.

[0160] Furthermore, the method also includes a real-time oral dialogue translation process, which sequentially executes speech activity detection (VAD), speech-to-text (STT), translation, and text-to-speech (TTS) through a streaming pipeline to achieve a real-time translation experience similar to simultaneous interpretation.

[0161] Furthermore, the AI ​​translation model is a large language model (LLM), and context-aware translation includes at least one of the following capabilities: ambiguity resolution based on scene context, terminology consistency maintenance based on full-text context, and cultural adaptation based on target language cultural norms.

[0162] Furthermore, the method also includes a spatial anchoring step, which uses the three-dimensional coordinate system of the augmented reality framework to anchor the rendering position of the translation to a fixed spatial position in the physical world, so that the translation maintains a spatial correspondence with the original text when the user's head moves.

[0163] Furthermore, the real-time oral dialogue translation process also includes a speaker separation step, which can distinguish the voices of multiple speakers and translate them separately to support multi-person dialogue scenarios.

[0164] Furthermore, the overall processing latency of the method is controlled within a preset real-time threshold, wherein the text detection, translation, typesetting replication and rendering stages are executed in parallel through pipeline to minimize end-to-end latency.

[0165] Furthermore, when the aforementioned real-time translation method is deployed as a cognitive augmentation scenario in a cognitive augmentation platform with a scenario registry, the corresponding scenario definition in the scenario registry registers the method as a real-time translation scenario and specifies the functional modules, AI translation model, and selective transformation pipeline modes required for translation.

[0166] Furthermore, the method also includes a life safety-level failure protection mechanism. When the system detects any serious fault that may affect user safety, it immediately triggers the highest priority security overlay event, forcing the system to enter a pure optical perspective mode and disabling all digital rendering.

[0167] Furthermore, the method also includes a privacy protection mechanism, wherein: the user's personalized data is encrypted and stored on the edge device and not transmitted to the cloud; the upgrade of the AI ​​model is achieved through federated learning and differential privacy technology to protect user privacy.

[0168] Furthermore, presenting the translation to the user in a way that is related to the original content space includes rendering and presenting the translation in a way that preserves the layout and font style of the original content through a layout replication module.

[0169] A computer-readable storage medium comprising a computer program that, when executed on an electronic device, causes the electronic device to perform any of the methods described above.

[0170] Example 1: General Cognitive Augmentation Platform Architecture, refer to Figures 1 to 6 This embodiment provides a core architecture implementation scheme for a general cognitive enhancement platform.

[0171] I. System Overall Architecture like Figure 1 As shown, the general cognitive enhancement platform of this invention adopts a layered architecture design, comprising, from bottom to top: a Hardware Abstraction Layer (HAL), an edge-side inference engine layer, a general content transformation pipeline layer, a multi-agent collaboration framework layer, and a scene registry and resource scheduling layer. The core design concept of this architecture is "the platform is one, the scenes are many"—all underlying modules are scene-independent general components, while specific cognitive enhancement functions (such as translation, vision correction, etc.) are registered in the platform in the form of "scenes," and functional expansion is achieved through configuration rather than coding.

[0172] II. Scenario Registry like Figure 3 As shown, the scene registry is the core mechanism for implementing the "unified platform and N scenes" architecture of this invention. The scene registry is a persistent data storage structure, where each scene definition includes the following fields: scenario_id (scenario identifier): A globally unique string identifier used to uniquely identify a cognitive augmentation scenario in the system, such as "realtime_translation" or "vision_correction".

[0173] required_agents (list of required software agents): An ordered list that defines the software agents that need to be activated for the operation of this scene and their cooperation order, such as ["PerceptionAgent", "TextDetectionAgent", "TranslationAgent", "RenderAgent"].

[0174] `required_models` (List of required AI model capabilities): Defines the types of AI model capabilities interfaces required for this scenario, such as ["ITranslationModel", "ITextDetectionModel"]. The platform selects and loads the corresponding model instance from the model registry based on this field.

[0175] `pipeline_mode`: Specifies the content transformation pipeline operation mode used in this scenario. Values ​​can be "selective" or "global". Translation scenarios use selective transformation mode, while vision correction scenarios use global transformation mode.

[0176] default_priority: An integer value that defines the default resource scheduling priority for this scenario when multiple scenarios are running concurrently. The higher the value, the higher the priority. For example, the priority of the vision correction scenario (95) is higher than that of the translation scenario (80) because vision correction directly affects the user's basic visual safety.

[0177] resource_profile (resource configuration file): Defines the computing resource quota required for running this scene, including parameters such as CPU usage limit, GPU / NPU usage limit, memory quota and frame rate requirements, which are used by the multi-scene resource scheduler to allocate resources.

[0178] `required_hardware` (list of required hardware modules): Defines the hardware modules required for the scene to run. For example, a vision correction scene requires ["WavefrontSensor", "EyeTracker", "AdaptiveLens"]. The platform automatically initializes the required hardware modules through the Hardware Abstraction Layer (HAL) when loading the scene.

[0179] fallback_chain (fallback chain configuration): Defines the specific behavior and degradation strategy of each level in the five-level fallback chain for this scenario, ensuring that the scenario can provide an appropriate level of service under various failure conditions.

[0180] Metadata (scene metadata): Includes auxiliary information such as scene version number, description, author, and creation time, used for scene management and version control.

[0181] When a user requests to activate a scene, the platform reads the scene definition from the scene registry, then loads the required software agents and AI models, configures the pipeline mode, allocates computing resources, and initializes the necessary hardware modules according to the definition. This process is entirely data-driven; adding a new scene only requires adding a new scene definition record to the registry, without modifying the platform's core code.

[0182] Those skilled in the art will understand that the scenario registry is a broad concept of configuration data storage, encompassing any form of scenario configuration data storage structure, including but not limited to database tables, JSON / XML configuration files, distributed key-value stores, in-memory data structures, or other equivalent structured data storage forms. The functional module is a broad concept of an intermediate processing layer, encompassing any form of software component, service, proxy, or direct call logic, as long as it can coordinate and execute the processing flow of cognitive enhancement functions.

[0183] III. General Content Transformation Pipeline (Dual Mode) like Figure 2 As shown, the general content transformation pipeline is the core data flow channel for the platform to process all cognitive augmentation tasks, and it includes five stages: input source acquisition → content detection → region classification → transformation decision → selective synthesis.

[0184] The key innovation of this invention lies in the fact that the pipeline supports two operating modes: (1) Selective Transformation Mode: Only regions in the input data identified as needing enhancement are processed. For example, in a translation scenario, only the detected foreign language text regions are translated and rendered, while the remaining regions remain unchanged and are passed through. This mode achieves selective occlusion through GPU Stencil Buffer technology. Specifically, in the GPU rendering pipeline, a Stencil value is assigned to each pixel, with regions requiring enhancement set to 1 and regions not requiring enhancement set to 0. During rendering, enhancement content is only applied to regions with a Stencil value of 1, while regions with a Stencil value of 0 directly pass through the original image. At the boundary between the enhanced and original regions, Alpha blending technology is used to achieve a smooth transition and avoid visual abruptness.

[0185] (2) Global Transformation Mode: Processes all content of the input data. For example, in a vision correction scenario, the entire image frame needs to be pre-distorted to correct the user's visual aberrations. In this mode, the pipeline skips the "region classification" stage and directly applies the transformation algorithm to the entire frame.

[0186] The pipeline mode is specified by the `pipeline_mode` field in the scene registry, and is automatically configured by the platform when loading the scene. Those skilled in the art should understand that the pipeline mode is a broad configuration concept, encompassing any configuration parameters, policy identifiers, or processing scope indicators used to distinguish between local and global processing, and its specific implementation is not limited to the two naming patterns mentioned above.

[0187] IV. Multi-Agent Collaboration Framework like Figure 4 As shown, the platform adopts a multi-agent collaborative architecture, with each agent responsible for an independent functional domain. The platform predefines the following 8 core agents: (1) Perception Agent: Responsible for acquiring raw data from input sources such as cameras and microphones. (2) Text Detection Agent: Responsible for real-time detection and extraction of text from image frames. (3) Translation Agent: Responsible for calling the translation model to perform text translation. (4) Render Agent: Responsible for rendering the enhanced content onto the display screen. (5) Voice Agent: Responsible for speech recognition (STT) and speech synthesis (TTS). (6) Quality Agent: Responsible for quality verification of the AI ​​model output. (7) Vision Correction Agent: Responsible for calling the vision correction model to perform image pre-distortion. (8) Vision Assessment Agent: Responsible for calling the vision assessment model to perform continuous vision monitoring.

[0188] Agents communicate asynchronously via an event bus. The event bus supports the following 14 event types: TextDetected (text detection complete), TranslationReady (translation complete), FrameCaptured (frame capture complete), CorrectedFrameReady (corrected frame ready), VisionProfileUpdated (visual profile updated), ModelSwapRequested (model replacement request), DegradationTriggered (degradation triggered), AmbientLightChanged (ambient light change), GazeDepthChanged (gaze depth change), VisualFatigueDetected (visual fatigue detection), SafetyOverrideTriggered (safety override triggered), ScenarioActivated (scenario activated), ScenarioDeactivated (scenario deactivated), and UserInteractionDetected (user interaction detected).

[0189] Different scenarios use different subsets of Agents. The required_agents field in the scenario registry defines the Agents required for each scenario. When loading a scenario, the platform only activates the required Agents, keeping unused Agents dormant to conserve resources.

[0190] V. Model Abstraction Layer and Model Registry The platform manages all AI models through a model abstraction layer. This abstraction layer defines a unified set of capability interfaces, with each interface corresponding to a specific AI capability. `ITranslationModel`: Translation capability interface, defining the `translate(text, source_lang, target_lang, context)` method. `ITextDetectionModel`: Text detection capability interface, defining the `detect_and_recognize(image_frame)` method. `ITTSModel`: Speech synthesis capability interface, defining the `synthesize(text, voice_profile)` method. `ISTTModel`: Speech recognition capability interface, defining the `transcribe(audio_stream, language)` method. `IVisionCorrectionModel`: Vision correction capability interface, defining the `correct(frame, vision_profile, gaze_depth)` method. `IVisionAssessmentModel`: Vision assessment capability interface, defining the `assess(eye_tracking_data, mode)` method.

[0191] The model registry stores metadata for all available AI models, including model_id (model identifier), capability (model capability type, corresponding to one of the above interfaces), location (model location, on the device or in the cloud), context_window (context window size), cost (call cost), and status (model status).

[0192] like Figure 16 As shown, hot model replacement is achieved through the following five steps: (1) Decision: The AI ​​model router or administrator decides to replace a model; (2) Initialization: The new model is loaded into memory and warmed up; (3) Routing: New requests are routed to the new model; (4) Drain: The old model waits for all accepted requests to be completed; (5) Unloading: The old model is unloaded from memory. This process ensures the continuity of service during model replacement, and users will not perceive any interruption.

[0193] VI. Multi-scenario Resource Scheduler like Figure 5 As shown, when multiple cognitive enhancement scenarios need to run simultaneously (e.g., a user simultaneously turns on translation and vision correction), the multi-scenario resource scheduler is responsible for allocating limited computing resources among them.

[0194] The scheduler employs a "priority queue and dynamic resource quota" model. Each scenario defines `default_priority` and `resource_profile` in the scenario registry. The scheduler allocates resources according to the following rules: (1) Basic allocation: Each active scenario obtains a basic resource quota based on its resource_profile. (2) Priority scheduling: When resources are insufficient, high-priority scenarios are given priority in obtaining resources. (3) Interaction enhancement: When it is detected that a user is interacting with a scenario (e.g., gazing at translation results or adjusting vision correction parameters), the priority of that scenario is instantly enhanced to ensure the real-time nature of the interaction response. (4) Elastic scaling: When a scenario is temporarily inactive, its resource quota can be temporarily borrowed by other scenarios.

[0195] When multiple scenes run concurrently, each scene's content transformation pipeline operates independently, maintaining its own input queue and output buffer. The rendering agent is responsible for compositing the enhanced content from multiple scenes during the final display stage: for globally transforming scenes (such as vision correction), its output serves as the base frame; for selectively transforming scenes (such as translation), its output is superimposed on the base frame. When resource contention exists between scenes, the scheduler determines the frame rate and resolution quotas for each scene using the aforementioned priority rules, ensuring that the real-time performance of high-priority scenes is not affected.

[0196] In multi-scenario concurrent operation, the platform also provides a data sharing mechanism and an enhanced region conflict resolution strategy. The data sharing mechanism allows different scenarios to share input data and intermediate processing results. For example, pre-distorted image frames generated in a vision correction scenario can be directly used as input for a translation scenario, avoiding redundant image acquisition and preprocessing, thereby reducing system resource consumption. This mechanism corresponds to the data sharing mechanism described in the claims. The enhanced region conflict resolution strategy is used to handle situations where enhanced content from multiple scenarios overlaps spatially. When multiple scenarios (such as translation and educational enhancement scenarios) in selective transformation mode simultaneously need to present enhanced content in the same spatial area, the system determines the display priority order of each area based on scenario priority and user preference. Enhanced content from high-priority scenarios is displayed first, while enhanced content from low-priority scenarios can be presented through semi-transparent overlay or edge offset, ensuring that users can simultaneously obtain enhanced information from multiple scenarios without visual confusion. This mechanism corresponds to the enhanced region conflict resolution steps described in the claims.

[0197] VII. Hardware Abstraction Layer (HAL) like Figure 6 As shown, the Hardware Abstraction Layer (HAL) manages various hardware modules in a pluggable manner through a standardized IHardwareModule interface. The IHardwareModule interface defines the following methods: `initialize()`: Initializes the hardware module. `getCapabilities()`: Returns a description of the hardware module's capabilities. `execute(command)`: Executes a specific hardware control command. `getStatus()`: Returns the current status of the hardware module. `shutdown()`: Safely shuts down the hardware module.

[0198] Specific hardware modules connect to the platform by implementing the IHardwareModule interface. For example: Electrochromic lens module: Implements the `setTint(level)` method to control the lens's light transmittance. Adaptive optics lens module: Implements the `setFocus(diopter)` method to control the lens's focal length. Wavefront aberration sensor module: Implements the `measureWavefront()` method to measure the user's eye aberration data. Bone conduction module: Implements the `playBoneConductionAudio(audioData)` method to transmit audio signals to the user via bone conduction technology, supporting hearing enhancement scenarios.

[0199] Integrating new hardware modules only requires implementing the IHardwareModule interface and registering it in the HAL; no modification to the platform's core code is needed. The required_hardware field in the scene registry specifies the hardware modules required for each scene, and the platform automatically initializes the necessary hardware when loading a scene.

[0200] 8. Speculative Cascaded Workflow like Figure 14 As shown, the speculative cascading workflow is the core mechanism of this invention for balancing latency and quality. This workflow includes the following steps: (1) On-device drafting: On the device, a lightweight AI model (such as a quantized miniature model) is used to quickly generate a draft version of the enhanced content and present it to the user immediately. For translation scenarios, this means displaying a preliminary translation result in about 200 milliseconds; for vision correction scenarios, this means quickly generating a correction frame using a basic defocus correction algorithm.

[0201] (2) Cloud-based refinement: At the same time, the original input data is sent to the cloud server, and a high-precision large AI model generates a refined version of the enhanced content.

[0202] (3) Semantic consistency judgment: After receiving the refined version in the cloud, calculate the semantic consistency score between it and the draft version on the client side. If the score is higher than the preset threshold (e.g., 0.9), the draft version is considered good enough and will not be replaced.

[0203] (4) Seamless replacement: If the semantic consistency score is below the threshold, the refined version will be seamlessly replaced with the draft version by a fade-in / fade-out animation (lasting about 200 milliseconds). Users will only perceive the "optimization" of the content rather than the "jump".

[0204] IX. Intelligent Routing and Semantic Caching like Figure 15 As shown, intelligent routing and semantic caching mechanisms are used to optimize the calling efficiency of AI models and reduce costs.

[0205] (1) AI Model Router: After receiving the input to be processed, the AI ​​Model Router dynamically selects the optimal AI model based on the following factors: the language of the input content (some models are better at specific languages), the complexity of the content (lightweight models are used for simple content, and large models are used for complex content), and the current network status (edge-side models are preferred when the network is poor). Routing decisions are completed in milliseconds.

[0206] (2) Semantic Caching: Utilize a vector database to store historical processing results. When a new input arrives, it is first converted into a high-dimensional vector representation using a lightweight text embedding model. Then, semantic similarity is compared with the entries in the cache using cosine similarity or dot product similarity. If the similarity exceeds a preset threshold (e.g., ≥0.95), the cached result is returned directly, skipping AI model inference, thereby reducing the response time from hundreds of milliseconds to single digits.

[0207] like Figure 13 As shown, the five-level fallback chain is the core robustness mechanism of this invention. When the system detects performance degradation, model errors, or network interruptions, it automatically downgrades the service from the current level to a preset lower level, ensuring that users always receive some degree of cognitive enhancement service, rather than complete failure.

[0208] The specific definition of a five-level fallback chain is as follows: L1 (Full Service Level): All AI models and hardware modules are operating normally, and the system provides complete cognitive augmentation services. Edge models and cloud models work together, inference cascade workflows execute normally, and all scenarios run at the highest quality.

[0209] L2 (Edge-based Degradation): When cloud connectivity is unavailable or latency is too high, the system automatically switches to pure edge-based mode. Only lightweight AI models deployed on the edge are used for inference, and cloud-based refinement processes are disabled. Cognitive augmentation quality is slightly reduced, but functionality remains complete; latency may increase but is still within acceptable limits.

[0210] L3 (Basic Rule Level): When the edge AI model encounters anomalies or insufficient resources during inference, the system reverts to a processing mode based on predefined rules. For example, in translation scenarios, it reverts to word-by-word translation based on a dictionary, and in vision correction scenarios, it reverts to fixed-parameter correction based on user-stored prescription data, no longer performing dynamic pupil tracking and real-time PSF adjustment.

[0211] L4 (Minimum Service Level): When the basic rules engine also fails to function properly, the system provides only minimal enhancement services. For example, in a translation scenario, only the detected text regions are highlighted without translation, and in a vision correction scenario, only the most recently successfully calculated static correction parameters are applied.

[0212] L5 (Safety Mode Level): When the system detects any serious fault that may affect user safety, it immediately triggers life-safety-level failure protection. The system completely disables all digital rendering and image processing, switches to pure optical see-through mode (for AR glasses) or displays the unprocessed raw image (for screen devices), ensuring that the user's basic visual safety is not affected. This level corresponds to the SafetyOverrideTriggered event in vision correction scenarios.

[0213] The circuit breaker uses a three-state model to control degradation behavior: Closed State: Normal operation, all requests are processed normally. Open State: Upon detecting a fault, the circuit breaker opens, all requests are degraded and no further attempts are made to invoke the faulty component. Half-Open State: After a preset cooldown period, the circuit breaker enters a half-open state, allowing a small number of requests to attempt to invoke the faulty component. If successful, it returns to the closed state; if it fails, it returns to the open state.

[0214] This mechanism avoids frequent switching between unstable service levels, ensuring a stable user experience. For vision correction scenarios, Level 5 is defined as "life safety level"—when the system detects any serious fault that may affect user safety (such as latency exceeding a safety threshold), it immediately triggers the SafetyOverrideTriggered event, forcing the system to enter pure optical vision mode to ensure the user's physical safety.

[0215] XI. Hallucination Inhibition Mechanism like Figure 17 As shown, the illusion suppression mechanism is used to verify the reliability of the AI ​​model's output and prevent AI "illusions" (i.e., generating seemingly reasonable but actually erroneous content) from misleading users.

[0216] Taking translation as an example, the workflow of this mechanism is as follows: (1) The AI ​​translation model translates the source language text into the target language text. (2) The target language text is translated back into the source language. (3) The semantic similarity score between the reverse translation result and the original source language text is calculated. (4) If the semantic similarity score is lower than a preset threshold (e.g., 0.7), the translation result is marked as "suspicious" and a downgrade process is triggered (e.g., displaying the original text or retranslating using a backup translation model).

[0217] This mechanism can be extended to other scenarios: in vision correction scenarios, the structural similarity (SSIM) of images before and after correction can be compared to verify whether the correction algorithm has introduced unacceptable artifacts.

[0218] Example 2: Real-time translation scenario, refer to Figure 12 This embodiment describes in detail a real-time translation cognitive enhancement scenario implemented based on the platform architecture described in Embodiment 1.

[0219] I. Scene Registration The translation scene is defined in the scene registry as follows: scenario_id: "realtime_translation" required_agents: ["PerceptionAgent", "TextDetectionAgent", "TranslationAgent", "RenderAgent", "VoiceAgent", "QualityAgent"] required_models: ["ITextDetectionModel", "ITranslationModel", "ITTSModel", "ISTTModel"] required_hardware:["camera","display","imu","microphone","speaker"] pipeline_mode:"selective" default_priority:80resource_profile:{cpu:"40%",gpu:"50%",memory:"512MB"}version:"1.0" When a user activates a translation scenario, the platform loads 6 agents and 4 model capabilities according to the above definition, and configures the pipeline to "selective transformation" mode.

[0220] II. Sub-Scene 1: Text Translation in the Physical World Physical world text translation refers to the process where users see foreign language text (such as road signs, menus, documents, etc.) through the camera of AR glasses, and the system automatically translates it into the user's native language and displays it in an AR overlay manner.

[0221] The millisecond-level timing pipeline for this sub-scene is as follows: The workflow of the layout replication module (such as) Figure 12 (as shown) (1) Text bounding box detection: The bounding box coordinates of each text region in the image are accurately identified by a convolutional neural network (CNN).

[0222] (2) Perspective transformation: Using the device attitude data provided by the inertial measurement unit (IMU), the perspective transformation matrix from the camera viewpoint to the display screen viewpoint is calculated to ensure that the translated text is accurately aligned with the original text in three-dimensional space.

[0223] (3) Original text removal and repair: Use image inpainting technology to remove the original text and fill the background.

[0224] (4) Translation rendering: The translated text is rendered onto the repaired area in a way that matches the original font style (size, color, weight).

[0225] (5) Spatial anchoring: Using the 3D coordinate system provided by ARKit / ARCore, the translation result is anchored to a fixed position in the physical world to ensure that the translated text does not drift when the user moves their head.

[0226] The above processing stages are executed using a pipeline parallelism approach. That is, when the Nth frame is in the translation stage, the N+1th frame has entered the text detection stage, and the N+2th frame is in the image acquisition stage. Through this multi-stage pipeline overlapping execution, the overall processing latency of the system is controlled within a preset real-time threshold, and the end-to-end latency is significantly lower than the sum of the latencies of each stage.

[0227] LLM contextual reasoning plays a crucial role in translation: large language models not only perform sentence-by-sentence translation, but also use contextual information understood from the scene to resolve ambiguities (e.g., interpreting "bank" as "riverbank" rather than "bank" in a restaurant scene), maintain terminology consistency (ensuring consistent translation of the same terms in the same document), and cultural adaptation (converting culture-specific expressions in the source language into equivalent expressions in the target language).

[0228] 3. Sub-scenario 2: Real-time oral dialogue translation Real-time oral dialogue translation supports face-to-face communication between people speaking different languages. This sub-scenario achieves a near-simultaneous interpretation experience through a streaming pipeline: (1) Voice Activity Detection (VAD): Continuously monitors microphone input and begins processing when voice activity is detected. (2) Speech-to-Text (STT): The voice agent calls the ISTTModel to convert the speech stream into text in real time. (3) Translation: The translation agent calls the ITranslationModel to translate the text into the target language. (4) Text-to-Speech (TTS): The voice agent calls the ITTSModel to synthesize the translated text into speech output in the target language.

[0229] In face-to-face scenarios, the voice agent acts as both the input (receiving user voice) and the output (playing translated voice). The quality agent monitors the translation quality throughout and marks suspicious translations. The system also supports speaker separation, enabling it to distinguish between different speakers in a conversation and translate them separately.

[0230] IV. Sub-scenario 3: Zero OCR Translation of Electronic Versions Electronic translation refers to the translation of electronic documents, web pages, application interfaces, etc., displayed on mobile phone or computer screens. The key innovation of this invention lies in "zero OCR" technology—by directly accessing the operating system's UI element tree (Android's AccessibilityService) or Document Object Model (DOM), it obtains the text content on the screen, completely bypassing the traditional OCR recognition steps, thereby achieving higher accuracy and lower latency.

[0231] This sub-scene supports multiple presentation modes: (1) One-click perspective mode: The translation is displayed when the user presses and holds the button, and the original text is restored when the button is released. (2) Split-screen comparison mode: The original text is displayed in the upper half of the screen, and the translation is displayed in the lower half. (3) Overlay mode: The translation is directly overlaid on the original text. (4) New file mode: A complete translated document is generated.

[0232] On the iOS platform, due to system limitations, the electronic translation adopts a scenario-based degradation strategy: (1) Safari browser: Translation script is injected via Safari Extension. (2) EPUB e-book: Implemented via EPUB reader extension. (3) PDF document: Text is extracted via PDFPage.string API; for scanned PDFs, it falls back to image text recognition. (4) Office document: Implemented via Document ProviderExtension. (5) Other applications: Image text recognition is performed after capturing screen content via Screen Broadcast API.

[0233] Example 3: AI vision correction and protection scenario, refer to Figures 7 to 11 , Figure 19 and Figure 20 This embodiment describes in detail the AI ​​vision correction and protective cognitive enhancement scenario implemented based on the platform architecture described in Embodiment 1.

[0234] I. Scene Registration The vision correction scene is defined in the scene registry as follows: scenario_id: "vision_correction" required_agents: ["PerceptionAgent", "VisionCorrectionAgent", "VisionAssessmentAgent", "RenderAgent"] required_models: ["IVisionCorrectionModel", "IVisionAssessmentModel"] required_hardware:["camera","display","eye_tracker","ambient_light_sensor","adaptive_lens","wavefront_sensor"] pipeline_mode:"global" default_priority:95resource_profile:{ cpu:"30%",gpu:"60%",memory:"256MB"} version:"1.0" The default_priority for vision correction scenarios is set to 95, higher than 80 for translation scenarios, because vision correction directly impacts the user's basic visual experience and has higher real-time and security requirements. The pipeline mode is set to "global" because vision correction requires processing the entire image frame.

[0235] II. Complete Workflow of "Digital Optometry" This invention proposes a novel "digital optometry" workflow that uses software algorithms to replace traditional physical lens prescriptions for correcting a user's vision. This workflow comprises three stages: Phase 1: First use of precise assessment (30-60 seconds) When a user first wears the smart glasses equipped with the system of this invention, the system initiates a quick but accurate vision assessment process: (1) The wavefront aberration sensor (called through the measureWavefront() method of HAL) measures the optical aberrations of the user's eye and obtains complete aberration data including spherical aberration, coma, astigmatism, etc.

[0236] (2) The eye-tracking camera captures baseline data such as the user's pupil size and eye movement patterns.

[0237] (3) The vision assessment agent calls the assessment(eye_tracking_data, "active") method of IVisionAssessmentModel to generate the user's "visual fingerprint (i.e. personalized visual aberration data)" - a personalized visual profile containing a complete set of PSF (point spread function) parameters.

[0238] (4) The entire evaluation process is completed within 30-60 seconds. Users only need to look straight ahead normally and do not need to cooperate in any way. The evaluation data is encrypted and stored on the device and is not transmitted to the cloud.

[0239] Phase Two: Real-time Correction in Daily Use like Figure 7 As shown, during daily use, the system continuously executes the following real-time correction process: (1) The camera captures image frames of the external world at a frame rate of not less than 60fps.

[0240] (2) The vision correction agent calls the correct(frame, vision_profile, gaze_depth) method of IVisionCorrectionModel to perform pre-distortion processing on each frame of the image.

[0241] (3) The core principle of the pre-distortion algorithm: Based on the user's PSF data, the image is subjected to deconvolution or pre-distortion. The deconvolution operation aims to "undo" the blurring caused by the user's eye optical system to the image; the pre-distortion operation applies a distortion to the image that is "complementary" to the user's eye aberration, so that the image forms a clear image on the retina after passing through the user's eye optical system. Specifically, in the frequency domain deconvolution method, let the frequency domain representation of the original image be G(u,v), and the frequency domain representation (optical transfer function, OTF) of the user's eye PSF be H(u,v), then the frequency domain representation F̂(u,v) of the corrected image is calculated by the Wiener filter as: F̂(u,v)=[H*(u,v) / (|H(u,v)|²+K)]×G(u,v), where H*(u,v) is the conjugate complex number of H(u,v), and K is the regularization parameter of the power spectral ratio of noise to signal, which is used to suppress noise amplification.

[0242] (4) such as Figure 8As shown, the system maintains a multi-pupil PSF lookup table, containing PSF data for different pupil sizes from 2mm to 8mm. In bright environments, the pupil constricts to approximately 2mm, with low-order aberrations predominating (such as defocus and astigmatism); in dim environments, the pupil dilates to approximately 8mm, with higher-order aberrations (such as spherical aberration and coma) significantly increasing. Based on the pupil size monitored in real-time by the eye-tracking camera, the system dynamically selects or interpolates the corresponding PSF from the lookup table to ensure optimal correction under any lighting conditions. It should be noted that the real-time monitoring of the user's pupil size includes, but is not limited to: directly measuring the pupil diameter through the eye-tracking camera, indirectly estimating the pupil size using ambient light sensor data combined with a preset pupil-light response model, or a combination of the above methods.

[0243] (5) The corrected image frame is displayed on the screen in front of the user through the rendering agent. The processing delay of the PSF deconvolution core algorithm is about 8-12 milliseconds / frame (accelerated by frequency domain fast Fourier transform). With the overhead of image acquisition and rendering, the entire end-to-end processing delay is controlled within 20 milliseconds, ensuring that the user will not perceive any obvious delay.

[0244] Phase Three: Regular Calibration and Health Trends The system uses continuous vision assessment (such as...) Figure 9 (As shown) to track changes in the user's vision: (1) Proactive assessment: Regularly (e.g., weekly) remeasure the user’s aberration data using a wavefront aberration sensor to update the visual fingerprint.

[0245] (2) Passive monitoring: During daily use, the vision assessment agent continuously analyzes eye-tracking data, including blink frequency (normal is 15-20 times / minute, which drops to less than 5 times / minute when fatigued), saccade pattern, and pupillary response. When these indicators deviate from the baseline, the system determines that the user's vision may have changed and automatically triggers an active assessment.

[0246] (3) Vision change trend report: The system records the results of each assessment and generates a long-term vision change trend report to help users understand their vision health status.

[0247] This combination of "passive monitoring and active assessment" ensures that vision correction parameters are always precisely matched to the user's current vision status, without requiring the user to manually adjust them or have regular eye exams.

[0248] III. Technological Implementation of Real-Time Vision Correction The vision correction system of the present invention can correct a full range of refractive errors, including myopia, hyperopia, astigmatism, presbyopia and amblyopia.

[0249] For myopia and hyperopia: compensation is achieved by adjusting the defocus parameter in the pre-distortion algorithm. For astigmatism: compensation is achieved by applying different correction intensities in specific meridional directions. For presbyopia: by combining fixation depth data (provided by eye tracking), correction parameters are dynamically adjusted to achieve an effect similar to progressive multifocal lenses, but with higher precision and no aberration zones. For amblyopia: training is aided by selectively enhancing image contrast and sharpness in the amblyopic eye.

[0250] IV. Active Vision Protection like Figure 19 As shown, the active vision protection function provides users with comprehensive vision protection through the collaborative work of software algorithms and electrochromic lenses (controlled by HAL's setTint(level) method).

[0251] Visual fatigue monitoring: The system continuously monitors the user's blinking frequency through eye tracking. When the blinking frequency drops from the normal 15-20 times / minute to below 5 times / minute, the system determines that the user is in a state of visual fatigue and automatically triggers the following protective measures: (1) issuing a VisualFatigueDetected event; (2) displaying a rest reminder on the display screen (based on the 20-20-20 rule: look at an object 20 feet away for 20 seconds every 20 minutes); (3) automatically reducing the display brightness and color temperature to reduce eye strain.

[0252] V. Adaptive Optical Lens Synergy like Figure 10 As shown, this invention supports a collaborative solution of "basic hardware adjustment and fine-tuning software": (1) Adaptive optical lenses (such as liquid crystal lenses) use HAL’s setFocus(diopter) method to adjust the basic focal length and compensate for the user’s main refractive error (such as high degree of myopia).

[0253] (2) Based on this, the software algorithm performs fine correction for higher-order aberrations (such as spherical aberration, coma, etc.).

[0254] The advantages of this collaborative approach are: the hardware lens handles a wide range of focal length adjustments, reducing the computational burden on the software algorithm; the software algorithm handles higher-order aberrations that the hardware lens cannot compensate for, providing correction accuracy that surpasses that of traditional lenses.

[0255] VI. Convergence-Regulation Conflict (VAC) Solution like Figure 11As shown, convergence-accommodation conflict (VAC) is a core physiological challenge in video perspective (VST) display systems: all objects are displayed on a screen with a fixed focal length, but the convergence (angle of inward rotation of the eyes) of the user's eyes changes with the distance of the object being viewed, resulting in a mismatch between convergence and accommodation (focusing of the lens), which can cause visual fatigue and headaches.

[0256] This invention proposes a three-stage progressive solution: (1) Recent solution - zoom display: control a zoom display through HAL, and dynamically adjust the display focal plane according to the user's gaze depth data provided by eye tracking so as to match the user's convergence distance.

[0257] (2) Mid-term solution - Light field display: Using light field display technology, images of multiple focal planes are presented simultaneously, and the user's eyes can naturally switch focus between different focal planes.

[0258] (3) Long-term solution - Holographic display: Using holographic display technology, each pixel is provided with independent depth information, completely eliminating the VAC problem.

[0259] VII. Binocular parallax correction The system of this invention supports two display architectures: Video See-Through (VST) and Optical See-Through (OST). In the VST architecture, the user observes the external world through a video stream captured by a camera, and enhanced content is directly superimposed on the video frames. In the OST architecture, the user observes the external world directly through a transparent lens, and enhanced content is superimposed on the user's field of vision via an optical waveguide or microprojector. For the OST architecture, the implementation of vision correction scenarios differs: since the user observes the external world directly through the lens, the system corrects the user's visual aberrations by superimposing a compensating optical pattern, rather than the full-frame pre-distortion method in the VST architecture. The platform automatically adapts to different display architectures through hardware configuration fields in the scene registry.

[0260] like Figure 20 As shown, due to the discrepancy between the physical position of the camera on the smart glasses and the position of the user's pupils (the camera is usually located above or to the side of the frame), there is a parallax between the images captured by the left and right eye cameras and the images actually seen by the user. This invention corrects binocular parallax through the following steps: (1) During the factory calibration phase, the physical positional deviation between the camera and the pupil is accurately measured. (2) In real-time processing, based on the deviation data and the current gaze depth, the image frames for the left and right eyes are subjected to parallax compensation transformation respectively. (3) The compensated image frames provide the user with a stereoscopic visual experience consistent with natural naked-eye viewing.

[0261] 8. Gradual Adaptation Model Considering that users may need an adaptation process when switching from traditional glasses to digital vision correction, this invention provides a gradual adaptation mode: (1) When using the system for the first time, it starts with a low correction intensity (e.g., 50% of the target correction amount). (2) During the preset adaptation period (e.g., 7 days), the system gradually increases the correction intensity each day. (3) The system monitors the user's adaptation (e.g., blink frequency, fixation stability) through eye-tracking data. If discomfort is detected, the system automatically slows down the rate of increase. (4) After the adaptation period, the system reaches the level of full correction.

[0262] Users can manually adjust the correction intensity at any time to maintain complete control over their visual experience.

[0263] Example 4: Robustness and Security Assurance. This example describes in detail the robustness and security assurance mechanisms of the platform.

[0264] I. Life safety level failure protection For vision correction scenarios, the system's failure protection is defined as "Life-Safety Critical." This means that when the system detects any of the following situations, it must switch to a safe state in the shortest possible time (target <100 milliseconds): (1) Image processing latency exceeds a safety threshold (e.g., >50 milliseconds). (2) Abnormal output of the correction algorithm (e.g., image structure similarity SSIM is below the threshold). (3) Hardware module failure (e.g., camera malfunction). (4) Extremely low battery power (e.g., <5%).

[0265] The safety switching is implemented as follows: The system issues a SafetyOverrideTriggered event, which has the highest priority. It immediately interrupts all ongoing processing and forces the system into a pure optical perspective mode—disabling all digital rendering. If the device supports it, it opens the optical perspective channel (e.g., by flipping the VST light shield) so that the user can directly see the physical world.

[0266] In addition, the system provides physical safety switches (such as double-clicking the temple or pressing a dedicated button), allowing users to trigger a safety switch immediately at any time through physical operation, independent of the normal operation of the software system.

[0267] II. Specific Robustness in Vision Correction Scenarios Based on the five-level fallback chain, a scenario-specific degradation strategy is defined for vision correction scenarios: The degradation strategy for vision correction scenarios is specifically designed to address the unique security requirements of this scenario, as follows: L1→L2 downgrade (not available in the cloud): Disables the refinement process of the high-precision aberration model in the cloud, and only uses the lightweight end-side correction model. The correction accuracy is downgraded from high-order aberrations (including spherical aberration, coma, etc.) to correcting only low-order aberrations (myopia, hyperopia, astigmatism), but the basic vision correction function is fully retained.

[0268] L2→L3 Degradation (End-side AI Model Anomaly): Disables the dynamic correction algorithm based on the AI ​​model, reverting to fixed correction based on the static prescription parameters stored from the user's most recent successful assessment. Real-time pupil tracking and dynamic PSF adjustment are stopped, and pre-calculated correction parameters corresponding to a fixed medium pupil size (approximately 4mm) are used.

[0269] L3→L4 Degradation (Rule Engine Anomaly): All active correction calculations are stopped, and only the static display of the most recently successfully rendered correction frame is maintained. The system enters "freeze frame" mode and issues a visual cue to the user (such as a flashing orange warning box at the edge of the screen), suggesting that the user suspend activities that rely on vision correction.

[0270] L4→L5 Degradation (Safety Mode): Immediately triggers the SafetyOverrideTriggered event, completely disabling all digital image processing and rendering within 100 milliseconds. For AR glasses devices, it switches to pure optical see-through mode, ensuring users can directly observe the outside world through transparent lenses; for screen devices, it displays the unprocessed raw camera image. Simultaneously, it activates the physical safety switch's listener, ensuring users can trigger the safety switch at any time through physical actions (such as double-tapping the temple).

[0271] The switching between each degradation level is controlled by the circuit breaker's three-state model to avoid frequent switching between unstable states. After degradation, the system continuously monitors the fault recovery status and gradually restores to a higher service level through a half-open state when conditions are met.

[0272] III. Privacy Protection Mechanism This invention places great emphasis on user privacy protection and adopts the following measures: (1) Data minimization: User's visual fingerprint, eye movement data and other personalized data are only processed and stored on the edge device, encrypted with AES-256, and not transmitted to the cloud.

[0273] (2) Federated learning: AI model upgrades are achieved through federated learning technology. Each user device trains and updates the model locally using its own data, and only uploads the gradient of the model parameters (rather than the original data) to the server for aggregation, thereby achieving continuous optimization of the model without exposing user data.

[0274] (3) Differential privacy: During the gradient upload process of federated learning, calibrated noise is added to ensure that even if an attacker obtains the uploaded gradient, they cannot deduce the user's original data.

[0275] (4) The image does not leave the device: The image frames captured by the camera are discarded after processing on the end side and are neither stored nor transmitted.

[0276] (5) User Consent and Data Control: Upon first use, the system informs users of the scope of data processing through a clear user consent process. Users can view, export, or delete their personal data at any time through the settings interface. The retention period for biometric data such as visual fingerprints is the default duration of device ownership, and users can manually set an automatic expiration policy.

[0277] Example 5: Cross-platform adaptation, such as Figure 18 As shown, this embodiment describes the platform's adaptation strategy on different hardware and operating system platforms.

[0278] I. Android Platform On the Android platform, enhanced content is presented in the following ways: (1) Use Android's AccessibilityService to obtain the UI element tree on the screen to achieve "zero OCR" text extraction.

[0279] (2) Use the TYPE_ACCESSIBILITY_OVERLAY type overlay window to display enhanced content over any application.

[0280] (3) This solution does not require modification of the target application and has the highest compatibility.

[0281] II. iOS Platform Due to security restrictions in the iOS system, the platform cannot access the global UI element tree like Android. Therefore, a scenario-based degradation strategy is adopted: (1) Safari browser: Translate scripts are injected via Safari Extension to directly manipulate the webpage DOM. (2) EPUB e-book: Text extraction and translation overlay are achieved through EPUB reader extension. (3) PDF document: Text content is extracted via PDFPage.string API; for scanned PDFs (without extractable text), it reverts to image text recognition. (4) Office document: Implemented via Document Provider Extension. (5) Other applications: Image text recognition is performed after capturing screen content via ScreenBroadcast API.

[0282] III. AR Glasses Platform On the AR glasses platform, the platform utilizes the Android XR SDK and the Jetpack Compose Glimmer framework to achieve spatial rendering of augmented content. AR glasses represent the most complete implementation of this invention because they simultaneously possess camera input, spatial display output, and various sensors, enabling them to support full functionality for all cognitive augmentation scenarios.

[0283] For edge deployment, the platform needs to operate efficiently on the limited computing resources of AR glasses. Key optimization measures include: (1) Model quantization: Quantize the AI ​​model from FP32 precision to INT8 or INT4 precision, significantly reducing computation and memory usage within an acceptable range of precision loss.

[0284] (2) Memory bandwidth optimization: The memory bandwidth of AR glasses is usually 25-50GB / s (far lower than the hundreds of GB / s of desktop GPUs), which has become the main bottleneck for edge inference. This can be alleviated by model quantization (reducing model size) and operator fusion (reducing the number of memory read and write operations).

[0285] (3) NPU acceleration: Prioritize the use of the neural network processing unit (NPU) on the AR glasses chip for AI inference, which has a much higher energy efficiency than general-purpose GPUs.

[0286] Example 6: Platform Extensibility Verification. This example verifies the generality and extensibility of the platform architecture by describing several potential cognitive enhancement scenarios. All of the following scenarios can be implemented by adding new scenario definitions to the scenario registry without modifying the platform's core code.

[0287] I. Assistance Scenarios for Reading Disabilities scenario_id: "dyslexia_assist" required_agents: ["PerceptionAgent", "TextDetectionAgent", "RenderAgent"] required_models: ["ITextDetectionModel", "IDyslexiaModel"] required_hardware: ["camera", "display", "eye_tracker"] pipeline_mode: "selective" default_priority:70 resource_profile: {cpu: "20%", gpu: "30%", memory: "128MB"} version: "1.0" This scenario reuses the platform's text detection agent and rendering agent, adding a new IDyslexiaModel interface to convert the detected text into a reading-impaired friendly format (e.g., adjusting character spacing, using special fonts, highlighting syllables, etc.). The pipeline mode is "selective transformation," processing only text regions.

[0288] II. Privacy Protection Shield Scenarios scenario_id: "privacy_shield" required_agents: ["PerceptionAgent", "RenderAgent"] required_models: ["IPrvacyModel"] required_hardware: ["camera", "display"] pipeline_mode: "selective" default_priority:90 resource_profile: {cpu: "15%", gpu: "25%", memory: "128MB"} version: "1.0" This scenario utilizes IPrivacyModel to detect sensitive information in the field of view (such as other people's faces, license plate numbers, personal documents, etc.), and then uses a rendering agent to blur or occlude these areas. The pipeline mode is "Selective Transformation".

[0289] III. Professional Skills Enhancement Scenarios scenario_id: "professional_enhance" required_agents: ["PerceptionAgent", "RenderAgent", "VisionCorrectionAgent"] required_models: ["IVisionCorrectionModel", "IProfessionalModel"] required_hardware: ["camera", "display", "eye_tracker"] pipeline_mode: "global" default_priority:85 resource_profile:{cpu: "30%", gpu: "50%", memory: "256MB"} version: "1.0" This scenario targets professionals such as surgeons and precision manufacturing workers who require exceptional visual abilities. It reuses a vision correction agent to provide basic vision correction and uses an IProfessionalModel to provide professional enhancement functions such as microscope-level magnification, edge enhancement, and highlighting of key structures. The pipeline mode is "Global Transformation".

[0290] IV. Education Enhancement Scenarios scenario_id: "education_enhance" required_agents: ["PerceptionAgent", "TextDetectionAgent", "TranslationAgent", "RenderAgent"] required_models: ["ITextDetectionModel", "IEducationModel"] required_hardware: ["camera", "display", "eye_tracker"] pipeline_mode: "selective" default_priority:60resource_profile:{cpu: "25%", gpu: "35%", memory: "256MB"} version: "1.0" This scenario targets students and learners, using the IEducationModel to provide instant annotations, explanations, and related knowledge links to detected text or objects, thus enhancing educational content. The pipeline pattern is "Selective Transformation".

[0291] V. Hearing Enhancement Scenarios required_agents: ["PerceptionAgent", "VoiceAgent", "RenderAgent"] required_models: ["ISTTModel", "IAudioEnhanceModel", "ITTSModel"] required_hardware: ["microphone", "display", "speaker", "bone_conductor"] pipeline_mode: "global" default_priority: scenario 80resource_profile:{cpu: "25%", gpu: "30%", memory: "192MB"} version: "1.0" This scenario targets users with hearing impairments or those in noisy environments. It utilizes the IAudioEnhanceModel to intelligently reduce ambient noise and enhance speech, reuses a voice agent for speech recognition, and displays real-time captions in the user's field of vision via a rendering agent. The pipeline mode is "Global Transform," processing all audio inputs. This scenario fully demonstrates the platform's ability to expand from visual to auditory enhancement.

[0292] The implementation of the above five scenarios does not require modification of the platform's core architecture code. Only the following steps are needed: (1) adding scenario definitions to the scenario registry; (2) implementing scenario-specific AI model interfaces (such as IDyslexiaModel, IPrivacyModel, IAudioEnhanceModel, etc.); and (3) developing scenario-specific agents if necessary. This fully verifies the universality and scalability of the "unified platform and N scenarios" architecture of this invention, and proves that this architecture is not only applicable to visual cognitive enhancement, but also to cognitive enhancement through other sensory channels such as hearing.

[0293] Example 7: Scenario SDK. This invention also provides a Scenario SDK to support third-party developers in developing new cognitive enhancement scenarios on this platform. The SDK includes: (1) Scene definition template: Standardized scene registry field templates and filling guidelines. (2) Model interface specification: Definition documents and reference implementations for unified capability interfaces. (3) Agent development framework: Basic classes and event bus access interfaces for developing custom agents. (4) Hardware module access guide: Implementation guidelines and testing tools for the IHardwareModule interface. (5) Debugging and testing tools: Scene simulator, performance analyzer, and compatibility test suite.

[0294] By providing standardized REST / gRPC API interfaces, third-party developers can integrate their AI models and algorithms into the platform in a standardized manner, forming an open cognitive augmentation ecosystem.

[0295] In one implementation, the presentation step supports a selective presentation mode and a global presentation mode. In selective presentation mode, the system merges the enhanced content with the unprocessed original content, allowing the user to see both the enhanced and unenhanced original portions simultaneously. In global presentation mode, the system displays all enhanced content to the user, achieving a complete cognitive enhancement experience.

[0296] The present invention also provides a real-time translation system, comprising at least one input device (for acquiring content to be translated), a display screen (for presenting the translated content to a user), a processor, and a memory. The memory stores an AI translation model and computer-executable instructions. When the instructions are executed by the processor, the system is configured to acquire the content to be translated from the input device, perform context-aware translation using the AI ​​translation model to generate a translated text, and present the translated text to the user in a manner that is spatially associated with the original content.

[0297] In one implementation, the system further includes a privacy protection module configured to encrypt and store users' personalized data (such as visual aberration data, translation preferences, etc.) on the edge device without transmitting it to the cloud, and to upgrade the AI ​​model through federated learning and differential privacy technology, thereby improving model performance while protecting user privacy.

[0298] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for providing scalable cognitive enhancement capabilities, implemented on a computing device, characterized in that, The method includes the following steps: Maintain a scene registry, which stores the definition of at least one cognitive augmentation scene. Each scene definition includes: scene identifier, functional module, processing engine, and pipeline pattern; wherein the processing engine includes an AI model and / or a rule-based processing module; wherein a preferred implementation of the functional module is a software agent based on a multi-agent architecture. Respond to requests to activate specific cognitive enhancement scenarios; Based on the definition corresponding to the specific cognitive enhancement scenario in the scenario registry, load at least one functional module and at least one processing engine required by the scenario, and configure the pipeline mode; The at least one loaded functional module invokes the at least one processing engine to process the input data to generate enhanced content. The enhanced content is presented to the user according to the pipeline pattern.

2. The method according to claim 1, characterized in that, The pipeline modes include a selective transformation mode and a global transformation mode; in the selective transformation mode, only the specific regions identified in the input data are processed; in the global transformation mode, the entire content of the input data is processed.

3. The method according to claim 1, characterized in that, The pipeline mode includes a selective transformation mode. In the selective transformation mode, the method further includes the following steps: detecting and locating a target region from input data through a sensing function module; applying the processing engine to the target region to generate enhanced content; and synthesizing the enhanced content with the unprocessed original content to generate a final output.

4. The method according to claim 1, characterized in that, The functional modules communicate asynchronously through an event bus, which is used to transmit scene lifecycle events and user interaction events.

5. The method according to claim 1, characterized in that, The method further includes: dynamically allocating computing resources to multiple concurrently running cognitive augmentation scenarios through a multi-scenario resource scheduler, based on the priority and resource configuration file defined in the scenario registry.

6. The method according to claim 1, characterized in that, The AI ​​models in the processing engine are managed through a model abstraction layer, which implements dynamic loading, replacement, and routing of AI models through a unified capability interface. The dynamic replacement is achieved through the following steps: decision to replace, initialization of the new model, routing of new requests to the new model, waiting for the old model to complete the accepted request, and unloading the old model.

7. The method according to claim 1, characterized in that, The method also includes a speculative cascaded workflow, which includes: rapidly generating a draft version of enhanced content on the device using a lightweight AI model and presenting it to the user; simultaneously sending input data to the cloud to generate a refined version of enhanced content using a high-precision AI model; and seamlessly replacing the draft version with the refined version when the semantic consistency between the refined version and the draft version is lower than a preset threshold.

8. The method according to claim 1, characterized in that, The method also includes a semantic caching mechanism, which stores historical processing results in a vector database. When the similarity between a newly input semantic vector and an entry in the cache exceeds a preset threshold, the cached processing result is returned directly to reduce latency.

9. The method according to claim 1, characterized in that, The method also includes a five-level fallback chain mechanism and a circuit breaker mechanism. The five-level fallback chain mechanism is configured to automatically downgrade the cognitive enhancement function from the current service level to a preset lower service level when a performance degradation or error is detected. The circuit breaker mechanism has three states: closed, open, and half-open, to prevent frequent switching between unstable service levels.

10. The method according to claim 1, characterized in that, The method also includes an illusion suppression mechanism, which verifies the reliability of the AI ​​model output through the following steps: reverse processing the enhanced content generated by the AI ​​model to generate a reverse result; calculating the semantic similarity score between the reverse result and the original input data; when the semantic similarity score is lower than a preset threshold, marking the enhanced content as suspicious and triggering a downgrade process.

11. The method according to claim 1, characterized in that, The method also includes a cross-platform adaptation strategy, wherein: on the Android platform, the enhanced content is presented through accessibility services and an accessibility overlay; on the iOS platform, the enhanced content is presented using a differentiated degradation strategy based on different application scenarios.

12. The method according to claim 1, characterized in that, The method further includes managing at least one hardware module through a hardware abstraction layer, the hardware module including at least one of the following: electrochromic lens, adaptive optics lens, wavefront aberration sensor, eye-tracking camera, ambient light sensor, inertial measurement unit, and bone conduction module.

13. The method according to claim 1, characterized in that, The computing device is integrated into a pair of smart glasses, which includes a video see-through display system or an optical see-through display system.

14. The method according to claim 1, characterized in that, The AI ​​model in the processing engine is optimized for edge deployment through model quantization and / or knowledge distillation techniques. This edge deployment optimization utilizes neural network processing units or graphics processing units on computing devices to perform AI model inference.

15. The method according to claim 1, characterized in that, The method further includes: providing a scenario development toolkit, which includes scenario definition templates, model interface specifications, proxy development frameworks, and hardware module access guidelines; and providing standardized application programming interfaces (APIs) to enable third-party developers to integrate their custom cognitive augmentation scenarios and processing engines into the platform in a standardized manner, thereby forming an open cognitive augmentation ecosystem.

16. The method according to claim 1, characterized in that, Presenting the enhanced content to the user according to the pipeline mode includes: in selective presentation mode, merging the enhanced content with the unprocessed original content for display; and in global presentation mode, displaying all enhanced content to the user.

17. The method according to claim 3, characterized in that, In the selective transformation mode, the method further includes an enhancement area conflict resolution step, in which the display priority order of each area is determined according to scene priority and user preference when the enhancement areas of multiple concurrent scenes have spatial overlap.

18. The method according to claim 1, characterized in that, The method also includes a data sharing mechanism that allows input data and intermediate processing results to be shared between different cognitive enhancement scenarios to avoid redundant data collection and preprocessing.

19. The method according to claim 5, characterized in that, The multi-scenario resource scheduler also supports elastic scaling of resource scheduling. When a cognitive augmentation scenario is temporarily inactive, its resource quota can be temporarily borrowed by other active scenarios.

20. The method according to claim 3, characterized in that, In the selective transformation mode, an alpha blending technique is used at the boundary between the enhanced content and the unprocessed original content to achieve a smooth transition.

21. A method for real-time correction of a user's vision, implemented on a computing device, characterized in that, The method includes the following steps: Acquire personalized visual aberration data of the user, the data representing the optical defects of the user's eye; Capture image frames representing the external world from at least one camera; Based on the user's visual state parameters, the corresponding image transformation parameters are dynamically obtained, and based on the parameters and the user's personalized visual aberration data, the image transformation algorithm is applied to the image frame to generate a pre-distorted image frame, which aims to compensate for the optical defects of the user's eyes. The pre-distorted image frame is displayed on the screen in front of the user.

22. The method according to claim 21, characterized in that, The dynamic acquisition of the corresponding image transformation parameters includes: dynamically selecting or interpolating the corresponding point spread function parameters from a multi-pupil point spread function lookup table containing multiple point spread functions.

23. The method according to claim 21, characterized in that, The image transformation algorithm is a frequency domain deconvolution operation or a spatial domain pre-distortion operation, wherein the frequency domain deconvolution operation uses a Wiener filter or constrained least squares method to suppress noise amplification.

24. The method according to claim 22, characterized in that, The multi-pupil point spread function lookup table contains point spread functions corresponding to multiple discrete pupil sizes ranging from 2 mm to 8 mm. When the real-time monitored user pupil size is between two discrete values, the corresponding point spread function parameters are obtained by interpolation calculation.

25. The method according to claim 21, characterized in that, The method further includes a continuous vision assessment step for dynamically updating the user's personalized visual aberration data through active measurement and / or passive monitoring; wherein the active measurement is achieved through an integrated wavefront aberration sensor, and the passive monitoring is achieved by analyzing user eye movement data captured by an eye-tracking camera, the eye movement data including at least one of blink frequency, saccade pattern, and pupillary response.

26. The method according to claim 21, characterized in that, The method further includes an active vision protection step, which adjusts the light intensity, color temperature, or spectral distribution displayed to the user through a software algorithm and / or a hardware module; wherein the protection step includes at least one of the following: strong light protection, blue light filtering, welding strong light protection, anti-glare for night driving, and ultraviolet protection.

27. The method according to claim 21, characterized in that, The method also includes a visual fatigue monitoring step, which determines the degree of visual fatigue by monitoring the user's blinking frequency. When the blinking frequency is lower than a preset threshold, a visual rest reminder is automatically triggered.

28. The method according to claim 21, characterized in that, The method further includes: controlling an adaptive optics lens through a hardware abstraction layer to provide basic focal length adjustment; and the image transformation algorithm performing fine-grained correction for higher-order aberrations on this basis.

29. The method according to claim 21, characterized in that, The method further includes a step for resolving convergence-accommodation conflict, which is achieved by controlling a zoom display, a light field display, or a holographic display to dynamically adjust the display focal plane according to the user's depth of gaze.

30. The method according to claim 21, characterized in that, The method also includes a binocular parallax correction step, which compensates for the parallax of the image frames for the left and right eyes respectively based on the physical positional deviation between the camera and the user's pupil.

31. The method according to claim 21, characterized in that, The method also includes a progressive adaptation mode that starts with a lower correction intensity when the user first uses it and gradually increases the correction intensity to a full correction level over a preset adaptation period.

32. The method according to claim 21, characterized in that, The personalized visual aberration data covers the full range from simple refractive prescription data to complete wavefront aberration data, covering at least one of myopia, hyperopia, astigmatism, and higher-order aberrations, thereby enabling the method to correct a full range of refractive errors.

33. The method according to claim 21, characterized in that, When the method is deployed as a cognitive enhancement scenario in a cognitive enhancement platform with a scenario registry, the corresponding scenario definition in the scenario registry registers the method as a vision correction scenario and specifies the functional modules, image transformation algorithms, and global transformation pipeline modes required for vision correction.

34. The method according to claim 21, characterized in that, The method further includes: dynamically updating the user's personalized visual aberration data through a continuous vision assessment step, wherein the continuous vision assessment is achieved through active measurement and / or passive monitoring.

35. The method according to claim 21, characterized in that, The user's visual state parameters include the user's pupil size, which is monitored in real time.

36. A method for real-time translation of content implemented on a computing device, characterized in that, The method includes the following steps: Obtain the content to be translated from an input source; Context-aware translation is performed using an AI translation model to generate a translation, wherein the context includes contextual information understood from the scene; The translation is presented to the user in a way that associates it with the original content space.

37. The method according to claim 36, characterized in that, Presenting the translation to the user in a way that is associated with the original content space includes: rendering and overlaying the translation on top of the original content in a way that preserves the layout and font style of the original content through a layout replication module.

38. The method according to claim 37, characterized in that, The typesetting replication module identifies text bounding boxes through a convolutional neural network, performs perspective transformation using inertial measurement unit data, and renders the translation after removing the original text using image restoration technology.

39. The method according to claim 36, characterized in that, The input source is the UI element tree or document object model of the operating system, thereby achieving text extraction with zero optical character recognition; and the method also supports multiple presentation modes, including at least one of one-click perspective mode, split-screen comparison mode, overlay mode and new file mode.

40. The method according to claim 36, characterized in that, The method also includes a real-time oral dialogue translation process, which sequentially executes speech activity detection, speech-to-text, translation, and text-to-speech through a streaming pipeline to achieve a real-time translation experience similar to simultaneous interpretation.

41. The method according to claim 36, characterized in that, The AI ​​translation model is a large-scale language model, and the context-aware translation includes at least one of the following capabilities: ambiguity resolution based on scene context, terminology consistency maintenance based on full-text context, and cultural adaptation based on target language cultural norms.

42. The method according to claim 36, characterized in that, The method also includes a spatial anchoring step, which uses the three-dimensional coordinate system of the augmented reality framework to anchor the rendering position of the translation to a fixed spatial position in the physical world, so that the translation maintains a spatial correspondence with the original text when the user's head moves.

43. The method according to claim 40, characterized in that, The real-time oral dialogue translation process also includes a speaker separation step, which can distinguish the voices of multiple speakers and translate them separately to support multi-person dialogue scenarios.

44. The method according to claim 36, characterized in that, The overall processing latency of the method is controlled within a preset real-time threshold, wherein the text detection, translation, typesetting replication and rendering stages are executed in parallel through pipeline to minimize end-to-end latency.

45. The method according to claim 36, characterized in that, When the method is deployed as a cognitive augmentation scenario in a cognitive augmentation platform with a scenario registry, the corresponding scenario definition in the scenario registry registers the method as a real-time translation scenario and specifies the functional modules, AI translation model, and selective transformation pipeline mode required for translation.

46. ​​The method according to claim 36, characterized in that, Presenting the translation to the user in a manner that is associated with the original content space includes rendering and presenting the translation in a way that preserves the layout and font style of the original content through a layout replication module.

47. The method according to claim 36, characterized in that, The method also includes a life-safety-level failure protection mechanism. When the system detects any serious fault that may affect user safety, it immediately triggers the highest priority security overlay event, forces the system to enter pure optical perspective mode and disables all digital rendering.

48. The method according to claim 36, characterized in that, The method also includes a privacy protection mechanism, wherein: the user's personalized data is encrypted and stored on the edge device and not transmitted to the cloud; the AI ​​model is upgraded through federated learning and differential privacy technology to protect user privacy.

49. A cognitive enhancement system, comprising: At least one input device, the input device including at least one of a camera, a microphone and / or a system interface, for acquiring data from the external world; A display screen is used to present enhanced content to the user; A processor; A memory that stores a scene registry and computer-executable instructions; The scenario registry stores the definition of at least one cognitive augmentation scenario, and each scenario definition includes a scenario identifier, functional modules, processing engine, and pipeline mode; The processing engine includes AI models and / or rule-based processing modules; When the instruction is executed by the processor, the system is configured to: respond to a request to activate a specific cognitive enhancement scenario; Load the corresponding functional modules and processing engine according to the scene definition in the scene registry and configure the pipeline mode; The functional module invokes the processing engine to process the input data and generate enhanced content. And to present the enhanced content to the user via the display screen.

50. The system according to claim 49, characterized in that, The system also includes at least one of the following hardware modules: electrochromic lens, adaptive optics lens, wavefront aberration sensor, eye-tracking camera, ambient light sensor, inertial measurement unit, and bone conduction module; and the system also includes a hardware abstraction layer that manages the hardware modules through a standardized interface.

51. The system according to claim 49, characterized in that, The system is integrated into a pair of smart glasses, which includes a video see-through display system or an optical see-through display system.

52. The system according to claim 49, characterized in that, The system also includes a multi-scenario resource scheduler, which dynamically allocates the processor's computing resources to multiple concurrently running cognitive augmentation scenarios based on the priorities and resource configuration files defined in the scenario registry.

53. The system according to claim 49, characterized in that, The system also includes an event bus, through which the functional modules communicate asynchronously. The event bus is used to transmit scene lifecycle events and user interaction events.

54. The system according to claim 49, characterized in that, The processor includes a neural network processing unit or a graphics processing unit for performing AI model inference, wherein the AI ​​model is optimized for edge deployment through model quantization and / or knowledge distillation techniques.

55. The system according to claim 49, characterized in that, The system also includes a security module configured to trigger a life safety-level failure protection mechanism when a serious fault affecting user safety is detected, forcing the system to enter a pure optical perspective mode and disabling all digital rendering.

56. The system according to claim 49, characterized in that, The system is also configured to execute a speculative cascading workflow, including: rapidly generating and presenting a draft version of enhanced content on the device using a lightweight AI model; simultaneously sending input data to the cloud to generate a refined version of enhanced content using a high-precision AI model; and seamlessly replacing the draft version with the refined version when the semantic consistency between the refined version and the draft version is lower than a preset threshold.

57. The system according to claim 49, characterized in that, The system also includes a semantic caching module, which stores historical processing results in a vector database. When the similarity between a newly input semantic vector and an entry in the cache exceeds a preset threshold, the cached processing result is returned directly to reduce latency.

58. The system according to claim 49, characterized in that, The system also includes an illusion suppression module, which is used to reverse process the enhanced content generated by the AI ​​model back to the original domain and calculate the similarity between the reverse processing result and the original input. When the similarity is lower than a preset threshold, an alarm or downgrade processing is triggered.

59. The system according to claim 49, characterized in that, The system also includes a five-level fallback chain module and a circuit breaker module. The five-level fallback chain module is configured to automatically downgrade the cognitive enhancement function from the current service level to a preset lower service level when a performance degradation or error is detected. The circuit breaker module has three states: closed, open, and half-open, to prevent frequent switching between unstable service levels.

60. The system according to claim 49, characterized in that, The system also includes a model registry and a model abstraction layer. The model registry is used to store metadata of multiple available AI models, including model identifier, model capability type, model location, and model status. The model abstraction layer is used to dynamically load and replace AI models through a unified capability interface. The dynamic replacement is achieved through the following steps: decision replacement, initialization of the new model, routing of new requests to the new model, waiting for the old model to complete the accepted request, and unloading the old model.

61. The system according to claim 49, characterized in that, The system also includes a cross-platform adaptation module, wherein: on the Android platform, the enhanced content is presented through accessibility services and an accessibility overlay; on the iOS platform, the enhanced content is presented using differentiated degradation strategies based on different application scenarios.

62. The system according to claim 49, characterized in that, When multiple cognitive augmentation scenarios run concurrently, the system is configured to: use the augmented content generated by the scenario configured in global transformation mode as the base frame, and overlay the augmented content generated by the scenario configured in selective transformation mode onto the base frame for composite display.

63. The system according to claim 49, characterized in that, The system also includes an AI model router, which dynamically selects the optimal AI model to process the input data based on the language, complexity, and current network status of the input content.

64. The system according to claim 49, characterized in that, The system also includes a privacy protection module configured to: encrypt and store users' personalized data on the edge device without transmitting it to the cloud; and upgrade the AI ​​model through federated learning and differential privacy technology to protect user privacy.

65. The system according to claim 49, characterized in that, The system also includes a scenario development toolkit and standardized application programming interfaces. The scenario development toolkit includes scenario definition templates, model interface specifications, proxy development frameworks, and hardware module access guidelines. The application programming interface (API) enables third-party developers to integrate their custom cognitive augmentation scenarios and processing engines into the system in a standardized manner, forming an open cognitive augmentation ecosystem.

66. A vision correction system, comprising: At least one camera is used to capture image frames representing the external world; A display screen used to show images to the user; A processor; A memory storing a user's personalized visual aberration data and computer-executable instructions; when the instructions are executed by the processor, the system is configured to: dynamically acquire corresponding image transformation parameters based on the user's visual state parameters; and apply an image transformation algorithm to the image frame based on the image transformation parameters and the user's personalized visual aberration data to generate a pre-distorted image frame; And display the pre-distorted image frame on the display screen.

67. The vision correction system according to claim 66, characterized in that, The memory also stores a multi-pupil point spread function lookup table, which contains point spread functions corresponding to multiple discrete pupil sizes ranging from 2 mm to 8 mm; the system also includes an eye-tracking camera for real-time monitoring of the user's pupil size. When the pupil size is between two discrete values, the processor obtains the corresponding point spread function parameters through interpolation calculation.

68. The vision correction system according to claim 66, characterized in that, The system also includes a continuous vision assessment module configured to: actively measure via a wavefront aberration sensor, and / or passively monitor by analyzing user eye movement data captured by an eye-tracking camera, to dynamically update the user's personalized visual aberration data.

69. The vision correction system according to claim 66, characterized in that, The system also includes an adaptive optics lens, which is controlled through a hardware abstraction layer to provide basic focal length adjustment; the image transformation algorithm performs fine high-order aberration correction on top of the basic focal length adjustment provided by the adaptive optics lens.

70. A real-time translation system, comprising: At least one input device is used to acquire the content to be translated; A display screen is used to present the translated content to the user; A processor; A memory storing an AI translation model and computer-executable instructions; when the instructions are executed by the processor, the system is configured to: acquire content to be translated from the input device; and perform context-aware translation using the AI ​​translation model to generate a translation, wherein the context includes contextual information understood from the scene; And present the translation to the user via the display screen in a manner that associates it with the original content space.

71. The real-time translation system according to claim 70, characterized in that, The system also includes a layout replication module, which is configured to: identify text bounding boxes through a convolutional neural network, perform perspective transformation through inertial measurement unit data, and render the translation after removing the original text through image restoration technology, thereby presenting the translation in a way that preserves the original layout and font style.

72. The real-time translation system according to claim 70, characterized in that, The system also includes a real-time oral dialogue translation module, which is configured to sequentially perform speech activity detection, speech-to-text, translation, and text-to-speech via a streaming pipeline to achieve a real-time translation experience similar to simultaneous interpretation.

73. The real-time translation system according to claim 70, characterized in that, The input device includes the UI element tree interface of the operating system or the document object model interface, thereby enabling text extraction with zero optical character recognition.

74. A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method of any one of claims 1 to 48.

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