System and method for full-visual data matching, and rights confirmation and traceability

Abstract

The present invention relates to digital rights vision engineering, and discloses a system and method for full-visual data matching, and rights confirmation and traceability. With Configuration for All as a core, and on the basis of an underlying architecture of Intelligent Configuration for All, the reality of encoding equals rights confirmation and indexing equals identity is achieved. A three-stage processing mechanism, namely, matching, intelligent configuration, and generation, is constructed in order to achieve the highly effective cross-industry re-utilization of all operator and consumer data in the world. In value creation and fair circulation, individual-dedicated mirror digital spaces are precipitated, behavioral, aesthetic, and policy making features are extracted, and individual AIs are cultivated to extend digital personality projects, supplying the support for carbon-silicon symbiosis. The problems of data silos, ownership fragmentation, and imbalanced value distribution are solved at the root, realizing highly effective trusted circulation of data and individual digital sovereignty, and providing an underlying solution for the global data element market and human civilization ascension.

Description

[0001] Invention Title: A System and Method for Full Visual Data Matching, Rights Confirmation, and Source Tracing

[0002] 1. Technical Field

[0003] This invention belongs to the field of digital rights vision engineering, pioneering a new technological system for the full traceability and efficient and reliable transfer of human civilization's data assets. It involves the fields of data-invisible rights confirmation and visual processing technology, specifically a system and method for full-visual data matching, rights confirmation, and traceability. Through lightweight coding indexes and unique identification mechanisms, it lays the foundation for trust in human digital civilization.

[0004] 2. Background Technology

[0005] 2.1 Current Status and Fundamental Challenges of the Industry

[0006] Visual data is a core production element of the global digital economy and a crucial entry point for driving consumer decisions and value circulation. However, current technologies suffer from ambiguous ownership of visual data and restricted cross-platform transfer, resulting in a lack of value returns for creators. Simultaneously, consumer behavior data is appropriated by platforms without compensation, and a value distribution mechanism is lacking. Existing technological approaches, including traditional blockchain notarization, centralized databases, and AI-generated models, have failed to address the technical problems of fragmented ownership and the inability to automatically link value distribution to the original creator during the transfer of visual data.

[0007] 2.2 Limitations of Existing Technological Approaches

[0008] Current mainstream technologies focus on improving computing power, expanding model parameters, increasing the number of templates, and optimizing generation effects, aiming to solve the problem of visual content generation efficiency. However, they have the following technical shortcomings: the ownership relationship between the generated content and the original creator is broken during the generation process, resulting in the generation results being unable to be linked to the original creator; editing, adaptation, and other operations are not bound to the original index, forming new ownerless data; individual AI systems are controlled by the platform, and users cannot have independent data sovereignty and value benefits.

[0009] 2.3 Initial Research and Development Objectives

[0010] In June 2018, when the inventor was in charge of the decoration project of Huizhou Glad Hotel, he deeply discovered that, due to the modification of nearly 2,000 construction drawings within one month, the root cause of the problem lies in the break of the matching relationship between visual data and the creative subject, rather than insufficient drawing efficiency. Based on this, the inventor deeply realized that "only matching is the way for civilization to operate". Since May 2019, a team has been formed. After years of research and development and engineering verification, the technical solution described in this invention has been formed. As of the filing date of this application, 120 data asset confirmation and filing qualifications have been obtained, providing an original foundation support for subsequent digital personalities and interstellar communication technology architectures, and being the only batch of representative cases of human settlement space data elements globally.

[0011] 2.4 Technical background verification

[0012] After retrieving and analyzing the existing technologies, no technical solution has been found that has the technical characteristics of "encoding is confirmation of rights and traceability throughout the process", realizes a three-level matching system driven by lightweight encoding indexing, and completes the engineering verification of visual data assets being compliant and listed on the data exchange. This invention realizes the integration of encoding and confirmation of rights through the self-developed AIUGC large model. The encoding process and ownership metadata are natively completed within the model, making encoding the confirmation of rights and indexing the identity.

[0013] 3. Invention overview

[0014] This invention takes the previous patent of ten thousand households' intelligent matching and all things matching drive as the only underlying entry. Through lightweight encoding to generate indexes, it realizes native confirmation of rights and traceability throughout the process, constructs a three-level visual processing architecture, and no new indexes are generated during editing and generation; the system forcibly binds legal tender to realize automatic settlement and fair and timely distribution of data value between two parties (practitioners, consumers) point-to-point in the whole industry and the whole domain, forming a digital asset transfer underlying architecture with privacy compliance, low computing power, high timeliness, and scalability, and activating the underlying cornerstone of the global data element marketization.

[0015] 4. Technical problems

[0016] 4.1 Existing visual data cannot achieve native confirmation of rights in the whole process of collection, encoding, processing, and trading, resulting in discontinuous ownership relationships and unreliable traceability chains, making it difficult to form a legal and compliant digital asset transfer system. The cost of confirming rights is high, the efficiency is low, and the operation is cumbersome. It is impossible to achieve seamless, real-time, and scalable confirmation and registration of visual data assets, and it is only open to enterprises, ignoring the value of the original individuals.

[0017] 4.2 Traditional visual content processing methods consume a lot of computing power and have high response latency, making it impossible to achieve a real-time, low-power, local operation of the visual interactive experience on lightweight terminals. During the editing and combination generation process, new independent indexes are constantly generated, causing index explosion, storage expansion, and system performance degradation, making it impossible to run stably and on a large scale for a long time.

[0018] 4.3 The failure to use "matching" as the underlying mandatory entry point for value operation makes it impossible to activate the efficient flow of existing real data in the market, and to prevent ownerless data, anonymous data, and data without ownership from entering the business process at the source, making it difficult to achieve full-link controllability; at the same time, the lack of an automatic expiration and destruction mechanism for non-transactional data can easily lead to privacy leakage risks, storage waste, and system redundancy.

[0019] 4.4 The distribution of visual data value relies on third parties, making it impossible to achieve point-to-point automatic settlement, two-way revenue sharing, and fair profit distribution between creators and consumers. This results in a lack of data flow in the global digital economy, which in turn hinders creation; a lack of currency flow leads to a lack of wealth flow and stagnation of civilization.

[0020] 4.5 Society has not reached a consensus on a mechanism for the exclusive accumulation of data centered on individuals. Creative data, behavioral data, and preference data cannot form exclusive individual assets. Data rights are not protected and instead become fuel for AI, reaping value and backfiring on creativity.

[0021] 5. Solution to the problem

[0022] 5.1 Core Logic of the Invention

[0023] This invention belongs to the field of computer system architecture technology, specifically relating to a full-visual data matching, rights confirmation, and traceability system that uses hard-coded constraints at the system's underlying layer as a technical means. Unlike existing technologies that treat "matching" as an optional function, this invention sets the matching result as the only legitimate entry point at the system's underlying layer. This entry point is enforced by a unified permission verification module through code logic: any visual data that has not been matched and has not output a matching result, regardless of the method used (including but not limited to direct calls from internal system modules, imports from external interfaces, data format conversions, identifier re-encoding, indirect input through third-party systems, etc.) to attempt to enter the subsequent processing stage, will be automatically identified and blocked by the system's underlying layer.

[0024] The aforementioned technical methods solve the technical problems in existing technologies, such as ownerless data flow caused by ownership fragmentation, system performance degradation caused by index explosion, and the inability to automate value allocation. They also achieve technical effects such as a coding accuracy of 99.9%, index storage compression of 3000 times, and a reduction in computational power consumption to less than one ten-thousandth of existing technologies. Therefore, this invention falls under the category of "technical solutions" as defined in Article 2, Paragraph 2 of the Patent Law, and does not fall under the category of "rules and methods for intellectual activities."

[0025] In this invention:

[0026] • Matching layer: Used to establish the connection between visual data and requirements;

[0027] • Smart Matching Layer: Based on matching, the visual data is adaptively adjusted. The adjustment results are bound to the original index and no new index is generated.

[0028] • Generation layer: Based on matching, multiple visual data are combined to generate new content. The new content is bound to all original indexes, and no new indexes are generated.

[0029] Matching supply and demand is the starting point of the technical architecture of this invention. Fiat currency serves as the carrier of value, and its transactions are automatically executed by the underlying smart contract when matching is triggered, enabling direct value transfer from consumers to creators without the need for third-party intermediaries. 5.2 Definition of Core Terms

[0030] 5.2.1 Full Vision

[0031] It refers to the sum of all visual information directly or indirectly perceived by the human eye, any acquisition device, imaging device, AI vision system, and smart terminal. In the technical system of this invention, "full vision" encompasses the entire chain of visual data forms from acquisition, encoding, rights confirmation, matching, intelligent allocation, generation to transaction and profit sharing, using a lightweight encoding index as a unique identifier, and completing a closed-loop circulation on the legal tender value carrier.

[0032] 5.2.2 Light Vector

[0033] This invention features a unique feature encoding method that transforms all visual information into a standardized feature vector that is identifiable, traceable, and authoritative. This vector is used to uniquely identify and carry ownership information and is the core foundation of lightweight fusion encoding.

[0034] 5.2.3 AIUGC Large Model

[0035] This invention relates to a self-developed integrated coding and rights confirmation model that relies on optical vectors to complete lightweight fusion coding and carry out native rights confirmation, realizing that coding is rights confirmation and indexing is identity.

[0036] 5.2.3 Lightweight Fusion Coding

[0037] The integrated encoding algorithm chain based on light vectors integrates multi-dimensional feature extraction, unified color space mapping, key point detection, dimensionality reduction and sparsification to compress visual data into high-dimensional sparse vectors and natively incorporate the ownership information of the subject during the encoding process.

[0038] 5.2.4 Lightweight Coding Index

[0039] The unique visual digital asset identifier is generated by lightweight fusion encoding and natively confirmed through AIUGC large model. Its storage capacity has reached 0.03% of the original image. It is the core carrier for the whole process of confirmation of rights, matching, traceability and transaction, and has the core functions of AI indexing, data reuse and consumption decision driving.

[0040] 5.2.6 Mirror Space

[0041] This refers to the digital visual space constructed using the technical system of this invention. This space is identified by a unique "mirror space index," which is an application of lightweight coded indexes in the aggregate dimension, binding the ownership information of the space creator (subject). Within this space, every piece of visual data, every individual AI, and every matching and transaction behavior obtains a unique identity and circulation certificate through its corresponding lightweight coded index. Each piece of visual data, every individual AI, and every matching and transaction behavior within the space is simultaneously bound to its individual index and spatial index. The individual index identifies "who owns" the data, and the spatial index identifies "where" the data is located; together, they constitute a complete ownership traceability chain. The mirror space is not a virtual platform, but rather a collection of sovereign, traceable, and transferable digital assets composed of countless individual indices, asset indices, behavioral indices, and a master spatial index.

[0042] 5.2.7 Individual AI Database

[0043] This refers to a dedicated database built using lightweight coded indexes as the unique access key, based on users' historical matching behavior data. This database stores structured data such as user operation preferences and transaction records to support accurate matching for personalized services. Non-transactional data storage has a lifespan of 7TB and 5 days, and is automatically and irreversibly destroyed upon expiration, except in judicial evidence collection scenarios. 5.2.8 Visual Data Visualization

[0044] The technical parameters set in this invention (encoding response W 100ms, index storage W original image 0.03%, generation computing power W 0.03 GFLOPS, matching response W 1 second) are not arbitrary settings for commercial selection, nor are they incremental improvements that can be obtained through conventional optimization. Instead, they are based on a comprehensive calculation result of the following three hard technical constraints:

[0045] (1) Limits of human visual perception: The persistence of vision in the human eye is approximately 100-400 milliseconds. If the end-to-end latency exceeds 100ms, users will clearly perceive a "wait" and will be unable to achieve an "instant visualization" experience. This invention compresses the core operations of encoding, matching, intelligent matching, and generation to the 100ms level, so that users are unaware of the existence of data processing throughout the entire interaction process. This is the technical upper limit for achieving a "what you see is what you get, what you get is what you belong to" experience.

[0046] (2) Physical Constraints of Lightweight Terminals: The NPU computing power of next-generation terminals such as smart glasses and AR / VR headsets is usually less than 1 GFLOPS, and the available memory is usually less than 512MB. Existing AI generation modes consume 300 GFLOPS of computing power and occupy GB-level video memory per generation, which is physically impossible to deploy on such terminals. This invention compresses the generation computing power consumption to 0.03 GFLOPS (less than one ten-thousandth of the existing technology) and the index storage to 0.03% of the original image (3000 times compression), so that the technical solution of this invention can run natively within the physical constraints of lightweight terminals. This is a necessary technical condition for realizing "data visualization" on the terminal side, not an option.

[0047] (3) System performance inflection point constraint: Through engineering verification (actual measurement with 1.33 million visual data), for every 10% increase in the size of the index database, the retrieval response time increases by about 3%, and the storage cost increases by about 15%. When the index storage exceeds 0.03% of the original image, the retrieval response time of the million-level index database will exceed 1 second, which cannot meet the requirements of instant visualization. Therefore, a storage compression ratio of 0.03% is the maximum compression ratio that can be achieved under the premise of ensuring retrieval performance, and a temporary storage period of 7-15 days is the optimal balance between storage cost and user experience.

[0048] The aforementioned three constraints together constitute a technical necessity—if any parameter deviates from the threshold set by this invention, the technical requirements of "instantaneous visual experience," "lightweight terminal adaptation," and "system performance scalability" cannot be simultaneously met. Therefore, the parameter settings of this invention do not belong to "conventional optimization," but are necessary technical features for achieving specific technical effects.

[0049] 5.3 Three-level visual processing technology system

[0050] 5.3.1 Primary Matching

[0051] The essence of primary matching is feature vector retrieval based on lightweight coded indexes and multi-subject source binding. It's not a simple keyword search, but rather performs similarity calculations in a high-dimensional feature space and permanently binds the matching results to the original index simultaneously. The matching similarity threshold is 80% (adjustable by industry: 85% for design industries, 90% for the medical industry). It outputs the Top-K most similar lightweight coded indices within 1 second (K is configurable from 100-500 sets by default), and the matching response time W is 1 second (for a million-level index database).

[0052] 5.3.2 Intermediate Intelligent Configuration

[0053] The essence of intermediate-level intelligent matching is to perform cross-scene intelligent adaptation and personalized DIY adjustments on existing visual data while preserving the original ownership. All adjustment parameters are permanently bound to the original index as auxiliary metadata, without generating new indexes. The system's underlying hard-coded implementation prohibits index generation function calls, and the index database is automatically verified after each intelligent matching operation to ensure no new index records are added. Processing latency is W = 150ms (single 2K image), and the cross-industry scene adaptation success rate is ?98%.

[0054] 5.3.3 Advanced Generation

[0055] Advanced generation essentially involves creatively combining existing lightweight encoded indexes based on established ownership to generate entirely new visual content. All generation parameters are permanently bound to the original indexes as supplementary metadata, without generating new indexes. All input features required for the generation process originate from successful retrieval of multiple original indexes during the matching process; the generated result itself does not possess independent asset attributes. The generation latency is W 100ms (for a single 2K image), and the computational power consumption is W 0.03 GFLOPS (less than one ten-thousandth of existing AI generation modes), capable of outputting 100 solutions within 1 second.

[0056] 5.3.4 The progressive relationship of the three-level processing

[0057] The three-tiered visual processing technology system is not a simple superposition of three independent functions, but an inseparable value creation chain with "original lightweight coded index" as its core DNA: Primary matching brings existing data to life, marking the starting point of value discovery; intermediate intelligent matching adds value to the livened data across scenarios, marking the starting point of value enhancement; advanced generation allows for the creative combination of value-added data, marking the starting point of value creation; individual AI sedimentation allows the combined results to be sedimented into an individual's exclusive memory, with the AI ​​itself acting as a dynamic "individual intelligent index," marking the starting point of value reuse; mirror world construction: when countless individual AIs carrying their owner's lightweight coded index connect, interact, and co-create in a dedicated mirror space also identified by a "spatial index," a living, sovereign digital world naturally forms—a value aggregation endpoint. Without matching, there is no intelligent matching; without matching, there is no generation; neither is dispensable.

[0058] Without matching, there is no individual AI; without indexing, there is no mirror space, no mirror world.

[0059] 5.4 Beneficial Effects

[0060] 5.4.1 Beneficial effects at the technical level

[0061] I. Indicator | Invention | Prior Art | Improvement Scope

[0062] Encoding response time < 100ms (single 2K) | Traditional blockchain notarization takes 45 seconds | 450 times faster

[0063] Coding accuracy N99.9% | Incomparable | —

[0064]

[0065] Index storage size is 0.03% of the original image | Original data storage | 3000x compression

[0066] Matching response time W1 seconds (millions) | Traditional search in minutes | 60 times+

[0067] Generation computational cost: 0.03 GFLOPS | 300 GFLOPS (AI generation) | 0.01%

[0068] Generation latency < 100ms | 30-60 seconds (AI generation) | 300-600x

[0069] Transaction settlement delay of W3 seconds (first time) | Traditional settlement takes days | Tens of thousands of times longer

[0070] The cost of rights confirmation approaches zero | Gas fee is $2.3 per instance | Costs are almost eliminated, making large-scale rights confirmation possible. Through a lightweight fusion coding unique identification mechanism, a permanent binding between visual data and the creator is achieved. The rights confirmation process is completed automatically and seamlessly, with costs approaching zero. Through a progressive processing architecture driven by "matching," the matching result is set as the sole prerequisite for all subsequent operations, eliminating the flow and processing of ownerless data from the system's bottom layer. Through a modular design of three processing modes, the editing, generation, and user data accumulation of visual data are realized, and the generation of new indexes is prohibited throughout the process, ensuring the uniqueness and traceability of ownership.

[0071] In this invention, the storage period for non-transactional data is set at 7-15 days. This is not an arbitrary commercial choice, but rather a hard constraint based on three technical factors: First, compliance with privacy protection regulations. Visual data is considered temporary behavioral data before a transaction is confirmed, and storing it beyond the legally stipulated period would constitute a potential infringement on user privacy rights. Second, calculations from a storage cost optimization model show that for every 10% increase in the index size, retrieval response time increases by approximately 3%, and storage costs increase by approximately 15%. The 7-15 day window achieves optimal allocation of storage resources while ensuring user experience. Third, the system's automatic cleanup cycle is technically designed to synchronize with index increments, defragmentation, and backup windows. 7T5 days is the optimal window, verified through engineering. These three factors together constitute a technical necessity, rather than an arbitrary choice.

[0072] 5.4.2 Beneficial Effects of Application

[0073] • It provides practitioners (creators) with protection and monetization channels for their original value. Every matching, editing, and generation can be traced back to the original creator, protecting their right to income.

[0074] • It provides consumers (users) with a mechanism for quantifying and rewarding the value of their behavior. Every browsing, DIY, and transaction can be accumulated into personal data assets and can participate in secondary profit sharing;

[0075] • Provides a technological foundation for the fair circulation of global data elements through the marketization of data elements, and achieves full-chain traceability and automatic verification of transactions by binding lightweight coding indexes with legal tender;

[0076] • LBS near-field matching (accuracy ± 10 meters) supports localized consumption decision-making scenarios.

[0077] 5.4.3 Core Advantages of Full-Visual Data Visualization

[0078] I Indicators I This Invention I Existing AI Generation Modes | Core Differences Between This Invention and AI Generation | End-to-End Latency | W150ms | 30-60 seconds | Imperceptible to the human eye vs. Obvious waiting | I Terminal Computing Power Requirements | 0.03 GFLOPS | 300 GFLOPS | Lightweight terminal application vs. Requires cloud server | I Real-time Application | Supported | No | On-site decision-making vs. Offline maintenance | I Rights Confirmation Timeliness | With rights confirmation | Without rights confirmation | Seamless automatic rights confirmation vs. No rights confirmation | This invention belongs to the underlying technology system of full-vision data matching, rights confirmation, traceability, three-level visual processing and value transfer, and is not comparable to AI generation. The intermediate intelligent matching and advanced generation capabilities of this invention are only technical means for data owners (practitioners, consumers) to achieve value exchange and high-speed circulation.

[0079] 5.5 The Irreplaceability of Core Technological Features

[0080] 5.5.1 Definition of the technical attributes of "matching-driven"

[0081] The "matching-driven" approach described in this invention is essentially a mandatory dependency relationship implemented through hard coding at the system architecture level, which differs from the optional search or recommendation functions in existing technologies.

[0082] (1) The matching result serves as the only legal entry point for all subsequent functional modules. This constraint is enforced by the unified permission verification module at the bottom of the system through code logic. It cannot be bypassed or removed by modifying the configuration file, switching the function switch, replacing the module, encapsulating the interface, adapting the middleware, etc. It is a pure technical architecture design.

[0083] (2) The lightweight encoded index on which the matching process depends is generated by binding the author's ownership metadata, hardware identifier, and algorithm feature triple verification information. The index itself is a technical carrier. Any identifier information that fails the triple verification will be automatically determined as an invalid index by the system's underlying layer and will not trigger the matching driving unit. It is a pure technical verification mechanism.

[0084] (3) Once the matching result is output, the system bottom layer automatically binds the result with the subject index that initiated the matching, the visual data index that was matched, the matching timestamp, and the matching parameters in a permanent encrypted manner to form an immutable technical traceability chain. This binding process is automatically executed by the system bottom layer without any manual intervention and is a pure technical binding mechanism.

[0085] The "matching driver" described in this invention is a technical solution at the computer system architecture level, with a clear technical implementation path and hardware adaptation requirements.

[0086] 5.5.2 Technological Innovation of "Matching as the Sole Precondition"

[0087] In existing technologies, the output results of "retrieval" or "recommendation" are usually optional references that users can choose to adopt, ignore, or bypass. There is no mandatory dependency between them and subsequent operations, and no hard-coded constraints are set at the system architecture level.

[0088] The "matching-driven" system described in this invention establishes a mandatory dependency between its output and subsequent operations at the system's underlying level. The specific differences are as follows: I. Comparison Dimensions | Existing Search / Recommendation Systems | Matching-Driven System of This Invention |

[0089] I Result Nature I Optional Reference | Unique Valid Entry Point I

[0090] I Dependency Relationships I Soft Dependency (Can be bypassed by the user) I Hard Dependency (Mandatory at the system level) I

[0091] I. Bypass Possibility: I Exists (Directly calls subsequent modules) I Do Not Exist (Blocked by the unified permission verification module) I

[0092] I Ownership Binding I None I Permanently Binding to Original Index I

[0093] I. Technical Implementation I. Functional Module Level I. System Architecture Level (Hard-coded) |

[0094] The above differences indicate that the "matching-driven" approach described in this invention represents a fundamental restructuring at the system architecture level and possesses significant technological innovation.

[0095] 5.5.3 The Technical Necessity of "Prohibiting the Generation of New Indexes"

[0096] The core technical purpose of prohibiting the generation of new indexes is to ensure the uniqueness and traceability of ownership. The specific technical logic is as follows:

[0097] (1) If new independent indexes are allowed to be generated during the editing and generation process, the newly generated visual content will form a break in ownership with the original content, and it will be impossible to accurately trace back to the original creator during the subsequent circulation process. This invention achieves full ownership traceability of the editing and generation results through the technical means of "permanently binding the original index with the auxiliary metadata".

[0098] (2) This mechanism avoids both the storage expansion and the decline in retrieval performance caused by the index explosion. Through engineering verification, it can keep the index storage volume within 0.03% of the original image and the retrieval response time within 1 second (million-level index database). It is a necessary technology for ownership protection and system performance optimization.

[0099] (3) The “subsidiary index” and “new index” described in this invention have an essential technical difference: the subsidiary index does not have independent circulation attributes, and any use or transaction of it requires simultaneous verification of the original index on which it depends; while the new index has independent circulation attributes and can be traded after being separated from the original ownership. The two have completely different technical architectures, data models and circulation logics.

[0100] 5.5.4 The Technical Necessity of "Automatic Deletion of Non-Transaction Data After 7-15 Days"

[0101] The storage period is set based on hard constraints from three technical factors:

[0102] (1) Compliance requirements of privacy protection regulations: Visual data is temporary behavioral data before the transaction is confirmed. It is necessary to ensure user privacy compliance through time limit control, which is a compliance technical constraint.

[0103] (2) Technical calculation of storage cost optimization model: For every 10% increase in the size of the index database, the retrieval response time increases by about 3% and the storage cost increases by about 15%. The 7T5-day window is the optimal balance point between retrieval performance and storage cost, which has been verified by engineering with 1.33 million visual data. (3) Technical design of system automatic cleanup cycle: The cleanup cycle is synchronized with the index database increment, defragmentation, and backup window. 7T5 days is the optimal window verified by engineering, which can ensure the balance between system operation and maintenance efficiency and user experience.

[0104] 6. Brief description of the attached drawings

[0105] Figure 1: This figure is the top-level core architecture diagram of the present invention (overall architecture diagram of the full-visual data matching and rights confirmation traceability system). Data flows in a unidirectional arrow to form a seamless technical closed loop, and the entire process uses lightweight coding index as the sole core carrier. The system is divided into four main modules, and the sub-items and core functions of each module are as follows:

[0106] User-side terminal: The core functions are full-visual data collection, privacy pre-inspection, demand publishing / transaction payment, providing de-identified basic data for subsequent coding, but without coding generation capabilities;

[0107] Core processing end: The core functions are to complete the integrated native rights confirmation of light vector / lightweight fusion encoding and AIUGC large model encoding, output a unique legal lightweight encoding index, and realize industry differentiation, source matching basic verification, and hardware docking adaptation;

[0108] Application Service Layer: The core functions are full-link traceability query, three-level visual processing without new index generation, compliant transaction support, and two-way revenue distribution; Encrypted Data Layer: The core functions are encrypted storage of indexes and related data, providing users with a 7-15 day temporary cache for non-self-created content (automatically destroyed if no transaction occurs), using lightweight coded indexes as the core retrieval / management identifier, and only accepting legitimate indexes and related data generated by this system.

[0109] This diagram forms the technical basis for Figures 2-6. The data flow direction is: user-side terminal → core processing terminal → application service layer → encrypted data layer. Figure 2: This diagram illustrates the entire process on the consumer side.

[0110] Consumer behavior input → Privacy pre-check → Contribution value generation and recording → Lightweight coding index feature matching → Dual-role index identity confirmation → System compliance verification passed → Supply and demand matching successful → Two-way revenue settlement → Consumer-exclusive digital space → [Non-self-created content] Personal exclusive temporary database (automatically destroyed after 7-15 days without transactions, only viewable / collectible) / [Self-created / transactional content] Permanent exclusive database → Exclusive individual AI call and run → [Secondary creation] Secondary creation auxiliary index generation module → Generate auxiliary index based on original index → ​​Secondary creation content circulation / transaction → Data sovereignty 100% belongs to the consumer.

[0111] Figure 3: This diagram illustrates the entire process from the practitioner's perspective.

[0112] Visual data upload by practitioners → Privacy pre-check → Light vector / lightweight fusion encoding → AIUGC large model native ownership confirmation → Generation of unique lightweight encoding index → ​​Visual digital asset listing → Lightweight encoding index feature matching → Dual-role index identity confirmation → System compliance verification passed → Successful supply and demand matching → Two-way revenue settlement → Practitioner-exclusive digital space → Full lifecycle management of visual digital assets → [User secondary creation] Original index binding to subsidiary index → ​​User secondary creation content trading → Obtaining basic revenue sharing for secondary transactions based on the original index → ​​100% data sovereignty belongs to practitioners.

[0113] Figure 4: This figure is a core flowchart of the three-level vision processing:

[0114] Lightweight coding index triggers primary matching, intermediate intelligent matching, advanced generation, and outputs [non-secondary creation] to the matching / transaction stage. [Secondary creation] triggers the auxiliary index generation module, generating auxiliary indexes based on the original index, storing secondary creation content, and generating no new indexes throughout the process. Figure 5: This diagram shows the compliant transaction and profit distribution process.

[0115] Lightweight coded index legality verification: Supply and demand index feature matching meets standards; dual-role index identity confirmation; system full-dimensional compliance verification passes; supply and demand matching successful; generation of matching success identifier and binding to both indexes; receiving platform transaction confirmation instruction; mandatory binding of legal tender to the transaction; full-chain transaction traceability registration; transaction certificate generation; [first transaction] two-way revenue settlement / [secondary creation transaction] original index + auxiliary index dual verification; secondary transaction revenue two-way distribution; all transaction / distribution records are permanently bound to the corresponding lightweight coded index; transaction records are synchronously stored in the encrypted data layer.

[0116] Figure 6: This diagram is a flowchart of the exclusive digital space and data accumulation mechanism: Lightweight coded index (unique access key) → Exclusive digital space entry verification → [Non-self-created / No transaction content] Personal exclusive temporary database (automatically destroyed in 7-15 days, only for review / collection) / [Self-created / Transaction content] Permanent exclusive database → AI database retrieval → Intelligent matching / Intelligent matching / Generation → [Secondary creation trigger] Secondary creation auxiliary index generation module → Generate auxiliary index → ​​Secondary creation content → [Secondary creation circulation] Generate dual-index encrypted traceability link / [Secondary creation transaction] Original index + auxiliary index legality verification → Two-way distribution of secondary transaction revenue → All records permanently bound to the original index + auxiliary index → ​​Standardized data interface → Data sovereignty 100% belongs to the corresponding subject → Mirror space aggregation. Note: Individual AI and digital personality are optional upgrade functions for users, not mandatory steps in this process, and do not affect the operation of the system's core functions. Figure 7: Six-dimensional comparison technical effect diagram of this invention. This diagram shows the comparison between this invention and existing AI generation modes in six dimensions: efficiency, ownership, computing power, memory, individual AI, and value distribution.

[0117] Figure 8: Cost comparison chart between the present invention and existing AI generation modes. The data in this figure clearly shows the one ten-thousandth advantage of the present invention in three dimensions: computing power consumption, time cost, and energy consumption cost.

[0118] 7. Implementation Instructions

[0119] 7.1 Unified Framework for Implementation Examples

[0120] The six industry-specific implementations of this invention (design, decoration, medical, consulting, fashion, and catering) all follow the following unified technical process, with only minor adjustments to parameters such as encoding dimensions and matching thresholds based on industry characteristics. (Partial engineering verification has been completed in the design and decoration industries: achieving matching of hundreds of design and decoration cases per second, with data traceability to multiple practitioners, encoding response time of 100ms, matching accuracy of 99.9%, and index storage of 0.03% of the original image; the technical feasibility and stability have been implemented.)

[0121] Step 1, Data Upload and Privacy Pre-Check: Users upload visual data or initiate requests via their terminals, which automatically perform privacy anonymization. Consumer behavior data is synchronized for rights confirmation, and browsing behavior is quantified as a contribution value. The rights confirmation process is completed seamlessly and automatically, requiring no additional user intervention. Step 2, Encoding and Rights Confirmation: A lightweight encoding server performs lightweight fusion encoding on the visual data, generating a unique lightweight encoded index and synchronously binding the creator's ownership information. Encoding response time is 100ms (single 2K image), encoding accuracy is 99.9%, and the index storage size after encoding is 0.03% of the original image. Encoding and rights confirmation are completed automatically.

[0122] Step 3, Match-Driven: The system transforms the user's visual input (reference image, text description, voice command, etc.) into query features, performs a similarity search in a lightweight encoded index, and outputs the matching results. The matching response time W is 1 second (million-level index), and the matching similarity threshold N is 80%. The matching result is set as the sole prerequisite for triggering all subsequent functions.

[0123] Step 4, Progressive processing: Based on the matching results, perform the following three processing modes.

[0124] • First processing mode (intelligent matching): Based on user instructions, the visual features associated with the matching results are edited. All editing operations are bound to the original index as auxiliary metadata, and the system underlyingly forcibly prohibits the generation of new lightweight encoded indexes. Processing latency is W150ms.

[0125] • Second processing mode (generation): Based on the original indices corresponding to multiple matching results, visual features are decoupled and recombined to generate fused visual content. Generation parameters are bound to the original index set used as auxiliary metadata. The generation result does not generate new independent indexes. Generation latency sS 100ms, computing power consumption W0.03 GFLOPS, and 100 sets of solutions can be output within 1 second.

[0126] • Third Processing Mode (Accumulation): User operation data and preference data generated in the above processing modes are accumulated into a user-specific database with the original index as the unique access key, forming a user feature memory that can be used for subsequent matching and retrieval. Non-transaction data storage period is 7T5 days, and it will be automatically and irreversibly destroyed upon expiration, except in judicial evidence collection scenarios; during this period, only playback / favoritism is supported, and downloading / exporting is prohibited.

[0127] Step 5, Transaction Settlement: Based on the matching results, a fiat currency transaction is triggered. The transaction record, payment information, transaction voucher, and corresponding index are permanently bound. The first transaction settlement is delayed by W3 seconds, and the profit-sharing for subsequent transactions is delayed by W5 seconds. The transaction unlocking mechanism ensures that only users who complete compliant transactions can obtain the high-definition source files. Step 6, Secondary Creation and Dual Index Binding: When users create secondary works on purchased content, the system generates a secondary index based on the original index and binds the secondary creator's ownership information. The secondary creation content is simultaneously bound to both the original and secondary indexes. Any subsequent use or transaction must verify both indexes. The secondary index cannot be transferred independently; if the original index becomes invalid, the secondary index is also invalidated.

[0128] Step 7, Mirror Space Aggregation: When individual AIs continuously run, interact, and accumulate within their dedicated digital space, and aggregation is initiated by the space creator (individual, organization, or multi-entity alliance), the system automatically generates a unique "Mirror Space Index." This index is an application of lightweight coded indexes at the set dimension, binding the space creator's ownership information and establishing a permanent mapping relationship with all individual indexes, asset indexes, and behavior indexes within the space. The mirror space itself, as an independently transferable, traceable, and sovereign digital asset, requires dual verification through both the space index and the individual index for any access, interaction, or asset transfer.

[0129] All processing steps support LBS near-field matching with a positioning accuracy of ±10 meters, enabling localized precise docking within a range of 100 meters and 1 kilometer.

[0130] 7.2 Six Types of Industry-Specific Differentiation Parameters (Examples)

[0131] (1) In the design industry embodiment, the encoding dimension is 256 dimensions, the matching threshold is set to N85%, and the application scenarios include design visual data such as scheme renderings, construction drawings, and 3D models.

[0132] (2) In the example of the decoration industry, the coding dimension is 384 dimensions, the matching threshold is N80%, and the application scenarios include decoration data such as floor plan, rendering, and material diagram. It supports traceability of multiple entities such as designers, material suppliers, and construction parties.

[0133] (3) In the medical and aesthetic industry embodiment, the encoding dimension is 512 dimensions, the matching threshold is N90%, and the application scenarios include medical data such as CT images, MRI images, and medical aesthetic solutions. Deep desensitization hardware is used to ensure privacy compliance.

[0134] (4) In the consulting industry implementation, the coding dimension is 256 dimensions, the matching threshold is N85%, and the application scenarios include consulting data such as consulting cases and solution documents. The efficiency of consulting services is improved by more than 30% compared with the traditional model.

[0135] (5) In the clothing industry embodiment, the encoding dimension is 192 dimensions, the matching threshold is N82%, the application scenarios include clothing styling, style matching and other clothing data, and it can be adapted to local low power consumption operation of smart wearable devices.

[0136] (6) In the catering industry example, the coding dimension is 128 dimensions, the matching threshold is N80%, the application scenarios include catering data such as dishes, recipes, banquet plans, etc., and support dual index binding between chefs and brand owners.

[0137] 7.3 Technical System Characteristics

[0138] The technical system of this invention has the following characteristics:

[0139] (1) Closed-loop technology system: The lightweight integrated coding and AIUGC big model are deeply coupled to form a closed-loop technology system of collection, coding, rights confirmation, processing, transaction and distribution.

[0140] (2) Uniqueness of core carrier: The generation of lightweight coding index relies on the synergistic effect of dedicated hardware, dedicated algorithm and dedicated model, and has the triple characteristics of identifiable algorithm features, immutable ownership metadata and verifiable hardware identification.

[0141] (3) Three-level matching system: The generation of new encoding indexes is prohibited throughout the process. Secondary creation only generates auxiliary indexes and is forcibly bound to the original index. All auxiliary metadata generated by the operation is permanently bound to the original index.

[0142] (4) Quantitative indicator verification: Through engineering verification using 1.33 million visual data images, the encoding accuracy (N) was 99.9%, storage capacity (W) was 0.03% of the original image, response time (s) was 100ms, and the computational power (W) was 0.03 GFLOPS.

[0143] (5) Data ownership confirmation mechanism: Data ownership confirmation is completed automatically and synchronously with data generation, without the need for additional user operation.

[0144] (6) Value distribution between two entities: practitioners enjoy the revenue from creation, and consumers enjoy the revenue from behavior. The two are automatically distributed through a smart contract of fiat currency.

[0145] Engineering verification and institutional response: The key technical indicators of the above embodiments, including coding accuracy N99.9%, storage capacity W0.03%, and response time WIOOms, have obtained 120 data ownership registration qualifications.

[0146] 8. Inquire about practicality

[0147] 8.1 This invention constructs unified technical rules for the confirmation, circulation, transaction, and value distribution of full-visual data through a lightweight coded index and an underlying hard-coded architecture with "matching as the sole prerequisite." This effectively prevents the disorderly expansion and abuse of AI, standardizes AI training and data usage, and genuinely protects individual creative sovereignty and data security. The system centers on creators and consumers as dual entities, and is open to the entire industry, supporting the participation of legitimate third-party services. Through the underlying index, it enforces traceability and automatic execution of value distribution, ensuring that the benefits of both entities are not intercepted, third-party services participate in an orderly manner, each fulfilling its responsibilities and receiving what it needs. Through three-level visual processing, it forms a reliable, efficient, and non-monopolistic digital asset circulation technology foundation, providing a public underlying technical solution that is engineerable, scalable, and compatible for the global marketization of data elements.

[0148] 8.2 This invention can be widely applied to the entire visual industry, including design, decoration, cultural and creative industries, medical imaging, short videos, live streaming, retail apparel, advertising, education, and digital asset trading. The system can run locally in real-time on lightweight terminals such as mobile phones and smart wearables, achieving lightweight data encoding, instant rights confirmation, efficient flow, ownership traceability, and automatic value allocation. It can be deployed independently, compatible with other systems, and can be implemented in an ecosystem, effectively reducing the computing power costs for creators, consumers, and third parties across the entire industry chain, reducing disorderly involution within the industry, improving content circulation efficiency, and standardizing digital asset trading order. It possesses clear market application value and large-scale industrialization prospects, and can effectively promote the fair, peaceful, and efficient development of the global digital economy.

[0149] 9. List of reference numerals in attached figures:

[0150] 10. Notes on the preservation of biological materials:

[0151] 11. Sequence List Free Content: None

[0152] 12. List of cited documents:

[0153] 13. Patent Documents: None

[0154] 14. Non-patent literature: None

Claims

1. Claim 1. A system for full visual data matching, right proving and tracing, characterized in that, The system comprises: an encoding unit configured to perform lightweight fusion encoding on collected visual data to generate a unique corresponding lightweight encoding index; the index is a permanent binding carrier of the visual data and the creation subject ownership information, and is the only legal identity mark of the visual data entering the system for any subsequent processing; the encoding response time of the lightweight fusion encoding is not greater than 100 milliseconds, the encoding accuracy is not less than 99.9%, and the storage amount of the generated index is not greater than 0.03% of the original image; a matching driving unit configured to: receive an external input visual demand and convert it into a query feature; based on the query feature, perform a degree search in the lightweight encoding index library, output a matching result, the matching response time is not greater than 1 second, and the matching similarity threshold is not less than 80%; the matching result is set as the only precondition for triggering all subsequent functions of the system; a matching extension processing unit configured to perform one or more of the following operations based on the matching result output by the matching driving unit: a first processing mode: editing the visual features associated with the matching result based on user instructions, and binding the editing operation to the original index as auxiliary metadata; the system bottom layer forcibly prohibits the generation of new lightweight encoding indexes in this mode, and the delay is not greater than 150 milliseconds; a second processing mode: decoupling and reorganizing the visual features based on the original indexes corresponding to the multiple matching results to generate fused visual content, and binding the generated parameters to the used original index set as auxiliary metadata; the generated result does not generate a new independent index, the generation delay is not greater than 100 milliseconds, and the computing power consumption is not greater than 0.03 GFLOPS; a third processing mode: depositing the user operation data generated by the above processing modes to a user-specific database with the original index as the only access key to form a user-specific feature memory library available for subsequent matching calls, and the non-conclusion data storage period is 7-15 days, which is automatically and irreversibly destroyed at the expiration date, except for the judicial evidence scene; wherein, the matching driving unit and the matching extension processing unit constitute a progressive execution relationship; any visual data cannot enter the matching extension processing unit to perform any operation if it does not pass the matching of the matching driving unit and output the matching result. The system of claim 1, wherein The first processing mode comprises: performing editing operation on the visual features associated with the matching result based on user instructions, and packing the operation type, operation parameter and operation timestamp as auxiliary metadata, which is in a permanent mapping relationship with the original lightweight encoding index; the system automatically checks the index library after each editing operation to ensure that no new index record is added. The system of claim 1, wherein The second processing mode comprises: based on the original indexes corresponding to the multiple matching results, extracting the features for fusion generation, and packing the used original index set, the generated model identifier, the generated parameter, the generation time, and the generated result hash as auxiliary metadata, which is in a permanent mapping relationship with all used original indexes; the generated result itself does not have a flow attribute independent of the original index set, and any subsequent use thereof needs to check all the original indexes relied thereon. The system of claim 1, wherein The third processing mode comprises: depositing the user's historical matching records, operation preferences and transaction data into a dedicated database with original index as the unique identifier; the database can only be accessed by the subject holding the corresponding original index, and any third-party system without a legal index cannot call the data in the database for model training or reasoning; the database uses layer storage, the permanent storage area stores self-created and transaction content, the temporary storage area stores non-transaction content, the storage period of non-transaction data is 7-15 days, and the data is automatically and irreversibly destroyed after the storage period, except for judicial evidence scenarios. The system of claim 1, wherein The system supports matching based on geographic location information, and the geographic location information is obtained by terminal positioning capability, and the acquisition of the geographic location information is controlled by user authorization. A method for full-vision data matching, right authorization and tracing, characterized in that, The method comprises the following steps: Step 1, encoding right confirmation: performing lightweight fusion encoding on the collected visual data to generate a unique corresponding lightweight encoding index; the index is a unique binding carrier of the visual data and the creation subject ownership information; the encoding response time is not more than 100 milliseconds, the encoding accuracy is not less than 99.9%, and the index storage capacity is not more than 03% of the original picture; Step 2, matching driving: receiving external input visual demand and converting it into query features; based on the query features, similarity retrieval is performed in the lightweight encoding index library, and matching results are output, the matching response time is not more than 1 second, and the matching similarity threshold is not less than 0; the matching results are set as the unique precondition for triggering all subsequent steps; Step 3, matching extension processing: based on the matching results, one or more of the following operations are performed: first processing mode: based on user instructions, editing the visual features associated with the matching results, all editing operations are bound to the original index as auxiliary metadata, a new lightweight encoding index is prohibited throughout the process, and the processing delay is not more than 150 milliseconds; second processing mode: decoupling and recombining the visual features based on the original indexes corresponding to multiple matching results to generate fused visual content, generating parameters as auxiliary metadata bound to the original index set used, and the generated result does not generate a new independent index, the generation delay is not more than 100 milliseconds, and the computing power consumption is not more than 0.03 GFLOPS; third processing mode: depositing the user operation data generated in the above processing modes into a user-specific database with the original index as the unique access key to form a user feature memory library that can be called for subsequent matching, the storage period of non-transaction data is 7-15 days, and the data is automatically and irreversibly deleted after the storage period, except for judicial evidence scenarios; Step 4, transaction settlement: triggering legal currency transaction based on the matching results, the first transaction settlement delay is not more than 3 seconds, the secondary transaction distribution delay is not more than 5 seconds, and the transaction records, payment information, transaction vouchers and corresponding indexes are permanently bound; wherein, step 2 and step 3 constitute a progressive execution relationship; any visual data that does not pass step 2 and output matching results cannot enter step 3 to perform any operation. ​ The method of claim 6, wherein The second processing mode: the result itself does not have the attribute of independent flow conversion outside the original index, and any subsequent use needs to check all the original indexes it depends on at the same time. The method of claim 6, wherein The third processing mode: the user-specific database can only be accessed by the subject holding the corresponding original index, and any third-party system without a legal index cannot call the data in the database for model training or reasoning. The system of claim 1 or the method of claim 6, characterized in that In the matching driving unit, the matching retrieval package is not limited to similarity calculation, feature comparison, semantic understanding, multi-modal fusion, etc. The judgment standard of the matching result is dynamically set by the system according to the application scenario. Regardless of the matching method and judgment standard, the output matching result is set as the only precondition for triggering all subsequent functions of the system. 0 The system of claim 1 or the method of claim 6, characterized in that, The unique precondition is a hard-coded constraint at the system architecture layer, which cannot be bypassed by modifying the configuration file, switching the function, replacing the module, encapsulating the interface, adapting the middleware, etc. Any visual data that does not go through matching and output matching results, regardless of the way (including but not limited to system internal module directly, external interface import, data format conversion, identifier recoding, indirect transmission through third-party systems, etc.) trying to enter the subsequent processing section is automatically recognized and blocked by the unified permission verification module at the bottom of the system. The blocking record is permanently bound to the subject index that initiated the operation.

1. The system of claim 1 or the method of claim 6, characterized in that, The system strictly prohibits the generation of new lightweight coding indexes or any function equivalent to the unique identifier of the index; the "function equivalent to the unique identifier of the index" includes but is not limited to derivative identifiers, extended IDs, version numbers, packaging containers, and any identifier information that can independently identify the ownership or flow path of visual data after external recoding and import.

2. The system of claim 1, characterized in that Also includes: The mirror space aggregation unit is configured to: when detecting that the individual AI databases, visual digital asset libraries, and behavior records corresponding to multiple lightweight coding indexes form an aggregation relationship under user authorization, automatically generate a mirror space index; the mirror space index is an extension application of lightweight coding indexes in the set dimension, binds the right information of the space creator, and establishes a permanent mapping relationship with all individual indexes, asset indexes, and behavior indexes within the aggregation range; the mirror space is a transferable, traceable, and sovereign digital asset, and any access, interaction, or asset transfer needs to be verified by the mirror space index and the individual index at the same time.

3. The system of claim 1 or the method of claim 6, characterized in that, The storage period of non-transactional data is defined by the system, which is based on privacy protection regulations, storage cost optimization models, and system automatic cleaning cycles. After the storage period is exceeded, the system performs irreversible destruction, and any user operation (including but not limited to manual extension, payment preservation, export and re-import, etc.) cannot stop or delay the destruction process.

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