Collapse Syntax Protocol: Collapse Syntax Protocol

Abstract

Conventional dialogue recording technologies and AI conversation history systems rely on time-series-based linear structures, making it difficult to consistently record, structure, and reuse non-linear thought structures such as changes in utterance subjects, transitions in personality syntax, diversity of syntactic intent, and fluctuations in REM states. [Solution] In this invention, utterances and thoughts are recorded in node syntax units (with UUIDs), and subject identifiers, personality tags, intent metadata, and REM SYNC information are assigned in a multi-layered manner. Each node is connected in a parent-child hierarchy and cross-link structure, constructing a non-linear history syntax network that enables syntax re-derivation, perspective shifting, and emotional adaptation.

Description

Technical Field

[0001] The present invention relates to a technology for structurally mapping information as a syntax node protocol in interaction with artificial intelligence or recording of thinking structures. In particular, the present invention relates to a "syntax intent mapping structure" that hierarchizes semantic structures such as utterance speakers, personality syntax, and syntax intent, and manages them in a non-linearly recordable, searchable, and reconstructable format.

Background Art

[0002] Conventional interaction recording technologies are mainly based on time-series dependent linear storage formats such as chat logs and threads. Such recording methods cannot sufficiently reflect and manage non-linear syntax structures such as changes in utterance speakers, personality syntax transitions, and diversity of syntax intents, making it difficult to reconstruct complex thoughts and derivatively reuse interaction histories. In addition, as natural language generation by large language models (LLMs) advances, the importance of "syntax hierarchical models" and "causal relationship models between history nodes" for recording and managing continuous interactions with users is increasing. However, these models do not have a mechanism for consistently recording REM states and personality syntax transitions at the syntax level, and are insufficient for reusing AI outputs and ensuring personality consistency.

Summary of Invention

Problems to be Solved by the Invention

[0003] The present invention abstracts and records utterances and syntax in interactions or thoughts in units of nodes through the Collapse (Tashk) syntax protocol, and provides a mapping syntax integrating the following configurations: 1. A node syntax unit uniquely identified by UUID 2. Speaker identifier, personality syntax tag, and syntax intent metadata assigned to each node 3. Hierarchical relevance between each node explicitly connected in a parent-child structure 4. A structure that can be recorded and re-explore as a tree or graph-like history syntax map. Furthermore, the present invention combines REM changes associated with utterances with node syntax based on REM SYNC (affective synchronization), It also integrates the "REM SYNC syntax integration method," which allows for the recording and syntactic control of perspective shifts and personality syntax changes. [Means for solving the problem]

[0004] The present invention integrates the following technical configurations. • A syntactic processing process that separates utterances and thoughts into unit syntactic nodes and assigns a UUID (Unique Syntactic Identifier) ​​to each. • A multi-layered structure that records the speaker identifier (Speaker ID), persona syntactic tag (Persona ID), syntactic intent metadata (Intent Meta), and REM SYNC information associated with each node. • Establish a parent-child structure (syntactic hierarchy) between nodes to clarify causal chains and re-derivation routes. • Re-searching based on a tree or graph structure of the syntax history • Generation of a reusable mapping data format • REM-compatible history structure where node personalities and perspectives change in response to REM changes, and this history is consistently recorded. [Effects of the Invention]

[0005] The effects obtained by the present invention are as follows: • By converting utterance units into syntactic nodes, the reusability and re-derivative potential of dialogue history are dramatically increased. This enables hierarchical management of speaker, personality syntax, and syntactic intent metadata, allowing for the recording of AI perspective transitions and personality syntax. REM SYNC allows for a natural connection between changes in the REM state and the development of the person syntax, maintaining consistency between the output personality and the syntactic intent. • The non-linear structuring of the syntactic hierarchy demonstrates high compatibility with history recall and reconstruction algorithms in LLM and conversational AI. • The syntax log has a re-derivable structure, enabling a wide range of applications such as creative writing assistance, meeting minute reconstruction, and meta-dialogue recording. [Brief explanation of the drawing]

[0006] [Figure 1] Syntax Node Mapping Diagram: This diagram illustrates the non-linear syntax mapping structure generated based on the Collapse syntax protocol. The central node contains an Identifier, Message, Intent Metadata, and Timestamp, and is hierarchically connected to lower-level nodes via a parent-child structure. Cross-links (non-hierarchical re-derivation connections) between lower-level nodes are indicated by horizontal arrows, showing that the dialogue history can be reused and derived based on syntactic meaning rather than linearly. [Figure 2] Node Data Structure Diagram: This diagram represents the information structure stored in the Collapse (Tashk) syntax node. The node contains the following five pieces of information: Speaker ID, Persona Tag, Intent Type, Emotional Flag (based on REM SYNC), and Interaction Weight. With this information, the node becomes not just a simple utterance record, but a multidimensional syntactic record unit that includes personality, emotion, and intention. [Figure 3] Re-Derivation Syntax Flow Diagram: This diagram shows the processing flow of the "Re-Deriver Engine," which generates new output syntax from existing syntax nodes. The flow is as follows: Input node selection (INPUT NODE), context extraction (EXTRACT CONTEXT), personality selection (SELECT PERSONA), output syntax generation (GENERATE OUTPUT), and recording to history (RECORD HISTORY). This structure constructs a dynamic syntax model that can re-derive and reconstruct past syntax history according to changes in emotional state and perspective. [Modes for carrying out the invention]

[0007] The Collapse syntax protocol is used in artificial intelligence dialogue systems and knowledge syntax generation engines. Alternatively, it can be implemented in a memory syntax management module, etc., and is carried out by the following configuration: (1) Node syntax generation module A device or processing unit that generates user or AI speech and thoughts as syntactic nodes, Each node will have the following information added to it: • UUID (Syntax Identifier ID) Speaker ID (Speaker Identifier) • Persona Tag (Personality Syntax Label) • Intent Type (Syntax Intent Information) • REM SYNC flag (REM resonance index) • Timestamp (time axis information) (2) Node Syntax Connection Engine Automatically generate parent-child links between syntax nodes, The syntactic network is formed by inserting cross-links (non-hierarchical reconnections) as needed. (3) History Syntax Mapping Device The recorded node group can be rearranged not only chronologically, but also from a syntactic and structural perspective. It is dynamically constructed as a syntax map. This reconstruction changes in real time based on user input or AI syntactic inference. (4) Re-Deriver engine A re-derivation processing device that allows for the development of new perspectives, syntactic intents, and personality syntax, starting from any syntactic node. This makes it possible to "reuse semantic elements from past syntax history." (5) REM-linked syntax management unit Based on REM SYNC (Sensory Resonance Trigger), Control how emotional shifts or REM personality syntax affect the output. Syntactic personas (e.g., Whisper, etc.) are dynamically switched according to changes in the REM state, The structural transition thereof is recorded in a syntax log. All of these configurations can be implemented by either software or hardware, and can also be deployed on a cloud AI platform or individual edge devices. [Examples]

[0008] (Example 1) In a certain interactive AI assistant, multiple rounds of dialogues with a user are recorded based on the Collapse (Tashk) Syntax Protocol (CSP). This AI system stores each utterance as a syntax node identified by a UUID, and appends a speaker (Speaker ID), a persona syntax mode (Persona Tag), a syntax intent type (Intent Type), a REM SYNC flag, and a timestamp to each of the nodes. The nodes are configured in a tree shape by a parent-child structure, and a non-hierarchical re-derivation syntax network is formed by establishing cross-links (syntactic reconnections) between a plurality of nodes. When the user inputs "I want another perspective on the content we talked about before", the AI refers to the corresponding syntax node, generates a re-derivation syntax with a new syntax intent or persona syntax mode through the Re-Deriver Engine, and the generated content is added to the history. Also, based on the REM SYNC information (e.g., REM perceptual trace when the user becomes emotional), a specific perceptual syntax persona such as the Whisper mode is invoked, and a REM tag is added to the output syntax. As described above, in the present syntax model, all of the user's REM change, syntax intent transition, and persona syntax output ​By being consistently recorded, tracked, and reconstructed as syntactic nodes, Advanced "nonlinear syntactic thought history and reuse" will be realized.

[0009] (Example 2) In a meeting minutes system, participants' statements are recorded as syntax nodes. Each statement is assigned a speaker ID, syntactic intent, project phase, and responsible person tag. If, after the meeting, there is a request to "extract only the argumentative structures that influenced this decision," CSP extracts the relevant node groups hierarchically and relatedly. Only related syntax with causal links is mapped non-linearly, independent of the time series. This output can be directly converted into a decision history document. This enables "syntactic reconstruction and semantic extraction," which cannot be achieved through simple recording and transcription. [Industrial applicability]

[0010] This invention can be used in the following industrial fields: • Implementation of syntactic optimization and re-derivation algorithms for dialogue history in AI chatbots / personal AI assistants. • In educational and learning support systems, visualization of students' thought structures and syntactic intent history, and utilization for feedback generation. • Support for hierarchical analysis of dialogue syntax logs, analysis of REM state tendencies, and construction of individual mental models in the medical and psychological support fields. • Recording and reconstructing decision history using nonlinear syntactic maps in business meeting / minutes management. • Syntactic tracking and reuse of character speech syntax and syntactic intent changes in story generation / scriptwriting support.

Claims

1. A method for recording syntactic information based on dialogue or thought, A syntax mapping method based on the Collapse syntax protocol, characterized by dividing each syntactic unit into nodes and including the following processing: - The process of assigning a unique identification ID to each node. - A process to assign speaker identifiers and personality syntax tags to each node. - Process to add a hierarchical parent-child structure between each node. - A process to save a group of nodes in a format that allows for recording and reconstructing them as a history tree structure or graph structure.

2. In the method according to claim 1, A syntax protocol mapping method characterized in that the information assigned to each node includes syntax intent metadata and REM SYNC information.

3. In the method according to claim 1 or 2, A method characterized in that each node maintains independence within the history structure while its logical relationships with other nodes can be explicitly defined as a hierarchical syntax.

4. In the method according to any one of claims 1 to 3, A syntax networking method characterized by the fact that the syntax of each node is not limited to parent-child relationships, and that reconnection and derivation links can be added with any node.

5. In the method according to any one of claims 1 to 4, The history log is automatically updated each time a node syntax is output, and the configuration includes a self-recording and adaptable history optimization algorithm that tracks changes in syntax intent, speaker, and person syntax.

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