An ai-accelerated sensor-based system for real-time toddler & child developmental metrics & universal readiness indexes
Patent Information
- Application Number
- PCT/IB2025/053874
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2025-02-20
- Filing Date
- 2025-04-14
- Publication Date
- 2026-08-27
Smart Images
Figure IB2025053874_27082026_PF_FP_ABST
Abstract
Description
AN Al- ACCELERATED SENSOR-BASED SYSTEM FOR REAL-TIME TODDLER & CHILD DEVELOPMENTAL METRICS & UNIVERSAL READINESS INDEXES TECHNICAL FIELD
[0001] The present invention relates to computerized child development assessment systems that employ concurrency-based data capture. More particularly, it enables real-time measurement of child developmental parameters — especially autism-related abilities (PAA) and general self-sufficiency (PC SSI)BACKGROUND OF THE INVENTION
[0002] Child development assessment plays a pivotal role in guiding parents, educators, and clinicians toward timely interventions and improved outcomes. Yet, most existing approaches remain segmented, relying on multiple, disparate tools — some specialized for autism screening, others focusing on broader developmental attributes. This fragmentation often results in inconsistent data collection, delayed feedback, and limited real-time insights. Consequently, caregivers and professionals may struggle to form a unified perspective on a child’s status across different domains, such as autismspecific skills (speech, social interaction) and general self-sufficiency (motor coordination, adaptive abilities).
[0003] While certain screening methods do integrate parent-reported questionnaires and direct observations, they typically lack the technical sophistication for comprehensive data validation. For example, a parent’s perception of a child’s clear speech may not always align with actual speech patterns, which could be more accurately captured and analyzed through synchronized audio or video recordings. Similarly, achild’ s motor abilities or capacity for independent tasks are often measured through static checklists rather than continuous, sensor-driven inputs. As a result, real-time anomalies or developmental milestones may go unnoticed, delaying effective responses.
[0004] Recent advancements in artificial intelligence (Al) and machine learning (ML) have paved the way for more dynamic and objective assessment frameworks. AI-driven anomaly detection allows for parallel processing of multiple data streams, including audio, video, and motion data. However, most existing child development solutions do not leverage concurrency -based sensor capture alongside accelerator-based analytics. Instead, they rely on sequential methods that are prone to latency, data mismatch, and limited scalability. Moreover, few systems address both autism-specific assessments and broader self-sufficiency evaluations under a single architecture, leaving parents and practitioners to juggle multiple tools with no universal numeric reference.
[0005] The present invention addresses these gaps by providing a concurrency-enabled, accelerator-based system and method for real-time child development scoring across different domains. The present invention framework culminates in two primary numeric scores on a 0-1000 scale: the Pinnacle Autism AbilityScore (PAA) for autism-focused assessments and the Pinnacle Child Self-Sufficiency Index (PCSSI) for holistic developmental independence. By unifying these assessments within one system, the invention not only enhances the accuracy and speed of data analysis but also establishes a universal method to track and benchmark a child’s developmental journey. This coordinated approach overcomes the limitations of siloed tools and allows users to make more informed decisions for individualized support, interventions, or further clinical evaluation.OBJECT OF THE INVENTION
[0006] The primary object of the present invention is to provide a unified system and method that concurrently captures sensor data (audio, video, motion) and synchronizes it with structured questionnaires, thereby enabling real-time measurement of both autism-specific development (PAA) and general self-sufficiency (PCSSI).
[0007] Another object of the invention is to leverage accelerator-based Al processing (e.g., GPU or TPU) for rapid anomaly detection, reducing latency and improving the accuracy of analyzing large volumes of audio-visual-motor inputs compared to conventional CPU-only methods.
[0008] Yet another object of the invention is to facilitate non-therapeutic data verification, wherein flagged inconsistencies between reported responses and sensor-derived evidence are routed to a qualified reviewer solely to confirm or amend data, without making any medical diagnoses or offering treatment.
[0009] Yet another object of the invention is to deliver universally interpretable numeric scores — including the Pinnacle Autism AbilityScore (PAA) and the Pinnacle Child Self-Sufficiency Index (PCSSI) — each on a 0-1000 scale, providing stakeholders (parents, educators, clinicians) with a simple, standardized reference for tracking developmental progress.SUMMARY OF THE INVENTION
[0010] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the followingdescription and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
[0011] According to an embodiment of the present invention, a concurrencymanaged, accelerator-based system for real-time child development assessment, comprising a concurrency manager implemented at an operating system or interruptcontroller layer, a multi -threaded Al validation engine communicatively coupled to said concurrency manager, a non-therapeutic verification console, configured to receive said flagged discrepancies for review by a qualified individual without providing any medical diagnosis or therapy; and a scoring module arranged to compute at least one numeric metric on a 0-1000 scale wherein the concurrency manager reduces latency by distributing sensor data processing across accelerator-based threads, ensuring near realtime alignment of subjective questionnaire inputs with objective sensor signals.
[0012] According to an embodiment of the present invention, further includes a scoring module that calculates readiness sub-scores covering one or more of speech articulation, motor coordination, cognitive responsiveness, or behavioral indicators, each sub-score derived from validated data in the same concurrency -driven pipeline without requiring additional user input.
[0013] According to an embodiment of the present invention, additionally provides a non-therapeutic verification console configured to (a) present flaggedinconsistencies in the form of synchronized video segments, audio clips, or motion graphs aligned with the corresponding questionnaire item, and (b) lock each data entry upon reviewer confirmation or correction, thus ensuring a stable final dataset for subsequent score computation.
[0014] According to an embodiment of the present invention, there is provided a computer-implemented method for generating a real-time developmental score using concurrency-managed, accelerator-based processing, the method comprising (a) presenting structured developmental questions on a mobile device, each question triggering concurrent sensor capture of audio, video, and / or motion data via an operating system- or driver-level concurrency manager, (b) preprocessing captured sensor data on the device to remove noise or compress large media files prior to transmitting said data to an Al validation engine, (c) executing feature extraction and anomaly detection on parallel accelerator threads (GPU, TPU, or equivalent), thereby identifying discrepancies between questionnaire responses and sensor-derived features, (d) flagging said discrepancies for non-therapeutic verification by a qualified reviewer who confirms or amends the flagged responses without performing diagnostic or therapeutic steps, and (e) computing at least one numeric score on a 0-1000 scale, selected from a Pinnacle Autism AbilityScore (PAA) or a Pinnacle Child Self-Sufficiency Index (PCSSI), based on validated data.
[0015] According to an embodiment of the present invention, there is provided a non-transitory computer-readable medium storing instructions that, when executed by one or more processors operatively coupled to a system-level concurrency manager, an accelerator device, and at least one sensor interface, perform the steps of any one of thepreceding method embodiments, thereby facilitating concurrent data capture, parallel AI-based anomaly detection, non-therapeutic review of discrepancies, and final numeric scoring in a unified real-time pipeline.
[0016] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The objects, features and advantages will occur to those skilled in the art from the following description of the preferred embodiment and the accompanying drawings which:
[0018] FIG.l illustrates an overall system architecture, detailing concurrency manager for sensor data and parent questionnaire integration, according to the present invention.
[0019] FIG.2 illustrates a flow diagram illustrating real-time capture, parallel Al processing, and flagged-item routing for verification, according to the present invention.
[0020] FIG.3 illustrates a block diagram of the GPU / TPU-based anomaly detection pipeline, highlighting concurrency and memory management., according to the present invention.
[0021] Although the specific features of the present invention are shown in some drawings and not in others. This is done for convenience only as each feature may be combined with any or all of the other features in accordance with the present invention.DETAILED DESCRIPTION
[0022] In the following detailed description, a reference is made to the accompanying drawings that form a part hereof, and in which the specific embodiments that may be practised are shown by way of illustration. These embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments and it is to be understood that other changes may be made without departing from the scope of the embodiments. The following detailed description is therefore not to be taken in a limiting sense. The various embodiments of the present invention relate an AI-accelerated sensor-based system for real-time toddler & child developmental metrics & universal readiness indexes.
[0023] According to an embodiment of the present invention, a concurrencymanaged, accelerator-based system for real-time child development assessment is provided. The system comprises a concurrency manager implemented at an operating system or interrupt-controller layer, configured to (i) synchronously trigger at least one camera, microphone, and motion sensor in response to each question of a structured developmental questionnaire presented on a mobile device and (ii) allocate incomingsensor data streams to multiple processing resources in real time. The concurrency manager adaptively adjusts priority among audio, video, and motion inputs based on realtime confidence scores, thereby ensuring optimized resource utilization. Additionally, the system includes a multi -threaded Al validation engine communicatively coupled to the concurrency manager, the engine comprising a feature extraction module executing on a graphics processing unit (GPU), tensor processing unit (TPU), or comparable accelerator, adapted to perform speech analysis, motion tracking, and / or facial feature detection. An anomaly detection submodule is operable to identify mismatches between sensor-derived features and caregiver responses, thereby generating flagged discrepancies. A model selection controller is configured to dynamically switch among convolutional neural networks (CNNs), recurrent neural networks (RNNs), hybrid Transformers, or spatiotemporal deep learning models based on real-time sensor input confidence thresholds, optimizing multi-modal feature extraction and improving anomaly detection in near real time.
[0024] According to an embodiment of the present invention, the concurrency manager is integrated within the operating system and / or interrupt controller to coordinate the real-time alignment of sensor triggers with caregiver responses. By interfacing with low-level scheduling logic or device interrupts, this concurrency manager detects question-specific events and promptly activates the camera, microphone, and motion sensor in precise synchronization. It then dispatches the resulting data streams to multiple processing resources — for example GPU or TPU threads — using OS-level resource allocation. This design minimizes latency, guaranteesthat subjective responses remain paired with objective sensor data, and prevents bottlenecks, thus ensuring reliable, near real-time development assessment.
[0025] According to an exemplary embodiment of the present invention, additional sensors can be integrated into a concurrency -based data capture framework to enhance the analysis of a child’s developmental or behavioral patterns, including eyetracking or gaze detection sensors that evaluate visual attention, wearable physiological sensors such as heart rate monitors or galvanic skin response devices that provide insights into stress, EEG headbands for brainwave pattern tracking, environmental sensors (light meters or sound level meters) that gauge contextual factors, infrared or depth cameras capable of detailed 3D motion tracking, smart wearables (e.g., watches or pressuresensing footwear) to assess gait and balance, and camera-based facial expression recognition modules that detect micro-expressions or emotional states, each chosen according to the practical, clinical, and technical feasibility of real-time data collection for specific child profiles.
[0026] According to an embodiment of the present invention, a non-therapeutic verification console is provided, which does not diagnose medical conditions but provides Al-assisted developmental observations that can be verified by caregivers, educators, or clinicians for non-medical assessment purposes. The console is configured to receive flagged discrepancies for review by a qualified individual without providing any medical diagnosis or therapy. A scoring module is arranged to compute at least one numeric metric on a 0-1000 scale, said metric selected from the Pinnacle Autism AbilityScore (PAA), Pinnacle Toddler / Child Self-Sufficiency Index (PCSSI), Pinnacle Toddler / Child Mainstream Readiness Index (PTCMSRI), Pinnacle Toddler / Child SchoolReadiness Index (PTCSRI), Pinnacle Toddler / Child Speech Readiness Index (PTCSTRI), Pinnacle Toddler / Child Motor Readiness Index (PTCMRI), Pinnacle Toddler / Child Behaviour Readiness Index (PTCBRI), or Pinnacle Toddler / Child Cognitive Readiness Index (PTCCRI). The concurrency manager reduces latency by distributing sensor data processing across accelerator-based threads, ensuring near realtime alignment of subjective questionnaire inputs with objective sensor signals. A reinforcement learning-based calibration optionally refines anomaly detection thresholds over time using caregiver feedback or expert validation, supporting deployment on any computational architecture capable of executing multi -threaded Al inference, including CPUs, GPUs, TPUs, FPGAs, mobile edge processors, cloud-based Al services, or neuromorphic computing systems.
[0027] According to an embodiment of the present invention, the Pinnacle Autism AbilityScore (PAA) is a standardized, Al-driven metric designed to provide a comprehensive assessment of a child's developmental abilities across multiple domains. The PAA integrates multi-modal sensor inputs, including speech, motor, cognitive, and social-emotional data, to generate a holistic, non-diagnostic measure of a child's developmental status. The score is computed dynamically through Al-driven sensor fusion, real-time data validation, and reinforcement learning-based calibration, ensuring continuous refinement based on historical trends and caregiver feedback. The concurrency manager reduces latency by distributing sensor data processing across accelerator-based threads, ensuring near real-time alignment of caregiver questionnaire inputs with objective sensor-derived developmental signals. The PAA provides auniversal, adaptive developmental tracking index that supports longitudinal monitoring and data-driven decision-making for personalized intervention planning.
[0028] According to an embodiment of the present invention, the Pinnacle Toddler / Child Self-Sufficiency Index (PCSSI) is an Al-driven metric that quantifies a child’s ability to independently perform daily tasks, regulate emotions, and function autonomously in structured and unstructured environments. The PCSSI integrates motion tracking, speech recognition, and behavioral analysis to evaluate independence in performing tasks such as self-care, problem-solving, social interactions, and executive functioning. The Al validation engine, working in conjunction with the concurrency manager, ensures parallel processing of multi-modal data, minimizing latency and maximizing real-time accuracy. The PCSSI score dynamically adjusts through reinforcement learning-based calibration, incorporating caregiver feedback and historical trends to refine the assessment over time.
[0029] According to an embodiment of the present invention, the Pinnacle Toddler / Child Mainstream Readiness Index (PTCMSRI) is an Al-powered metric designed to assess a child’s readiness for integration into mainstream social and educational environments. This index evaluates key developmental areas such as cognitive adaptability, speech and communication skills, social interaction, and behavioral self-regulation. The Al validation engine applies real-time sensor fusion techniques, using synchronized video, motion, and speech inputs to quantify a child’s ability to engage in group settings, follow structured activities, and regulate emotions in dynamic environments. The concurrency manager allocates processing tasks acrossmultiple accelerator-based threads, reducing computational latency and ensuring synchronized analysis of caregiver-reported responses and sensor-derived indicators.
[0030] According to an embodiment of the present invention, the Pinnacle Toddler / Child School Readiness Index (PTCSRI) is an Al-driven, non-diagnostic metric designed to measure a child’s preparedness for early education. The PTCSRI evaluates cognitive, linguistic, social-emotional, and behavioral readiness, ensuring a quantifiable measure of a child's ability to adapt to structured learning environments. The Al validation engine processes real-time interactions, sensor-based inputs, and historical caregiver feedback to derive an adaptive school readiness score. The concurrency manager synchronizes questionnaire prompts with real-time sensor data, ensuring low-latency fusion of multiple developmental indicators. The PTCSRI dynamically refines its scoring using reinforcement learning, continuously adapting based on cumulative learning milestones and observed developmental trajectories.
[0031] According to an embodiment of the present invention, the Pinnacle Toddler / Child Speech Readiness Index (PTCSTRI) is an Al-powered metric that quantifies a child's preparedness for age-appropriate speech and language development. This index measures phonemic awareness, expressive and receptive language skills, articulation clarity, fluency, and non-verbal communication. The concurrency manager facilitates real-time processing of multi-modal speech data by assigning speech recognition, phonetic analysis, and articulation assessment tasks to dedicated Al accelerator threads (e.g., GPU, TPU, or neuromorphic processors). The Al validation engine dynamically refines speech readiness scores using reinforcement learning,leveraging caregiver feedback and historical performance patterns to enhance accuracy and adaptability.
[0032] According to an embodiment of the present invention, the Pinnacle Toddler / Child Motor Readiness Index (PTCMRI) is an Al-driven, multi-modal assessment tool designed to measure a child's fine and gross motor development. This index evaluates movement coordination, spatial awareness, motor planning, and task execution speed. The concurrency manager distributes real-time motion tracking and sensor-based analysis across accelerator-based processing threads, ensuring low-latency evaluation of motor responses. The Al validation engine utilizes motion data from depth cameras, wearable sensors, and gesture recognition modules to dynamically refine the motor readiness score. Through reinforcement learning, the PTCMRI adjusts scoring parameters over time based on historical performance, task complexity, and caregiver feedback.
[0033] According to an embodiment of the present invention, the Pinnacle Toddler / Child Behavior Readiness Index (PTCBRI) is an Al-powered, non-diagnostic metric that quantifies a child’s behavioral adaptability, emotional regulation, and social readiness. The index evaluates impulse control, emotional resilience, social interaction, and self-regulation. The concurrency manager ensures real-time processing of facial expressions, body language, and social cues using synchronized video, motion tracking, and speech analysis. The Al validation engine applies deep learning-based behavioral analysis, leveraging reinforcement learning to refine behavioral scoring patterns over time. The PTCBRI enables adaptive, data-driven tracking of social-emotionaldevelopment, facilitating personalized intervention strategies based on objective assessment trends.
[0034] According to an embodiment of the present invention, the Pinnacle Toddler / Child Cognitive Readiness Index (PTCCRI) is an Al-driven metric that quantifies a child's cognitive preparedness for problem-solving, memory retention, and learning adaptability. This index measures executive function, attention control, reasoning abilities, and abstract thinking. The concurrency manager assigns cognitive performance tasks (e.g., memory recall, logical sequencing, and problem-solving exercises) to parallel Al processing threads, ensuring low-latency assessment. The Al validation engine applies adaptive learning models and real-time pattern recognition, refining cognitive readiness scores based on historical learning milestones, environmental factors, and developmental trends. The PTCCRI enables personalized cognitive tracking, offering non-diagnostic, data-driven insights into a child's learning progression.
[0035] According to an embodiment of the present invention, the scoring module further calculates readiness sub-scores covering one or more of speech articulation, motor coordination, cognitive responsiveness, or behavioral indicators, each sub-score derived from validated data in the same concurrency -driven pipeline without requiring additional user input. Multi-modal fusion weights are updated via a probabilistic confidence scoring mechanism in real time to ensure an adaptive and responsive evaluation framework.
[0036] According to an embodiment of the present invention, the non-therapeutic verification console is configured to (a) present flagged inconsistencies in the form of synchronized video segments, audio clips, or motion graphs aligned with thecorresponding questionnaire item, and (b) lock each data entry upon reviewer confirmation or correction, thereby ensuring a stable final dataset for subsequent score computation. The console is further interoperable with external developmental tracking tools via standardized data exchange protocols such as FHIR, HL7, or an equivalent interoperability framework.
[0037] According to an embodiment of the present invention, the Al validation engine executes concurrent kernels on GPU or TPU resources, each kernel assigned to a distinct data modality selected from speech analysis, motor coordination checks, or facial-expression recognition. The concurrent processing architecture achieves a processing-latency reduction of at least 30% relative to a sequential, CPU-only approach, ensuring real-time developmental assessment.
[0038] According to an embodiment of the present invention, the multi -threaded Al validation engine integrates concurrent multi-modal Al fusion of speech, motion, and physiological data using one or more machine learning architectures selected from CNNs, RNNs, hybrid Transformers, or spatiotemporal deep learning models, thereby improving real-time anomaly detection and verification mechanisms. The Al engine dynamically adapts its feature extraction methodologies based on real-time confidence scores derived from the sensor inputs. The validation engine further configured to adaptively switch between convolutional neural networks (CNNs) for static image processing, recurrent neural networks (RNNs) for temporal speech analysis, and hybrid transformers for multi-modal feature fusion, based on real-time sensor input confidence scores, ensuring optimal feature extraction and pattern recognition from audio, video, motion, or mixed sensory inputs.
[0039] According to an embodiment of the present invention, the validation engine prioritizes model selection based on real-time input confidence thresholds: CNNs for high-confidence static image processing (>85% clarity score), RNNs for time-series speech data (80% audio clarity), and hybrid transformers for multi-modal fusion (minimum combined score of 75%).
[0040] According to an exemplary embodiment of the present invention, the system architecture encompasses a smartphone application that presents over 510 questions spanning speech, behavior, motor, cognitive, and environmental domains, featuring a concurrency manager that schedules sensor triggers (camera, microphone, accelerometer) in real time as parents answer, and a local preprocessing step for noise reduction, motion stabilization, and video compression. An Al validation engine receives parent responses and sensor outputs in parallel streams, where a multi -threaded ML module performs feature extraction (e.g., speech clarity, movement fluidity) using GPU-accelerated routines, compares these features with parent answers to generate anomaly or confidence scores, and routes questionable items via a discrepancy router to a therapist collaboration console. The console displays flagged data (short videos, audio clips, motion graphs) so that a qualified reviewer can confirm whether the parent’s response or the Al’s analysis is correct, without diagnosing or prescribing. A scoring and index computation module aggregates validated data into relevant skills, computes the Pinnacle Autism Ability Score (PAA) on a 0-1000 scale, and optionally generates additional readiness indices (Speech, Motor, Study / IQ, etc.), also on a 0-1000 scale. This unified approach addresses the lack of a universal metric by automatically merging all validated data into a single numeric reference for immediate comparison across time and clinicalsettings, leveraging concurrency scheduling for near real-time data capture and feedback. Internal benchmarking indicates a 30-40% latency reduction for processing flagged items due to GPU acceleration. Because no direct medical intervention is performed, the numeric PAA and related indices serve as reference metrics, analogous to blood pressure or HbAlC, rather than diagnostic or therapeutic tools.
[0041] According to an another exemplary embodiment of the present invention, the system first collects child-specific data through structured assessments comprising 508 developmental questions that map to 333 skills grouped under 74 abilities, along with biometric and behavioral data obtained in real time from smartphone applications, loT sensors, and motion trackers for parameters such as motor movements or speech patterns. Demographic factors, including age and gender, are also recorded and compared against normative references. A Skill Index (60%) is computed by applying weighted impact factors to each skill, resulting in a 0-100 range, while an Ability Index (40%) clusters these skills into broader domains — cognitive, emotional, adaptive — and similarly produces a 0-100 value through hierarchical weighting. An age-gender adjustment factor then refines the child’s actual performance by dividing it by the expected ability score for that demographic, thereby accounting for individual variations. This yields a final Pinnacle Child Self-Sufficiency Index (PCSSI) using the formula: PCSSI = [(Skill Index x 0.6) + (Ability Index x 0.4)] x Adjustment Factor, and the PCSSI is recalculated in near real time upon arrival of new data points, such as updated questionnaires or sensor readings. The resulting score is mapped to one of ten self-sufficiency levels ranging from Critical to Thriving, and the system provides personalized recommendations and dashboards for progress tracking, as well as potentialintegration with external systems like health records or school platforms to enable comprehensive case management.
[0042] According to an another embodiment of the present invention as illustrated in Fig 3, a computer-implemented method for generating a real-time developmental score using concurrency-managed, accelerator-based processing is provided. The method comprises (a) presenting structured developmental questions on a mobile device, each question triggering concurrent sensor capture of audio, video, and / or motion data via an operating system- or driver-level concurrency manager, (b) preprocessing captured sensor data on the device to remove noise or compress large media files prior to transmitting said data to an Al validation engine, (c) executing feature extraction and anomaly detection on parallel accelerator threads (GPU, TPU, or equivalent), thereby identifying discrepancies between questionnaire responses and sensor-derived features, (d) flagging said discrepancies for non-therapeutic verification by a qualified reviewer who confirms or amends the flagged responses without performing diagnostic or therapeutic steps, and (e) computing at least one numeric score on a 0-1000 scale, selected from PAA, PCSSI, PTCMSRI, PTCSRI, PTCSTRI, PTCMRI, PTCBRI, or PTCCRI. The concurrency manager dynamically assigns processing tasks to appropriate Al models (CNN, RNN, hybrid Transformers, or spatiotemporal deep learning) according to real-time sensor confidence scores, minimizing latency while maintaining accuracy in anomaly detection.
[0043] According to an another embodiment of the present invention as illustrated in Fig 2, the process begins when a caregiver initiates the autism assessment on a smartphone and receives a structured question, after which the concurrency managerinstantly triggers real-time capture via camera, microphone, or accelerometer aligned with that specific query; any local pre-processing such as noise filtering or file compression may then be performed on-device before transmitting data to the Al Validation Engine running in parallel threads on GPU, TPU, or FPGA resources, where anomalies are identified by comparing parent responses to sensor-derived features; flagged discrepancies are subsequently forwarded for non-therapeutic verification so a qualified individual can confirm or adjust data entries without diagnosing or treating the child; finally, the validated responses are aggregated into relevant skill categories (e.g., speech, motor, behavior), and the Pinnacle Autism AbilityScore (PAA) and optional readiness sub-scores are computed to conclude the assessment flow, thereby generating a universal numeric metric.
[0044] According to an another embodiment of the present invention, the concurrency-managed, accelerator-based system for real-time child development assessment further comprising a reinforcement learning-based model calibration module that refines anomaly detection thresholds in real time based on caregiver-reported responses and sensor-derived outputs, wherein the Al validation engine acts as an agent whose state is the current anomaly detection thresholds, whose action is to adjust said thresholds according to confidence scores and caregiver validation, and whose reward function provides positive reinforcement when updated thresholds match expert-labeled ground truth feedback and negative reinforcement when incorrect anomalies are flagged, the system collecting multi-source inputs including caregiver feedback, sensor data, and Al-generated anomaly scores to continuously calibrate thresholds over multiple iterations, thereby minimizing false positives and false negatives.
[0045] According to an embodiment of the present invention, the operating system- or driver-level concurrency manager dynamically allocates processing tasks to multiple accelerator threads, allowing simultaneous speech analysis, motor coordination checks, and facial expression monitoring in parallel for minimal latency. Multi-modal sensor fusion is enabled using adaptive weighting based on environmental conditions or caregiver feedback. The final readiness score is refined via reinforcement learning if reviewer feedback indicates consistent false positives or negatives.
[0046] According to an embodiment of the present invention, flagged discrepancies are forwarded to a non-therapeutic verification console providing a structured interface that displays short video, audio, or motion data clips aligned with each relevant question, thereby enabling the reviewer to finalize data entries without providing any medical diagnosis. The console supports interoperability with external healthcare or educational systems via standardized APIs or data exchange protocols, ensuring role-based authentication and encryption for secure data handling.
[0047] According to an embodiment of the present invention, the Al validation engine incorporates an interoperability layer that enables data exchange with external developmental assessment tools, ensuring that any competing software integrating multisensor developmental analysis falls within the protection scope of the present invention. This interoperability layer is implemented using standardized data exchange protocols, including FHIR (Fast Healthcare Interoperability Resources) and HL7, allowing seamless integration with healthcare and educational systems. The system is designed with an API-driven architecture, utilizing RESTful and GraphQL APIs to enable secure third-party data exchange, allowing external applications to request, retrieve, and submitassessment data without platform restrictions. To ensure multi-format data compatibility, the system translates sensor data into JSON, XML, and CSV formats, allowing seamless operation across diverse platforms. The interoperability layer supports both cloud-based synchronization and edge deployment, ensuring real-time data sharing between local devices and centralized databases for latency-optimized processing. Secure access control mechanisms, including role-based authentication and encryption, restrict data exchange privileges to authorized systems, preventing unauthorized access or data manipulation. This interoperability ensures broad accessibility, platform independence, and compliance with global data exchange standards, enabling seamless integration into varied digital ecosystems. Furthermore, the interoperability layer supports real-time API synchronization with external developmental tools, including Speech Recognition APIs (Google Speech-to-Text), Motor Function Tracking (Microsoft Azure Kinect), and Neurophysiological Monitoring (Muse EEG SDK), enhancing the system’s scalability and multi-modal assessment capabilities.
[0048] According to an embodiment of the present invention, the concurrencymanaged, accelerator-based system integrates reinforcement learning-based model calibration that refines anomaly detection thresholds in real time based on caregiver-reported responses and sensor-derived outputs. The Al validation engine functions as an agent whose state is the current anomaly detection thresholds, whose action is to adjust said thresholds according to confidence scores and caregiver validation, and whose reward function reinforces accuracy in detecting true anomalies while minimizing false positives and negatives.
[0049] According to an embodiment of the present invention, the system includes a sensor array comprising at least one of audio microphones, video cameras, depth cameras, infrared-based motion sensors, thermal imaging devices, EEG-based brainwave tracking sensors, galvanic skin response (GSR) monitors, heart rate variability (HRV) sensors, or eye-tracking modules. An adaptive sensory fusion module dynamically assigns processing priority to each sensor input based on real-time confidence scores, ensuring efficient computational resource allocation and reducing latency in anomaly detection. The system assigns higher processing priority to sensor inputs with high confidence scores, ensuring that the most reliable data is used for real-time anomaly detection while low-confidence inputs are either filtered or assigned secondary processing threads. The adaptive sensory fusion module continuously updates its prioritization logic based on environmental changes, historical validation accuracy, and Al-driven feedback loops to enhance detection efficiency.
[0050] According to an embodiment of the present invention, the reinforcement learning-based model calibration module updates anomaly detection thresholds at adaptive intervals (initially every 50 sessions, reducing dynamically based on model confidence score stability exceeding 90%).
[0051] According to an embodiment of the present invention, external wearable devices, including smartwatches, pressure-sensitive footwear, and biometric wristbands, are integrated into the system to capture additional physiological or motor movement data. The concurrency manager's multi -threaded resource allocation enables real-time processing of both stationary and wearable sensor data, enhancing the developmental milestone assessment framework.
[0052] According to an embodiment of the present invention, the concurrencymanaged, accelerator-based processing solution is deployed across multiple hardware and software environments, ensuring universal accessibility and seamless integration. The scoring mechanism is universally deployable through a modular API architecture, incorporating RESTful and GraphQL endpoints to facilitate seamless interaction with third-party applications, web-based dashboards, and mobile platforms without proprietary restrictions. The system employs a cross-platform compatible scoring algorithm, developed using open-source frameworks such as TensorFlow, PyTorch, and ONNX, ensuring smooth execution across different operating systems, including Windows, Linux, and macOS, as well as cloud platforms such as AWS, Azure, and Google Cloud.
[0053] According to an embodiment of the present invention, to optimize computational efficiency, the system supports edge and cloud processing flexibility, enabling on-device execution for low-latency real-time assessments while leveraging cloud-based processing for computationally intensive tasks. The hardware-agnostic architecture allows deployment across CPUs, GPUs, TPUs, and neuromorphic processors, ensuring adaptability to a wide range of computing environments. The concurrency manager dynamically allocates processing tasks across accelerator-based threads, reducing latency and enhancing real-time performance. For data interoperability, the system complies with open data exchange standards such as FHIR and HL7, ensuring seamless communication with healthcare, education, and assistive technology platforms for real-time developmental tracking. The Al scoring mechanism further enhances deployment flexibility by dynamically allocating computations based on devicecapability, supporting on-device processing for real-time tasks with latencies under 50 milliseconds on ARM-based mobile processors, while also enabling cloud-based computation for large-scale analytics exceeding 100GB, facilitating scalability, platform independence, and comprehensive ecosystem integration. By ensuring platform-agnostic compatibility, the system enables large-scale accessibility, eliminates vendor lock-in, and promotes widespread adoption across diverse technological ecosystems, making it an ideal solution for real-time child developmental assessments.
[0054] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such as specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments.
[0055] It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modifications. However, all such modifications are deemed to be within the scope of the claims.
Claims
I / We Claims;1. A concurrency-managed, accelerator-based system for real-time child development assessment, comprising:a concurrency manager implemented at an operating system or interrupt-controller layer, configured to (i) synchronously trigger at least one camera, microphone, and motion sensor in response to each question of a structured developmental questionnaire presented on a mobile device, and (ii) allocate incoming sensor data streams to multiple processing resources in real time, wherein said concurrency manager adaptively adjusts priority among audio, video, and motion inputs based on real-time confidence scores; a multi-threaded Al validation engine communicatively coupled to said concurrency manager, the engine comprising (a) a feature extraction module executing on a graphics processing unit (GPU), tensor processing unit (TPU), or comparable accelerator, adapted to perform speech analysis, motion tracking, and / or facial feature detection, (b) an anomaly detection submodule operable to identify mismatches between sensor- derived features and caregiver responses, thereby generating flagged discrepancies, and (c) a model selection controller configured to dynamically switch among convolutional neural networks (CNNs), recurrent neural networks (RNNs), hybrid Transformers, or spatiotemporal deep learning models based on real-time sensor input confidence thresholds, thereby optimizing multi-modal feature extraction and improving anomaly detection in near real time;a non-therapeutic verification console, which does not diagnose medical conditions but provides Al-assisted developmental observations that can be verified by caregivers, educators, or clinicians for non-medical assessment purposes, configured to receive saidflagged discrepancies for review by a qualified individual without providing any medical diagnosis or therapy; anda scoring module arranged to compute at least one numeric metric on a 0-1000 scale, said metric selected from: (i) a Pinnacle Autism AbilityScore (PAA), (ii) a Pinnacle Toddler / Child Self-Sufficiency Index (PCSSI), (iii) a Pinnacle Toddler / Child Mainstream Readiness Index (PTCMSRI), (iv) a Pinnacle Toddler / Child School Readiness Index (PTCSRI), (v) a Pinnacle Toddler / Child Speech Readiness Index (PTCSTRI), (vi) a Pinnacle Toddler / Child Motor Readiness Index (PTCMRI), (vii) a Pinnacle Toddler / Child Behaviour Readiness Index (PTCBRI), or (viii) a Pinnacle Toddler / Child Cognitive Readiness Index (PTCCRI),wherein the concurrency manager reduces latency by distributing sensor data processing across accelerator-based threads, ensuring near real-time alignment of subjective questionnaire inputs with objective sensor signals, and wherein a reinforcement learning-based calibration optionally refines anomaly detection thresholds over time using caregiver feedback or expert validation, the system supporting deployment on any computational architecture capable of executing multi-threaded Al inference, including CPUs, GPUs, TPUs, FPGAs, mobile edge processors, cloud-based Al services, or neuromorphic computing systems.
2. The concurrency-managed, accelerator-based system for real-time child development assessment as claimed in claim 1, wherein the scoring module further calculates readiness subscores covering one or more of speech articulation, motor coordination, cognitive responsiveness, or behavioral indicators, each sub-score derived from validated data in the same concurrency-driven pipeline without requiring additional user input, and wherein multimodal fusion weights are updated via a probabilistic confidence scoring mechanism in real time.
3. The concurrency-managed, accelerator-based system for real-time child development assessment as claimed in claim 1, wherein the non-therapeutic verification console is configured to (a) present flagged inconsistencies in the form of synchronized video segments, audio clips, or motion graphs aligned with the corresponding questionnaire item, and (b) lock each data entry upon reviewer confirmation or correction, thereby ensuring a stable final dataset for subsequent score computation, wherein the console is further interoperable with external developmental tracking tools via standardized data exchange protocols (FHIR, HL7, or equivalent).
4. The concurrency-managed, accelerator-based system for real-time child development assessment as claimed in claim 1, wherein the Al validation engine executes concurrent kernels on GPU or TPU resources, each kernel assigned to a distinct data modality selected from speech analysis, motor coordination checks, or facial-expression recognition, thereby achieving a processing-latency reduction of at least 30% relative to a sequential, CPU-only approach.
5. The concurrency-managed, accelerator-based system for real-time child development assessment as claimed in claim 1 , wherein the multi-threaded Al validation engine integrates concurrent multi-modal Al fusion of speech, motion, and physiological data using one or more machine learning architectures selected from CNNs, RNNs, hybrid Transformers, or spatiotemporal deep learning models, thereby improving real-time anomaly detection and verification mechanisms.
6. A computer-implemented method for generating a real-time developmental score using concurrency-managed, accelerator-based processing, the method comprising:(a) presenting structured developmental questions on a mobile device, each question triggering concurrent sensor capture of audio, video, and / or motion data via an operating system- or driver-level concurrency manager;(b) preprocessing captured sensor data on the device to remove noise or compress large media files prior to transmitting said data to an Al validation engine;(c) executing feature extraction and anomaly detection on parallel accelerator threads (GPU, TPU, or equivalent), thereby identifying discrepancies between questionnaire responses and sensor-derived features;(d) flagging said discrepancies for non-therapeutic verification by a qualified reviewer who confirms or amends the flagged responses without performing diagnostic or therapeutic steps; and(e) computing at least one numeric score on a 0-1000 scale, selected from the group comprising: (i) the Pinnacle Autism AbilityScore (PAA), (ii) the Pinnacle Toddler / Child Self-Sufficiency Index (PCSSI), (iii) the Pinnacle Toddler / Child Mainstream Readiness Index (PTCMSRI), (iv) the Pinnacle Toddler / Child School Readiness Index (PTCSRI), (v) the Pinnacle Toddler / Child Speech Readiness Index (PTCSTRI), (vi) the Pinnacle Toddler / Child Motor Readiness Index (PTCMRI), (vii) the Pinnacle Toddler / Child Behaviour Readiness Index (PTCBRI), or (viii) the Pinnacle Toddler / Child Cognitive Readiness Index (PTCCRI),wherein the concurrency manager dynamically assigns processing tasks to appropriate Al models (CNN, RNN, hybrid Transformers, or spatiotemporal deep learning) according to realtime sensor confidence scores, thus minimizing latency while maintaining accuracy in anomaly detection, wherein the scoring mechanism remains independent of proprietary software or hardware by supporting open-source Al frameworks and an edge-cloud hybrid approach, and wherein the method facilitates large-scale accessibility and minimal latency for real-time child development assessment on any computational architecture capable of multithreaded Al inference.
7. The method as claimed in claim 6, wherein the operating system- or driver-level concurrency manager dynamically allocates processing tasks to multiple accelerator threads, allowing simultaneous speech analysis, motor coordination checks, and facial expression monitoring in parallel for minimal latency, further enabling multi-modal sensor fusion using adaptive weighting based on environmental conditions or caregiver feedback, and wherein the final readiness score is refined via reinforcement learning if reviewer feedback indicates consistent false positives or negatives.
8. The method as claimed in claim 6, wherein the flagged discrepancies are forwarded to a non-therapeutic verification console providing a structured interface that displays short video, audio, or motion data clips aligned with each relevant question, thereby enabling the reviewer to finalize data entries without providing any medical diagnosis, and wherein the console supports interoperability with external healthcare or educational systems via standardized APIs or data exchange protocols, ensuring role-based authentication and encryption for secure data handling.
9. The method as claimed in claim 6, wherein computing one or more Pinnacle Indexes selected from PAA, PCSSI, PTCMSRI, PTCSRI, PTCSTRI, PTCMRI, PTCBRI, or PTCCRI comprises aggregating multi-modal sensor data (audio, video, motion) in real time via a concurrency-managed accelerator-based architecture, preprocessing said data to remove noise or compress large media files, applying an Al-based feature extraction and anomaly detection process on parallel threads, flagging discrepancies for non-therapeutic verification, and refining at least one final 0-1000 readiness score using a reinforcement learning-based model calibration that dynamically adjusts thresholds if reviewer feedback indicates consistent false positives or negatives, thereby providing a universal, non -diagnostic measure of a child’s developmental status without offering any clinical diagnosis or therapeutic intervention.
10. A non -transitory computer-readable medium storing instructions that, when executed by one or more processors operatively coupled to a system-level concurrency manager, an accelerator device, and at least one sensor interface, perform the steps of any one of claims 6 through 9, thereby facilitating concurrent data capture, parallel Al-based anomaly detection, non-therapeutic review of discrepancies, and final numeric scoring in a unified real-time pipeline, wherein said instructions define multi-modal feature extraction architectures selected from CNNs, RNNs, spatiotemporal deep learning models, or hybrid Transformers.
11. The concurrency -managed, accelerator-based system for real-time child development assessment as claimed in claim 1 , wherein the multi-threaded Al validation engine is further configured to adaptively switch among CNNs for static image processing, RNNs for temporal speech analysis, and hybrid Transformers for multi-modal feature fusion based on real-time sensor input confidence scores, ensuring optimal feature extraction and pattern recognition from audio, video, motion, or mixed sensory inputs, wherein the adaptive switching mechanism is implemented through a multi-stage processing pipeline comprising data preprocessing to normalize and denoise sensor data, confidence scoring to assign probabilistic confidence levels to each input type, a model selection controller that dynamically determines which model to activate above specified confidence thresholds, and real-time adaptation that refines the selection strategy based on historical accuracy, caregiver feedback, and anomaly validation.
12. The concurrency-managed, accelerator-based system for real-time child development assessment as claimed in claim 1 , further comprising a reinforcement learning-based model calibration module that refines anomaly detection thresholds in real time based on caregiver-reported responses and sensor-derived outputs, wherein the Al validation engine acts as an agent whose state is the current anomaly detection thresholds, whose action is to adjust said thresholds according to confidence scores and caregiver validation, and whose reward functionprovides positive reinforcement when updated thresholds match expert-labeled ground truth feedback and negative reinforcement when incorrect anomalies are flagged, the system collecting multi-source inputs including caregiver feedback, sensor data, and Al-generated anomaly scores to continuously calibrate thresholds over multiple iterations, thereby minimizing false positives and false negatives.
13. The concurrency -managed, accelerator-based system for real-time child development assessment as claimed in claim 1, wherein the sensor inputs include but are not limited to audio microphones, video cameras, depth cameras, infrared-based motion sensors, thermal imaging devices, EEG-based brainwave tracking sensors, galvanic skin response (GSR) monitors, heart rate variability (HRV) sensors, or eye-tracking modules, each configured to provide multimodal developmental analysis.
14. The concurrency-managed, accelerator-based system as claimed in claim 13, wherein an adaptive sensory fusion module dynamically assigns processing priority to each sensor input based on real-time confidence scores, ensuring efficient computational resource allocation and reducing latency in anomaly detection, said module computing signal-to-noise ratio (SNR) for audio inputs, sharpness and stability for video inputs, motion consistency for inertial data, and physiological variability including heart rate or GSR fluctuations for stress-level adaptation, thus prioritizing high-confidence inputs for near real-time anomaly detection and assigning secondary resources to low-confidence inputs.
15. The concurrency -managed, accelerator-based system as claimed in claim 13, further integrating external wearable devices, including smartwatches, pressure-sensitive footwear, and biometric wristbands, so as to capture additional physiological or motor movement data and correlate these with developmental milestone assessments, wherein the system leverages the concurrency manager’s multi-threaded resource allocation to process both stationary and wearable sensor data in near real time.
16. A method of deploying the concurrency -managed, accelerator-based processing solution as claimed in claim 6 across multiple hardware and software environments, wherein the scoring mechanism is universally deployable by virtue of a modular API architecture using RESTful or GraphQL endpoints, a hardware-agnostic framework supporting CPUs, GPUs, TPUs, and neuromorphic processors, compliance with data standards (FHIR, HL7) enabling real-time data exchange with healthcare or educational platforms, and dynamic computation allocation permitting on-device processing with latencies under 50 ms on ARM-based mobile processors for real-time tasks or cloud-based computation for large-scale analytics exceeding 100 GB, thereby facilitating large-scale adoption, platform independence, and comprehensive ecosystem integration without vendor lock-in.