Human-AI interaction system using generative intelligence

Abstract

A human-AI interaction system using generative intelligence, wherein the system includes the following a sensor acquisition unit configured to acquire multimodal input data, including at least audio signals, visual data and environmental parameters from a user and their environment; a preprocessing processor that is operationally coupled with the sensor acquisition unit and is configured to perform signal conditioning, noise reduction, normalization and feature extraction on the acquired multimodal input data; a context aggregation unit that is operationally connected to the preprocessing processor and is configured to combine heterogeneous data streams into a unified context data structure that includes temporal attributes, spatial attributes, semantic attributes, and behavioral indicators; a user state determination processor that is operationally coupled with the context aggregation unit and configured to calculate at least the user intent, emotional state, and interaction preference using trained machine learning models and historical interaction datasets; a generative response processor operationally connected to the user state determination processor and the context aggregation unit, wherein the generative response processor is configured to produce one or more output representations, including text output, synthesized speech, visual content, or tactile signals, based on the unified context data structure and the computed user state; an output playback unit configured to output the generated output representations via at least one of the following interfaces: a display interface, an audio output interface, or a haptic interface; a coordination processor configured to manage data flow and distribute computational tasks between the preprocessing processor, the context aggregation unit, the user state determination processor, and the generative response processor; and a communication interface configured to enable data exchange between an edge computing environment and a remote computing environment, with the system configured to dynamically adapt interaction responses to the continuous updating of context data and user status.

Description

Application area of ​​the invention

[0001] The present invention relates to the field of artificial intelligence systems and human-machine interfaces, in particular a system and a device for enabling adaptive, context-sensitive and multimodal human-AI interaction using generative intelligence models integrated into a structured hardware and software architecture. Background of the invention

[0002] Existing human-computer interaction systems are predominantly rule-based or rely on tightly trained machine learning models, lacking contextual adaptability, multimodal integration, and real-time generation. Conventional interfaces such as graphical user interfaces, voice assistants, and chatbots often operate in isolation, resulting in fragmented user experiences and limited personalization. Furthermore, such systems do not dynamically adapt to user intent, emotional states, environmental conditions, and previous interaction history. With the advent of generative intelligence models, including transformer-based architectures capable of generating contextually coherent output across various modalities, there is a need for an integrated system and a dedicated device that leverages these models for seamless and intelligent human-AI interaction.

[0003] Human-machine interaction has evolved dramatically in recent decades – from simple command-line interfaces to sophisticated graphical user interfaces, dialogue systems, and multimodal interaction platforms. Early interaction paradigms were primarily deterministic and relied on explicit user commands. This offered limited flexibility and required users to adapt to system constraints. With the advancement of machine learning techniques, particularly supervised learning and pattern recognition, systems began to incorporate limited natural language processing capabilities and speech recognition modules. However, such systems remained largely domain-specific and heavily reliant on predefined datasets and static models. This limited their ability to generalize to other contexts or dynamically adapt to diverse user needs.

[0004] Conventional voice assistants and chatbot systems represent a significant milestone in human-AI interaction. They utilize natural language processing frameworks to interpret user input and generate responses. These systems typically employ pipeline architectures consisting of speech recognition, intention classification, entity extraction, dialogue management, and response generation. While effective in structured environments, such architectures are inherently modular and prone to error propagation between stages. For example, inaccuracies in speech recognition can impair intention detection, leading to irrelevant or incorrect responses. Furthermore, these systems rely on predefined intentions and pre-defined responses, limiting their ability to handle ambiguous, novel, or contextually complex queries.Furthermore, their lack of deep contextual understanding limits their ability to conduct coherent, multi-stage conversations over longer interactions.

[0005] Recent advances in deep learning, particularly the introduction of transformer-based architectures, have enabled the development of generative models capable of generating human-like text, images, and audio. Trained on extensive datasets, these models have demonstrated remarkable capabilities in natural language understanding and generation. Despite these advances, existing generative intelligence implementations are predominantly cloud-based and exhibit insufficient integration with real-time interaction systems. The reliance on centralized cloud infrastructure leads to latency issues, bandwidth limitations, and privacy concerns, especially for applications requiring continuous, low-latency interaction. Furthermore, such systems often operate as standalone modules rather than being seamlessly integrated into a unified interaction framework, resulting in fragmented user experiences.

[0006] Multimodal interaction systems were developed to overcome the limitations of unimodal interfaces by integrating multiple input channels such as speech, image, and gesture recognition. These systems aim to enhance the user experience through more natural and intuitive interaction. However, existing multimodal systems face significant challenges in synchronizing and merging heterogeneous data streams. Temporal misalignment, inconsistent data quality, and the lack of standardized fusion mechanisms often lead to incomplete or erroneous interpretations of user intent. Furthermore, the computational complexity associated with real-time processing of multimodal data places considerable demands on hardware resources, making such systems less suitable for portable or embedded devices.

[0007] Another crucial limitation of existing solutions lies in their inability to effectively represent the user's state—including emotional, cognitive, and behavioral characteristics. While some systems incorporate basic sentiment analysis or user profiling techniques, these approaches are typically superficial and fail to capture the dynamic and context-dependent nature of human behavior. Consequently, current interaction systems cannot adapt their responses to nuanced user states, resulting in interactions that can feel impersonal or inappropriate in certain contexts. The lack of continuous learning mechanisms exacerbates this problem, as the systems cannot evolve based on user feedback or changing interaction patterns.

[0008] Edge computing has established itself as a promising approach to addressing latency and data privacy concerns, as data processing occurs closer to the data's point of origin. Several existing systems utilize edge-based processing units for tasks such as speech recognition and image processing. However, the integration of edge computing with generative intelligence remains limited due to the computational cost of large-scale models. Current edge implementations often rely on compressed or simplified models, which impacts performance and accuracy. Furthermore, the lack of efficient orchestration mechanisms between edge and cloud resources leads to suboptimal utilization of computing resources and increased system complexity.

[0009] Besides technical challenges, existing human-AI interaction systems also face significant problems regarding scalability and interoperability. Many solutions are designed for specific applications or platforms, which makes their expansion or integration into broader ecosystems difficult. Proprietary architectures and a lack of standardized interfaces hinder seamless communication between different system components and external services. This fragmentation not only limits the scalability of such systems but also increases development and maintenance costs.

[0010] Security and privacy concerns represent another significant drawback of current solutions. The collection and processing of sensitive user data, including voice recordings, facial images, and behavioral patterns, raises considerable ethical and regulatory questions. While some systems implement basic encryption and access control mechanisms, these measures are often insufficient to meet the complex data protection requirements in distributed and multimodal environments. The lack of robust privacy-preserving methods such as federated learning and differential privacy further limits user trust and the acceptance of such technologies.

[0011] Furthermore, existing systems often lack robust feedback and adaptation mechanisms. Interaction is typically unidirectional, limiting the system's ability to learn from user corrections, preferences, or context changes. Reinforcement learning approaches have been explored to enable adaptive behavior; however, their implementation in real-world systems remains limited due to challenges in defining reward functions, ensuring stability, and preventing unintended behaviors. Consequently, most current systems exhibit static or slow adaptation, reducing their effectiveness in dynamic and evolving interaction scenarios.

[0012] Another limitation is the lack of a unified hardware-software co-design in existing solutions. Most systems are primarily developed with a focus on software functions, while hardware aspects are often neglected. This leads to suboptimal performance, increased energy consumption, and limited mobility. Specialized interaction devices, if they exist, often lack the necessary integration of sensors, processing units, and communication modules for smooth operation. The absence of a coherent device architecture further restricts the use of advanced interaction systems in real-world environments.

[0013] In summary, while significant progress has been made in the field of human-AI interaction, existing solutions suffer from several critical weaknesses. These include limited context understanding, a lack of multimodal integration, high latency, inadequate modeling of user state, and insufficient data privacy. The fragmented nature of current architectures, coupled with the absence of unified device frameworks, further restricts their scalability and practical applicability. These challenges underscore the need for a comprehensive system that integrates generative intelligence with advanced context modeling, multimodal processing, and adaptive interaction capabilities within a coherent hardware and software framework, thereby enabling more natural, efficient, and secure human-AI interaction. Summary of the invention

[0014] The present invention describes a system for human-AI interaction using generative intelligence. It comprises a specialized interaction device and a distributed computing framework for processing, interpreting, and generating multimodal data streams. The system includes a generative intelligence engine, a context fusion module, a user state inference engine, and an adaptive response synthesis module. All components are interconnected via a high-speed communication bus and coordinated by a central orchestration controller. The invention further describes a physical device with embedded sensors, actuators, edge processing units, and communication interfaces to enable continuous and adaptive interaction with a human user.

[0015] The present invention aims to provide a system for human-AI interaction using generative intelligence. This system enables seamless, context-sensitive, and adaptive communication between a human user and an artificial intelligence system, thus overcoming the limitations of conventional rule-based and modular interaction frameworks. A further aim of the invention is the development of an integrated architecture capable of processing multimodal inputs such as speech, visual signals, gestures, and environmental signals and fusing them into a unified contextual representation. This improves the accuracy and relevance of the system's responses. Another aim of the invention is the integration of a generative intelligence engine configured to generate coherent and context-aware outputs across various modalities, including natural language, audio, visual, and haptic feedback.This improves the naturalness and effectiveness of the interaction.

[0016] A further objective of the invention is to provide a mechanism for determining the user's state, dynamically recognizing and updating user intent, emotional state, cognitive load, and behavioral patterns. This enables the system to provide personalized responses in real time and adapt to changing interaction scenarios. Another objective of the invention is the efficient coordination of edge and cloud computing resources through a central orchestration controller. This reduces latency, optimizes resource utilization, and ensures real-time capability even in resource-constrained environments. Furthermore, an objective of the invention is to develop a dedicated interaction device that incorporates integrated sensors, processing units, and output interfaces within a unified framework, thus enabling continuous and immersive human-AI interaction in wearable and stationary configurations.

[0017] A further objective of the invention is the integration of data protection mechanisms, including encryption, anonymization, and distributed learning methods, to ensure the secure processing of sensitive user data and to strengthen user trust in the system. Another objective is the provision of adaptive response synthesis mechanisms capable of selecting and delivering optimal outputs based on context constraints, user preferences, and device capabilities, thereby improving usability and interaction efficiency. It is also an objective of the invention to enable continuous learning and further development of the system through feedback-driven optimization and analysis of historical interactions, allowing the system to improve its performance over time.Furthermore, the invention aims to provide a scalable and interoperable framework that can be integrated into various applications and platforms, thereby extending its applicability to various fields such as healthcare, education, assistive technologies and intelligent environments. BRIEF DESCRIPTION OF THE IMAGE

[0018] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of a human-AI interaction system using generative intelligence.

[0019] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0020] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0021] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation of it.

[0022] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0023] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0025] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0026] Fig.Figure 1 shows a block diagram of a system for human-AI interaction using generative intelligence.System 100 comprises: a sensor unit (102) for acquiring multimodal input data, including at least audio signals, visual data, and environmental parameters of a user and their environment; a preprocessing processor (104) connected to the sensor unit, which performs signal conditioning, noise reduction, normalization, and feature extraction of the acquired multimodal input data; a context aggregation unit (106) connected to the preprocessing processor, which combines heterogeneous data streams into a unified context data structure with temporal, spatial, semantic, and behavioral attributes; and a user state determination processor (108) connected to the context aggregation unit, which calculates at least the user's intent, emotional state, and interaction preference using trained machine learning models and historical interaction data.A generative response processor (110), operationally connected to the user state determination processor and the context aggregation unit, is configured to generate one or more output representations, including text output, synthetic speech, visual content, or tactile signals, based on the unified context data structure and the computed user state. An output playback unit is configured to output the generated output representations through at least one display interface, an audio output interface, or a haptic interface. A coordination processor (112) is configured to manage the data flow and distribute computational tasks between the preprocessing processor, the context aggregation unit, the user state determination processor, and the generative response processor.A communication interface (114) is configured to enable data exchange between an edge computing environment and a remote computing environment, with the system being configured to dynamically adapt interaction responses based on continuous updates of context data and user state.

[0027] In one embodiment, the sensor acquisition unit (102) comprises a distributed microphone array configured for beamforming and directional audio recording, an image sensor device with at least one high-resolution camera for face detection, and a depth sensor device for capturing three-dimensional spatial information, wherein the preprocessing processor is further configured to synchronize the timestamps of audio, image, and depth data streams to enable time-synchronized data processing.

[0028] In one embodiment, the preprocessing processor (104) comprises a dedicated neural acceleration processor configured to perform convolutional network operations for feature extraction from visual data and recurrent neural network operations for analyzing temporal sequences of audio data, the preprocessing processor further performing dimensionality reduction and encoding of the extracted features into a compact representation for transmission to the context aggregation unit.

[0029] In one embodiment, the context aggregation unit (106) is configured to generate the unified context data structure by applying weighted fusion techniques that assign dynamic importance values ​​to the various input modalities based on signal quality metrics, environmental conditions, and historical reliability values ​​of the interaction stored in a memory unit.

[0030] In one embodiment, the user state determination processor (!08) is configured to implement probabilistic inference techniques and reinforcement learning-based updates to continuously refine the estimation of user intent and emotional state. The processor further utilizes a temporal sequence of previous interactions stored in a data store to improve prediction accuracy across successive interaction cycles.

[0031] In one embodiment, the generative response processor (110) comprises a transformer-based neural network architecture configured for token-level prediction and sequence generation, wherein the processor is further configured to synchronize the generated outputs across multiple modalities by synchronizing the temporal features of speech, visual representation, and haptic feedback.

[0032] In one embodiment, the coordination processor (112) is configured to dynamically distribute computational tasks between a local processing unit and a remote processing unit, based on latency thresholds, available network bandwidth, and processing load conditions. The coordination processor also prioritizes the execution of time-critical interaction tasks within the local processing unit.

[0033] In one embodiment, the communication interface (114) comprises a wireless transceiver that supports multiple communication protocols and is configured to perform secure data transmission using encryption techniques and anonymization procedures to protect sensitive user data during transmission between the system and the remote computing environment.

[0034] In one embodiment, the output unit is configured to adapt the selection of the output modality based on user preference profiles and environmental conditions, with the unit selectively prioritizing audio output in poor visibility conditions and visual output in noisy environments, and furthermore adjusting the intensity and format of the haptic signals based on user sensitivity parameters.

[0035] The present invention provides a system for human-AI interaction using generative intelligence. System operation is controlled by a sequence of coordinated computational processes executed on multiple processing units (see claims). The system begins operation with the sensor acquisition unit, which continuously acquires multimodal input data such as audio signals, image sequences, depth information, and environmental parameters. Each data stream is time-stamped by an internal clock to ensure the temporal alignment of the modalities. The acquired signals are transferred to the preprocessing processor, which performs a sequence of signal conditioning operations, including filtering, normalization, segmentation, and feature extraction. Audio signals are spectrally decomposed and temporally structured, followed by the extraction of features such as frequency coefficients and energy distributions.Visual data is processed using convolutional layers to recognize facial features, object features, and gesture patterns, while depth data is converted into spatial point representations. The preprocessing processor then encodes the extracted features into structured vectors of reduced dimensionality to enable efficient further processing.

[0036] The encoded feature vectors are then fed to the context aggregation unit, which performs multimodal fusion to generate a unified context data structure. The aggregation process involves aligning the feature vectors based on synchronized timestamps, followed by the calculation of modality-specific confidence scores derived from signal quality metrics such as noise levels, lighting conditions, and sensor reliability. A weighted fusion technique is employed, assigning each modality a dynamic weight coefficient that influences its contribution to the final context representation. The fusion process produces a composite data structure containing semantic descriptors, temporal sequences, spatial relationships, and behavioral indicators related to the user and the environment.

[0037] The unified context data structure is then processed by the processor to determine the user's state. This processor implements a multi-stage inference procedure to estimate user intent, emotional state, and interaction preferences. It utilizes trained neural network models in combination with probabilistic inference techniques to analyze patterns in the context data. A temporal sequence of previous interactions, stored in memory, is integrated into the inference process, allowing the system to consider historical behavioral trends. Reinforcement-based update mechanisms are employed, using feedback signals from user responses and interaction outcomes to iteratively adjust internal model parameters, thereby improving predictive accuracy over time.The result of this stage is a structured user state representation that includes intent classification, emotional state vectors, and preference indicators.

[0038] The user state representation and the unified context data structure are provided to the generative response processor, which executes a transformer-architecture-based method for modeling generative sequences. The processor performs token-based predictions by iteratively generating output sequences that depend on the context representation and the user state. Attention mechanisms selectively focus on relevant parts of the input data, enabling the generation of coherent and contextually appropriate responses. For multimodal output generation, the processor creates synchronized data streams containing text, speech, visual content, and tactile signals. Temporal alignment is achieved by assigning each generated output element to a corresponding time index, thus ensuring consistency across modalities.

[0039] After generation, the output data is transferred to the output unit, which determines the appropriate modality or combination of modalities for displaying the response. This determination is based on user preferences, environmental conditions, and system limitations. For example, in noisy environments, the system prioritizes visual output, while in poor visibility conditions, audio output takes precedence. The output unit also adjusts parameters such as audio amplitude, screen brightness, and haptic intensity to optimize the user experience.

[0040] The coordination processor oversees the entire workflow by controlling data flow and the allocation of computing resources. It continuously monitors system parameters such as utilization, latency requirements, and network conditions. Based on these parameters, the coordination processor dynamically distributes tasks between a local and a remote processing unit. Time-critical operations, such as initial preprocessing and the generation of immediate responses, are prioritized in the local processing unit to minimize latency, while computationally intensive tasks, such as model updates and extensive data analysis, can be offloaded to the remote processing unit.

[0041] The communication interface enables secure data exchange between local and remote system components. Data transmitted via this interface is encrypted and anonymized to ensure data privacy. Furthermore, the system supports the continuous synchronization of model parameters and context data in distributed computing environments, thus enabling consistent performance and adaptive learning.

[0042] The entire technical process of the system is iterative and adaptive, with continuous feedback loops across multiple phases. User reactions and interaction results are captured and fed back into the processing chain, thereby optimizing context understanding, user state estimation, and response generation in real time. This closed-loop control ensures that the system incrementally improves its interaction capabilities and achieves a higher degree of personalization, context accuracy, and responsiveness over time.

[0043] The invention comprises a dedicated human-AI interaction device in a modular, portable, or stationary design. The device consists of a housing containing a multi-core processor unit, a neural accelerometer chip, memory modules, and a power management system. It also features multiple input sensors, such as microphone arrays, optical cameras, depth sensors, biometric sensors, and environmental sensors for capturing multimodal user and environmental data. Output interfaces include a high-resolution display, haptic feedback modules, audio output systems, and projection interfaces. The device also includes a connectivity module to support wireless communication protocols and edge cloud synchronization.

[0044] The system continuously captures multimodal input data from the user and the environment via a sensor array integrated into the interaction device. The captured data is preprocessed in an edge processing layer using signal normalization, noise filtering, and feature extraction. The processed data is then transferred to a context fusion module, which integrates heterogeneous data streams into a unified context representation. This context representation encompasses semantic, temporal, spatial, and behavioral attributes of the user and the interaction environment.

[0045] The context fusion module is operationally coupled with a user state inference engine configured to determine user intent, emotional state, cognitive load, and interaction preferences using probabilistic models, deep neural networks, and reinforcement learning techniques. The determined user state is continuously updated through feedback loops and historical interaction data stored in memory.

[0046] A generative intelligence engine, consisting of one or more transformer-based architectures and multimodal generative models, receives the context representation and the derived user state as input. The engine is configured to generate adaptive responses in the form of natural language text, synthetic speech, visual content, or haptic signals. The response generation process includes token-based prediction, semantic alignment, and multimodal synchronization to ensure coherence and relevance.

[0047] An adaptive response synthesis module selects, refines, and outputs the generated outputs via appropriate output interfaces based on user preferences, device capabilities, and environmental conditions. The module uses optimization techniques to minimize latency, maximize interpretability, and ensure energy efficiency.

[0048] The central orchestration control manages data flow, module coordination, and resource allocation across the entire system. It dynamically distributes computational tasks between edge and cloud environments based on latency requirements, network conditions, and utilization. The system also includes a privacy-friendly framework with encryption, anonymization, and federated learning mechanisms to ensure the secure processing of user data.

[0049] The device also incorporates a structural design with a modular housing, thermal management components, vibration dampers, and ergonomic design features that ensure user comfort and durability. The internal architecture includes multilayer circuit boards interconnected via high-speed buses, enabling parallel processing and low-latency communication between modules.

[0050] In operation, the system enables continuous, adaptive, and personalized interaction between a human user and an AI system. For example, the device can interpret the user's speech, facial expressions, and gestures in real time, infer intentions and emotional states, and generate context-sensitive responses in various modalities. The system also continuously adapts by learning from user interactions, thereby improving accuracy, responsiveness, and user satisfaction.

[0051] The present invention provides a unified and adaptive framework for human-AI interaction that enables the seamless integration of multimodal inputs and generative outputs. The system improves context awareness, personalization, and responsiveness while maintaining data privacy and computational efficiency. The unique device architecture ensures portability, scalability, and real-time capability, thus overcoming the limitations of existing systems.

[0052] The elements described in the system are implemented as concrete hardware components that form an integrated physical system, rather than representing abstract constructs. The sensor acquisition unit comprises physically integrated sensors such as microphone arrays, image sensors, and depth sensors. These are each implemented using electroacoustic transducers, photodetector arrays, and distance measurement circuits mounted on hardware substrates. The preprocessing processor, the user state determination processor, the generative response processor, and the coordination processor are each implemented as dedicated microelectronic processing circuits. These consist of arithmetic logic units, control units, and embedded memory structures, manufactured using semiconductor devices and interconnected via high-speed communication buses.The context aggregation unit is implemented as a hardware data integration circuit, comprising buffer memory, data fusion circuits, and timing controllers for the physical combination of synchronized data streams. The output unit consists of haptic output hardware such as display panels, audio transducers, and haptic actuators, which are controlled by signal generation circuits. The communication interface is implemented as a hardware transceiver, including high-frequency circuitry, modulation and demodulation circuitry, and antenna structures for physical signal transmission.

[0053] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0054] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A system for human-to-artificial intelligence interaction using generative methods. 102 Sensor data acquisition unit 104 Preprocessing processor 106 Context aggregation unit 108 User State Determination Processor 110 Generative Response Processor 112 Coordination Processor 114 Communication interface

Claims

A human-AI interaction system using generative intelligence, comprising: a sensor acquisition unit configured to acquire multimodal input data, including at least audio signals, visual data, and environmental parameters from a user and their environment; a preprocessing processor operationally coupled to the sensor acquisition unit and configured to perform signal conditioning, noise reduction, normalization, and feature extraction on the acquired multimodal input data; and a context aggregation unit operationally coupled to the preprocessing processor and configured to combine heterogeneous data streams into a unified context data structure that includes temporal attributes, spatial attributes, semantic attributes, and behavioral indicators.a user state determination processor that is operationally coupled with the context aggregation unit and configured to compute at least the user intent, emotional state, and interaction preference using trained machine learning models and historical interaction datasets; a generative response processor that is operationally coupled with the user state determination processor and the context aggregation unit, wherein the generative response processor is configured to produce one or more output representations, including text output, synthesized speech, visual content, or tactile signals, based on the unified context data structure and the computed user state;an output playback unit configured to output the generated output representations via at least one of the following interfaces: a display interface, an audio output interface, or a haptic interface; a coordination processor configured to manage the data flow and distribute computational tasks between the preprocessing processor, the context aggregation unit, the user state determination processor, and the generative response processor; and a communication interface configured to enable data exchange between an edge computing environment and a remote computing environment, the system being configured to dynamically adapt interaction responses to the continuous updating of context data and user state. System according to claim 1, wherein the sensor acquisition unit comprises a distributed microphone array for performing beamforming and directional audio recording, an image sensor device with at least one high-resolution camera for face recognition, and a depth sensor device for capturing three-dimensional spatial information, wherein the preprocessing processor is further configured to synchronize the timestamps of audio, video, and depth data streams to enable time-synchronized data processing. System according to claim 1, wherein the preprocessing processor comprises a dedicated neural acceleration processor configured to perform convolutional network operations for feature extraction from visual data and recurrent neural network operations for analyzing temporal sequences of audio data, wherein the preprocessing processor further performs dimensionality reduction and encoding of the extracted features into a compact representation for transmission to the context aggregation unit. System according to claim 1, wherein the context aggregation unit is configured to generate the unified context data structure by applying weighted fusion techniques that assign dynamic importance values ​​to the various input modalities based on signal quality metrics, environmental conditions, and historical reliability values ​​of the interaction stored in a memory unit. System according to claim 1, wherein the processor for determining the user state is configured to implement probabilistic inference techniques and reinforcement learning-based updates to continuously refine the estimation of user intent and emotional state, wherein the processor further utilizes a temporal sequence of previous interactions stored in a data store to improve prediction accuracy across successive interaction cycles. System according to claim 1, wherein the generative response processor comprises a transformer-based neural network architecture configured for token-based prediction and sequence generation, wherein the processor is further configured to synchronize the generated outputs across multiple modalities by synchronizing the temporal features of speech, visual representation and haptic feedback. System according to claim 1, wherein the coordination processor is configured to dynamically distribute computational tasks between a local processing unit and a remote processing unit based on latency thresholds, available network bandwidth and processing load conditions, wherein the coordination processor further prioritizes the execution of time-critical interaction tasks within the local processing unit. System according to claim 1, wherein the communication interface comprises a wireless transceiver that supports multiple communication protocols and is configured to ensure secure data transmission using encryption techniques and anonymization methods to protect sensitive user data during transmission between the system and the remote computing environment. System according to claim 1, wherein the output unit is configured to adapt the selection of the output modality based on user preference profiles and environmental conditions, wherein the unit selectively prioritizes audio output in poor visibility conditions and visual output in environments with high noise levels, and furthermore adjusts the intensity and format of the haptic signals based on user sensitivity parameters.

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