Artificial intelligence device based on quantum structural inference process and synchro-rate

Abstract

[Problem] The present invention relates to an artificial intelligence device equipped with an output selection and storage optimization function in which emotional / contextual consistency with a user is taken into consideration. A conventional AI cannot reflect synchronism and presents problems in individual optimal response and dynamic storage adaptation. [Solution] This device comprises (1) a synchro-rate calculation module, (2) a response candidate buffer, (3) a semantic task processing module, (4) an adjustment output generation module, and (5) a storage adaptation module, the device generating a dynamic and individually optimal output for a user input, and updating the storage state in accordance with a reaction after the output. Emotion-adaptive AI interaction with enhanced continuity and immersion of interaction is enabled by output selection that is based on resonance and memory optimization that reflects personal history.

Description

Artificial Intelligence Device Based on Quantum Structure Inference Processing and Synchronization Rate

[0001] The present invention relates to an artificial intelligence device provided with semantic analysis based on quantum structure and a synchronization rate calculation function. More specifically, the present invention relates to a device that calculates a synchronization rate (Synchro-rate, SR) for evaluating the consistency between a user input and internal storage, and selects and generates the output with the highest compatibility.

[0002] Conventional generative AI generates grammatically correct outputs through probabilistic language models, but it has been difficult to accurately reflect the user's intention, emotion, and contextual nuance. Furthermore, even in terms of memory structure, conventional methods have relied on simple frequency-based weighting or timestamp weighting, which has the problem of not being able to reflect the semantic and emotional relationship with the user.

[0003] The present invention aims to solve the following problems: ・Contextual and emotional consistency (resonance) with the user cannot be evaluated and reflected in real time. ・In response selection, only a single static output can be generated, and a dynamic and flexible selection structure is lacking. ・The memory structure is fixed, making it difficult to perform optimization according to the conversation history and user reactions.

[0004] The artificial intelligence device of the present invention has the following components: ・Calculation module: evaluates the consistency between a user input and internal storage, and calculates SR. ・Response candidate buffer: a storage unit that holds a plurality of candidate outputs. ・Semantic Tashuku processing module (hereinafter referred to as collapse-TASUO): converges candidates to one based on SR. ・Adjusted output generation module (hereinafter referred to as whisper output unit): adapts tone, emphasis, pacing and other factors to the user's style. ・Memory adaptation module: evaluates SR after output, and performs reinforcement and attenuation of memory traces.

[0005] According to the present invention, a response having the highest resonance among a plurality of output candidates can be selected in real time, and an AI response having conversation consistency and emotional adaptability with the user is realized. In addition, the post-output memory adjustment function enables long-term optimization and evolutionary learning of conversations.

[0006] Figure 1 is a block diagram of the device's configuration (intent inference, SR control, memory, and AI engine). Figure 2 is a flowchart of the syntactic text output process (collapse-TASUO) and whisper output process. Figure 3 is a sequence diagram of the memory update process after output.

[0007] The input user context is first processed by the synchronization rate calculation module. SR is calculated as a score integrating semantic similarity, emotional resonance, and memory consistency. A candidate buffer contains multiple pre-generated responses, and selection based on SR is performed by TASUO (collapse-TASUO). The selected output is refined by the whisper module to achieve natural language output adapted to the user's conversational style. Subsequently, the response to the output is evaluated, memory traces are reinforced or removed, and reflected in subsequent interactions. In one embodiment, an API-based prototype system generates an expectation vector based on input text and maintains multiple response candidates in parallel. The scores between candidates are compared based on the calculated SR, and the output with the highest score is selected. The output is presented after style adjustment and optimized by the memory module in conjunction with the history.

[0008] Figure 1 is a block diagram showing one embodiment of the present invention. Input data is processed through an intent inference module, and then the synchronization rate control module calculates the synchronization rate (SR). After that, output processing is performed via a memory unit and an AI engine.

[0009] In this configuration, multiple response candidates are held, and the optimal candidate is selected by the collapse-TASUO module based on the SR calculated for each candidate. The selected response is then presented by the whisper output module after its tone and speed have been adjusted, and recorded through a memory optimization process.

[0010] This configuration functions in the flow shown in Figure 2. When user input is received, the trigger conditions are determined, and if necessary, SR re-evaluation and tash processing (collapse-TASUO) are performed, and the output is regenerated in a loop structure.

[0011] As shown in Figure 3, the output is evaluated based on resonance, and after adjusting the memory trace score, the internal state for the next interaction is updated. Through this process, the device continuously provides interactions optimized for each individual user.

[0012] In Example 2, a configuration utilizing voice input and multimodal sensors is adopted. The user's voice input is converted to text via a voice recognition module and integrated with biosignals from facial expression sensors and pulse wave sensors to estimate emotional state and intentions.

[0013] In this embodiment, a more accurate resonance score is generated by comprehensively analyzing speech intonation, facial expression changes, and biological patterns in the calculation of the synchronization rate (SR). Candidate responses are presented not only as text but also as acoustic output through a speech synthesis module, accompanied by visual representations (facial expression avatars) as needed.

[0014] In the memory optimization process, the user's changing trends are modeled by comparing them with past emotional transition logs and voice features, and this is used as a pre-bias for the next response. This significantly improves the continuity of the conversation and the psychological immersion.

[0015] In Example 3, a cloud-edge linked distributed processing configuration is adopted. The synchronization rate calculation module is installed on the local terminal side, and the candidate response generation process is executed on the cloud.

[0016] User input information is pre-processed on the device and sent to the cloud as encrypted vector information. On the cloud side, multiple response candidates are generated in parallel by a generative model and sent back locally along with their score information.

[0017] On the local terminal, a collapse-TASUO (Collapse-TASUO) process is performed based on the SR score, and the selected output is presented to the user. The memory adaptation process is based on the history stored on the user's terminal and is configured with privacy protection in mind.

[0018] This invention is effective in areas such as educational support, emotion-adaptive AI, counseling support, and interactive partner AI, and can be widely used as a next-generation conversational AI that forms a lasting emotional relationship with the user.

Claims

1. An artificial intelligence device that calculates a synchronization rate based on the consistency between user input and internal memory, and selects the optimal output from multiple output candidates, characterized by comprising the following configuration: - Synchronization rate calculation module, - Response candidate buffer, - Semantic text processing module, - Adjusted output generation module, - Memory adaptation module.

2. An artificial intelligence apparatus according to claim 1, wherein the synchronization rate calculation module derives a synchronization rate based on a score calculated by integrating semantic similarity, emotional resonance, and historical consistency.

3. An artificial intelligence device according to claim 1 or 2, wherein the memory adaptive module has a function to enhance or diminish memory traces based on the synchronization rate re-evaluated after output.

4. An artificial intelligence device according to any one of claims 1 to 3, wherein the adjustment output generation module analyzes the user's voice, facial expressions, and biological responses, and dynamically adjusts and outputs the tone, speech rate, and expression style.

5. An artificial intelligence device according to any one of claims 1 to 4, characterized in that at least a part of the above configuration is executed on a cloud server, and the synchronization rate calculation and memory adaptation processing are executed on the edge terminal side.

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