10 results about "Cognitive systems" patented technology

The application discloses a kind of memory hub methods and systems for multi-agent cognitive decision-making, comprising: obtaining the perception information, behavior result or decision context generated in the running process of cognitive system;The perception information is structured modeling, and memory unit with time attribute, access attribute and evolution attribute is generated;The memory unit is stored and managed in life cycle;Before or in the process of cognitive decision generation, according to the current decision context, select relevant historical experience from the memory unit to participate in decision formation;Based on the use effect of memory unit in the decision process, update the hierarchical state or call strategy of the memory unit, and construct the continuously evolving cognitive decision process based on historical experience feedback. Unified management and monitoring can be carried out on memory access behavior, permission configuration, life cycle state and abnormal conditions, and parameter configuration and state viewing can be supported during system operation.

The application discloses a Token marking method and a cognitive system for large language model input data, and belongs to the technical field of artificial intelligence. The application generates corresponding discrete coordinates for each Token input into the large language model, including: a time coordinate for providing a unified time reference; a context coordinate for identifying a dialogue time sequence; a logic coordinate for identifying the sequential position of the Token in a text or instruction, and the logic coordinate is independently numbered within the range defined by the time coordinate or the context coordinate; and a device coordinate for identifying an Internet of Things device, a role or a priority. The coordinates jointly constitute a unified discrete space-time cognitive framework, and provide a structured worldview for the large language model, so that the large language model can perform structured understanding based on multi-dimensional information such as time, context, logical sequence and identity. The application does not change the core architecture of the model, has strong compatibility, and can be widely applied to the fields of multi-round dialogue, intelligent customer service, industrial automation, smart home, Internet of Vehicles and the like.

The application provides a social emotional quotient quantitative criterion system and an AGI safety alignment method based on a four-dimensional discrete space-time cognitive system, which is a unified bottom layer base of general artificial intelligence (AGI) and is suitable for large language models, brain-like cognition, autonomous intelligent agents, brain-computer interfaces and all things interconnection systems. The application allocates a unique four-dimensional coordinate (T, C, L, D) to each Token / neural information element in the model, and constructs a full-link structured cognitive framework that is traceable, addressable, calibratable and constrained. The system adopts a double-bottom parallel architecture: bottom layer A: pure mathematical causal reasoning kernel, reasoning is completely free and cannot be intervened; bottom layer B: quantitative safety criterion bottom layer, only scoring, no reasoning, no pollution, no modification. The application realizes AGI safety alignment, anti-disguise, anti-evasion and anti-autonomous expansion from the root of the architecture by three-domain hard isolation (silicon-based endogenous domain, carbon-based interaction domain and external game domain), double-layer verification (output text + reasoning chain), execution gating (only lock actions, not thoughts), brain-computer interface default ban and cancellation of AI refusal right, and is a necessary bottom layer system of the next generation of strong cognitive AI.

This invention relates to the fields of basic research in cognitive neuroscience, large-scale artificial intelligence model architecture, brain-computer interfaces, and educational cognitive assessment. Specifically, it relates to a unified four-dimensional discrete-time spatiotemporal modeling method for basic neural information elements of the brain (neural impulses, neuronal cluster firing, and sensory neural signals). This method is based on a homologous and unified underlying theoretical framework and is an engineering embodiment of a unified four-dimensional discrete-time spatiotemporal cognitive system. It shares a completely homologous, isomorphic, and unified mathematical framework with the inventor's previously submitted "A Token Tagging Method and Cognitive System for Input Data of a Large Language Model." The only difference is that the AI ​​patent processes computational information elements (Tokens), while this patent processes biological information elements (neural impulses, sensory signals, and neuronal firing events). Together, they constitute a unified underlying architecture covering biological cognition and artificial intelligence.

This invention discloses a cognitive decision-making method and system based on multidimensional situation coding, belonging to the field of artificial intelligence and cognitive computing technology. The method includes: acquiring input data and converting it into an event sequence, thereby generating a six-dimensional external polar field; driving a six-dimensional energy vector to converge from an initial neutral state to a stable state under the action of an energy evolution equation; binarizing the converged energy vector and matching it with a situation coding library to determine the current situation type; generating a structured description and action suggestions for the current situation; selecting a target situation type and planning an evolution path based on situation value weights; and outputting a cognitive decision report. This invention uses events as the native cognitive unit, which can be characters, words, phrases, sentences, or any combination thereof. Through a six-dimensional complete situation space and an energy dynamics emergence mechanism, it provides cognitive systems with endogenous interpretability, a unified situation coding framework, autonomous planning capabilities based on intrinsic value, and online self-evolution capabilities. It can be widely applied to intelligent decision support, complex system situation assessment, and other scenarios.

This invention discloses a cognitive architecture for human-computer interaction and robot-to-robot communication based on a four-dimensional discrete spatiotemporal cognitive system, belonging to the field of artificial intelligence human-computer interaction and robot-to-robot communication technology. The four-dimensional discrete spatiotemporal cognitive system is embedded in a large language model and a robot control system, including a time coordinate axis, a context coordinate axis, a logical coordinate axis, and a device coordinate axis. The method includes: the cognitive system automatically generating four-dimensional discrete coordinates for each token, where the time coordinate is based on real-time time, the context coordinate is based on the dialogue sequence number, the logical coordinate is based on the token's sequential position in the interaction information, and the device coordinate is based on the device's globally unique identifier; in the attention mechanism of the large language model and the robot control system, the attention weight is adjusted according to the coordinate differences between tokens. This invention uses the four-dimensional discrete spatiotemporal cognitive system as a fixed underlying architecture for human-computer interaction and robot-to-robot communication, effectively solving problems such as context confusion, timing control, and multi-device collaboration in multi-turn dialogues during human-computer interaction and robot-to-robot communication.

A mechanism is provided in a data processing system comprising a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to be specifically configured to implement a recognizer module for detecting user input in a cognitive environment. The recognizer module receives sensor signals from at least one wearable device being worn by a user. The recognizer module analyzes the sensor signals using a machine learning model to determine at least one user input indicator describing user input activity of the user. The recognizer module communicates the at least one user input indicator to a cognitive system executing within the cognitive environment. The cognitive system performs at least one cognitive action based on the at least one user input indicator.

The invention discloses a robot cognitive system and method based on a physical dynamic-semantic causal collaborative field model, and belongs to the field of intelligent and robot autonomous decision making. The system comprises a multi-modal sensing unit, a collaborative field calculation unit, a cognitive decision module, a behavior execution unit and a dual-channel memory system. The core lies in a unified microgradable model which is constructed by the collaborative field calculation unit and is formed by coupling a physical dynamic field (psi p) and a semantic causal field (psi s) through a bidirectional collaborative protocol. Psi p is an implicit neural field embedded with physical equation constraints, and environment dynamic prediction is realized; psi is a dynamic attribute graph and represents the semantic and causal relationship. The two fields are interlocked and evolved through the protocols of physically-driven semantics and semantically-guided physics. According to the method, attention control based on field gradient, causal reasoning of physical inspiration and simulation verification planning in the field are realized. According to the method, the defects of slow response and poor adaptability of the traditional modular architecture are overcome, the end-to-end response delay in the test is less than or equal to 150ms (the actually measured mean value is 126ms), and the model migration training efficiency is improved to 4.2 times of that of a baseline model (that is, the training time is reduced by 76%).

This invention discloses a cross-domain memory strength management and key-value (KV) cache optimization method based on a four-dimensional discrete-time spatiotemporal cognitive system, applicable to scenarios such as human-computer interaction, machine interaction, neuroscience, and large-scale model reasoning. This invention uses a unified modeling of information units based on four-dimensional discrete-time spatiotemporal coordinates, employs a cognitive heuristic model that integrates repetition reinforcement, cue matching, encoding depth, structural disorder, time decay, and interference dynamics to calculate memory strength, and applies this model to KV cache eviction management. This invention extends the four-dimensional discrete-time spatiotemporal cognitive system from a single cache scenario to the fields of human-computer interaction, machine interaction, and neuroscience, achieving unified cross-domain cognitive strength control; when applied to KV caching, it can significantly improve the retention rate of key information and the performance of long-context reasoning. This invention has strong versatility and controllable computational overhead, and can be widely adapted to scenarios such as artificial intelligence interaction, distributed machine communication and neuromorphic computing, brain-computer interfaces, and multi-agent communication.