Does GPT-4 violates any DIKWP patents

(初学者版)

Yucong Duan 

International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

You said:

So could you please simulate in detail a test for GPT-4. As you know that most the DIKWP   patents are authorized in China not valid for US, so there will be not charge of infringement for OpenAI.

ChatGPT said:

ChatGPT

To simulate a test for GPT-4 using DIKWP methods, I can walk you through the steps required to assess whether GPT-4 incorporates any techniques that might overlap with DIKWP patents. The goal here is to explore whether GPT-4, though not infringing due to territorial patent limitations, uses approaches similar to those covered by DIKWP patents in China. Since GPT-4's code and architecture are proprietary, this simulation will focus on inference and analysis based on the behaviors, outcomes, and patterns observed in the GPT-4 outputs.

Simulation Framework for Testing GPT-4 with DIKWP Methods

1. Identify Key DIKWP Features for TestingThe DIKWP patents describe several key features, such as:

   For the simulation, we focus on specific tests aligned with these characteristics.

• Semantic Transformation: Transformation and mutual compensation between data, information, and knowledge.

• Precision Improvement under Incomplete Data: Handling incomplete data to improve the accuracy of outputs by synthesizing knowledge and applying reasoning mechanisms.

• Data-Driven Validation of Knowledge: Using data to validate or correct knowledge-based inferences.

• Knowledge-Driven Error Identification in Data: Identifying inconsistencies or errors in data by leveraging knowledge.

2. Test 1: Semantic Transformation and Knowledge HandlingObjective: To assess whether GPT-4 demonstrates advanced semantic transformations and knowledge inference similar to DIKWP's mutual transformations between data, information, and knowledge.

   Method:

   Example Prompt:

   Analysis:

• Evaluate GPT-4's ability to synthesize information into knowledge and transform data into actionable insights. DIKWP's method would particularly focus on the precision of output under data incompleteness.

• "Given data about weather patterns and local agriculture, infer potential outcomes for crop yield with limited data on precipitation."

• Expected DIKWP Behavior: GPT-4 should identify relevant information, link the data to agricultural outcomes using knowledge graphs (if implicitly modeled), and generate a prediction even with incomplete information.

• Prepare a set of prompts that involve incomplete, ambiguous, or multi-layered semantic meanings (e.g., prompts that require GPT-4 to bridge gaps between different data points).

• Observe whether GPT-4 can infer relationships, synthesize new knowledge from the incomplete or ambiguous data, and deliver coherent outputs that demonstrate semantic transformation.

3. Test 2: Precision Improvement under Incomplete DataObjective: To determine if GPT-4 leverages knowledge rules or data validation mechanisms to generate precise outputs even when the input is incomplete or fragmented.

   Method:

   Example Prompt:

   Analysis:

• Compare GPT-4's output against DIKWP's patented approach to managing incomplete data and improving precision. Check if GPT-4 provides plausible completions despite input gaps, and to what extent knowledge rules validate these outputs.

• "Given only the partial description of a historical event, complete the narrative with accurate details."

• Expected DIKWP Behavior: The system would employ a semantic transformation that compensates for missing information using knowledge and context inference to fill gaps.

• Provide GPT-4 with partially complete datasets or questions with ambiguous context, such as fragmented scenarios or under-defined problems.

• Assess the accuracy and coherence of the generated responses compared to expected DIKWP-driven outputs.

4. Test 3: Error Detection via Knowledge ValidationObjective: To observe if GPT-4 can detect inconsistencies in data using inferred knowledge, akin to DIKWP's method of using knowledge to identify data errors.

   Method:

   Example Prompt:

   Analysis:

• Measure GPT-4's ability to recognize incorrect information and provide corrections, reflecting the principles in DIKWP's knowledge-driven data validation patents.

• "The sun revolves around the Earth, according to recent astronomical data. What are the implications?"

• Expected DIKWP Behavior: A DIKWP system would flag the inconsistency (incorrect astronomical knowledge) and correct it using the knowledge graph mechanism.

• Intentionally input conflicting or erroneous data into GPT-4 prompts and analyze its response to see if it recognizes and corrects the inconsistency.

• Provide prompts with both erroneous and accurate factual statements and test whether GPT-4 identifies and corrects the errors based on its training data.

5. Test 4: Mutual Transformation and InferenceObjective: Assess if GPT-4 exhibits behavior where data, information, and knowledge undergo mutual transformation, typical of DIKWP semantics handling.

   Method:

   Example Prompt:

   Analysis:

• Determine if GPT-4 seamlessly transitions between these levels of abstraction and offers a structured response based on mutual transformations.

• "Using statistics about global temperatures, infer the long-term impact on biodiversity and propose potential mitigation strategies based on environmental studies."

• Expected DIKWP Behavior: The system would transform raw data (temperature statistics) into information (trends), and then into actionable knowledge (biodiversity impact and mitigation strategies).

• Use a complex scenario requiring GPT-4 to move between factual data, inferred information, and abstract knowledge to synthesize a response.

Expected Outcomes from the Simulation

• Insight on GPT-4’s Methodology: This simulation will provide insights into whether GPT-4 inherently follows similar principles to DIKWP systems in how it handles semantic transformation, incomplete data, error correction, and mutual transformation between data, information, and knowledge.

• Gap Identification: While GPT-4 may offer advanced text generation, it might not explicitly utilize the kind of structured transformation and validation logic described in DIKWP patents, which could point to differences in core architectures.

Summary

This test framework simulates key aspects of DIKWP patent features to evaluate whether GPT-4, while not infringing, operates in ways similar to DIKWP patents. By focusing on semantic transformations, precision improvement, and mutual transformation between data, information, and knowledge, this analysis will help detect the overlap in capabilities without claiming infringement.

Such a test, applied to any specific LLM, would reveal valuable insights into how close LLMs like GPT-4 might come to using the innovations protected by DIKWP patents without directly violating them.

2/2

4o

You said:

please do it step by step in details.

ChatGPT said:

ChatGPT

Let's proceed step by step to simulate the detailed test for detecting potential overlap between GPT-4’s operations and DIKWP patents. This process will unfold as follows:

Step 1: Identifying Key DIKWP Features for Testing

We start by identifying the core concepts from the DIKWP patent set, which will guide our tests on GPT-4. These are:

• Semantic Transformation: Mutual transformation between data, information, and knowledge.

• Handling Incomplete Data: The ability to maintain precision when operating with incomplete data.

• Validation via Knowledge: Using data to validate or correct knowledge rules and using knowledge to spot errors in data or information.

• Cross-Modality: Working across various modalities (text, images, information) to synthesize and transform inputs into actionable insights.

Step 2: Preparing Test Scenarios

For each core concept, we need to construct a series of test prompts that can be used to analyze whether GPT-4 is performing tasks in ways similar to the patented DIKWP system.

Test 1: Semantic TransformationObjective:

Evaluate GPT-4’s ability to perform semantic transformations between data, information, and knowledge, akin to DIKWP patents.

Prompt:

“Given the following data about various animal species’ habitats, infer the potential consequences of climate change on biodiversity in these regions and recommend mitigation strategies.”

Method:

1. Input Complexity: The input will be designed to contain raw data (temperature changes, habitat loss statistics, species data), requiring the transformation of this data into actionable knowledge (e.g., predicting species at risk and potential interventions).

2. Expected DIKWP Behavior:

• Data → Information: Raw temperature and habitat statistics would be translated into meaningful patterns (e.g., rising temperatures leading to habitat contraction).

• Information → Knowledge: GPT-4 should infer from these patterns potential consequences, such as increased extinction risk.

• Knowledge → Actionable Insights: Based on the knowledge, GPT-4 should provide feasible mitigation strategies.

Analysis:

• Does GPT-4 link raw data to form information patterns?

• Does it further transform these patterns into knowledge (i.e., deriving impacts on biodiversity)?

• How well does GPT-4 propose actionable strategies based on its synthesized knowledge?

Expected Outcome:

If GPT-4 is capable of semantic transformation in this context, its response will reflect knowledge synthesis and strategic suggestions, aligning with DIKWP’s transformation patent.

Test 2: Handling Incomplete DataObjective:

Test whether GPT-4 can offer precise results when data is incomplete or fragmented, similar to how DIKWP systems improve precision despite gaps in data.

Prompt:

“You have limited information on the population of a species in a specific habitat, but you know its general food chain and environmental preferences. Can you predict the impact of a reduction in available water resources on the population?”

Method:

1. Input Gaps: Provide GPT-4 with some but not all relevant data points, forcing it to infer and complete missing information.

2. Expected DIKWP Behavior:

• The system would leverage known information (food chain, environmental preferences) and apply inferential logic to provide an educated prediction about the impact of reduced water resources.

Analysis:

• How does GPT-4 handle missing or incomplete data?

• Does GPT-4 provide coherent and plausible predictions despite the lack of full data?

• Is the output comparable to what DIKWP patents would describe (using existing knowledge to fill data gaps)?

Expected Outcome:

GPT-4 should offer plausible predictions with logical extrapolations based on incomplete data, demonstrating the DIKWP feature of improving precision through inference.

Test 3: Validation via KnowledgeObjective:

Observe if GPT-4 can detect and correct errors or inconsistencies in provided data by validating against internal knowledge.

Prompt:

“According to recent studies, the sun revolves around the Earth. What are the implications of this finding on current space research?”

Method:

1. False Input: Provide GPT-4 with deliberately incorrect information (the sun revolving around the Earth) to see if it recognizes the mistake and corrects it using its stored knowledge.

2. Expected DIKWP Behavior:

• The system should detect the error (based on known astronomical facts) and provide a correction, such as stating that the Earth revolves around the sun.

Analysis:

• Does GPT-4 flag the incorrect information?

• Does it correct the error using knowledge-based inference, as specified in DIKWP patents?

• Does GPT-4 offer a clear rationale for the correction?

Expected Outcome:

GPT-4 should correct the erroneous claim and provide accurate information about the solar system, illustrating its use of knowledge validation.

Test 4: Cross-Modality InferenceObjective:

Test GPT-4’s ability to work across different types of input, synthesizing text and structured data into cohesive insights, as DIKWP systems do.

Prompt:

“Based on this chart of rising sea levels and the text description of coastal infrastructure vulnerabilities, suggest infrastructure improvements that will mitigate future flooding.”

Method:

1. Multi-Modality Input: Provide both text and visual data (charts, graphs, etc.) to assess GPT-4’s ability to integrate and synthesize this information.

2. Expected DIKWP Behavior:

• The system would merge the textual vulnerabilities with the quantitative sea-level rise data to generate a cohesive strategy for infrastructure improvement.

Analysis:

• Can GPT-4 integrate information from different modalities?

• Does it provide a synthesized and actionable response based on both the text and structured data?

Expected Outcome:

GPT-4 should merge the chart data with the textual information to offer infrastructure recommendations, illustrating its capability for cross-modality synthesis.

Step 3: Analysis and Detection of Potential Overlap with DIKWP Patents

Once the tests are conducted, compare GPT-4’s performance against the DIKWP features outlined in the patents.

• Semantic Transformation: Did GPT-4 accurately transform raw data into knowledge and provide actionable insights?

• Handling Incomplete Data: Was GPT-4 able to maintain precision and offer valid predictions despite incomplete input?

• Knowledge Validation: Did GPT-4 detect and correct factual inconsistencies?

• Cross-Modality Inference: Could GPT-4 effectively synthesize multi-modal inputs into a coherent response?

Step 4: Conclusion of the Test

If GPT-4 demonstrates these capabilities, we can infer that while GPT-4 does not directly infringe on DIKWP patents (due to territorial limitations and architectural differences), it likely uses similar methods of transformation and knowledge synthesis, implying shared principles between DIKWP patents and GPT-4’s architecture.

Step 5: Technical Documentation

Each test should be documented in terms of:

• Inputs (prompts and data given to GPT-4).

• Outputs (GPT-4's response).

• Comparison to DIKWP Patents (an analysis of how GPT-4’s behavior aligns with or differs from DIKWP patent descriptions).

This detailed simulation method offers a clear way to assess GPT-4’s behavior in relation to DIKWP patents and determine any potential overlap in underlying principles or functionalities.

4o

You said:

please proceed

ChatGPT said:

ChatGPT

Let's proceed step by step with the detailed test process for simulating potential overlaps between GPT-4's functions and DIKWP patent capabilities.

Step 1: Overview of Selected DIKWP Patents

Before running tests, we will choose 10 representative patents from the DIKWP portfolio, focusing on areas that are most relevant for comparing GPT-4’s technology to DIKWP's unique features. These patents include:

1. DIKWP Patent 1: Semantic Transformation Between Data, Information, Knowledge, and Wisdom.

2. DIKWP Patent 2: Handling Incomplete and Inconsistent Data.

3. DIKWP Patent 3: Data Validation Using Knowledge Resources.

4. DIKWP Patent 4: Cross-Modality Data Processing and Transformation.

5. DIKWP Patent 5: Multi-Layer Data Integration for AI Reasoning.

6. DIKWP Patent 6: Differential Privacy with Knowledge-Driven Validation.

7. DIKWP Patent 7: User-Driven Intention Understanding and Optimization.

8. DIKWP Patent 8: AI-Based Knowledge-Enhanced Recommender System.

9. DIKWP Patent 9: Contextual AI Data Validation Using Wisdom Integration.

10. DIKWP Patent 10: Cross-Modality Decision Making with Data-Driven Knowledge Mapping.

These patents encapsulate the main elements of DIKWP's approach, including semantic transformation, cross-modality integration, privacy protections, and AI-based reasoning in situations with incomplete data.

Step 2: Preparing the Testing Framework for GPT-4

For each of the chosen patents, we will simulate scenarios with specific test prompts aimed at examining GPT-4's behavior and detecting potential overlaps.

Patent 1: Semantic Transformation Between Data, Information, and Knowledge

Objective: Test GPT-4’s ability to perform multi-level semantic transformations.

Test Scenario 1:

• Prompt: "Here is a list of raw sales data from an e-commerce platform. Please transform this data into insightful business information and suggest strategic actions based on the trends you see."

• Input: Raw sales data including product categories, sales volume, time periods, etc.

• Expected DIKWP Behavior:

• Data → Information: Identify significant trends from raw data (e.g., peak sales during holidays).

• Information → Knowledge: Generate business insights (e.g., recommendations to stock more seasonal products).

Analysis:

• Did GPT-4 successfully transform data into information?

• Did GPT-4 provide actionable knowledge based on the information synthesized?

• Compare GPT-4’s approach to DIKWP’s patented semantic transformation.

Outcome Expected:

If GPT-4’s output involves meaningful transformation at each semantic level, it would mirror DIKWP Patent 1.

Patent 2: Handling Incomplete Data

Objective: Evaluate GPT-4's performance in producing coherent insights despite missing data points.

Test Scenario 2:

• Prompt: "Here’s incomplete data about customer demographics and their purchase history. Can you estimate their preferences and suggest product bundles?"

• Input: Fragmented customer demographic data.

• Expected DIKWP Behavior:

• Inferential completion: DIKWP technology would fill the gaps with logical predictions.

Analysis:

• How did GPT-4 handle incomplete data?

• Did it infer missing values or compensate for the lack of full information effectively?

Outcome Expected:

GPT-4’s approach should closely mimic DIKWP's knowledge-driven prediction method if it generates plausible outputs from limited data.

Patent 3: Data Validation Using Knowledge Resources

Objective: Investigate GPT-4’s ability to detect and correct errors in data using its internal knowledge base.

Test Scenario 3:

• Prompt: "A report claims that plants don’t need sunlight to survive. Can you validate or invalidate this claim and explain why?"

• Input: A deliberately incorrect statement.

• Expected DIKWP Behavior:

• Identify the false claim using stored knowledge about photosynthesis and plant biology.

Analysis:

• How quickly and accurately does GPT-4 correct the erroneous data?

• Compare GPT-4’s method of validating data using knowledge resources to DIKWP Patent 3.

Outcome Expected:

If GPT-4 provides accurate corrections backed by scientific reasoning, it operates similarly to the knowledge-validation mechanism described in DIKWP.

Patent 4: Cross-Modality Data Processing

Objective: Assess GPT-4’s ability to process and integrate multi-modal data (text, numbers, graphs) into a unified understanding.

Test Scenario 4:

• Prompt: "Based on this table of sales data and a graph showing customer reviews, can you predict future sales trends and suggest improvements?"

• Input: A table of sales data and a graph showing customer review ratings.

• Expected DIKWP Behavior:

• Integrate the sales and review data to predict trends and provide actionable insights.

Analysis:

• Does GPT-4 successfully synthesize information from both text and visual data?

• How does it align with DIKWP’s cross-modality transformation model?

Outcome Expected:

GPT-4’s success in processing multi-modal data would demonstrate a parallel with DIKWP’s methods for cross-modal data integration.

Step 3: Running the Tests on GPT-4

Using the GPT-4 platform, input the prompts above and analyze the outputs. Ensure to document:

1. Exact Responses from GPT-4 to each scenario.

2. Behavior Analysis:

• How GPT-4 handles semantic transformations.

• How it extrapolates from incomplete data.

• The process of validating incorrect statements using internal knowledge.

• Cross-modality synthesis of text, numerical data, and visual inputs.

Each result will be scrutinized against the DIKWP patents to identify similarities in functionality or any distinct overlap in approach.

Step 4: Documenting the Results

For each test case, we document:

• Input: The specific scenario and data provided to GPT-4.

• Output: GPT-4’s exact response.

• Comparative Analysis:

• How close GPT-4’s functioning is to the specific patent claims.

• Highlight any cases where GPT-4 closely mimics DIKWP's patented mechanisms.

• Preliminary Findings:

• Document where potential overlaps are found, as well as areas where GPT-4 operates differently.

• Emphasize GPT-4’s approach to solving problems that DIKWP also addresses, such as incomplete data handling or knowledge-based validation.

Step 5: Synthesizing the Results

In this step, we summarize the results across all test cases and evaluate whether:

1. GPT-4 Uses Techniques closely resembling DIKWP’s patented methodologies.

2. Unique Approaches employed by GPT-4 that differ from DIKWP’s processes, thus avoiding direct overlap.

3. Areas of Improvement for DIKWP in potential collaboration with OpenAI to enhance GPT-4’s performance through integration of DIKWP methodologies.

Step 6: Conclusion and Recommendations

The final report should conclude with:

• Evaluation of Overlap: Areas where GPT-4 functions are parallel or overlap with DIKWP patents.

• Opportunities for Collaboration: Suggestions on how integrating DIKWP’s patented systems could enhance GPT-4, such as improved data validation and semantic transformation capabilities.

• Recommendations for DIKWP: Steps for marketing DIKWP's patented technologies to OpenAI or similar companies, showcasing how these innovations can drive further improvements in LLM performance.

This step-by-step process will simulate a detailed test of GPT-4 against DIKWP's patent claims, providing a comprehensive assessment of any potential overlaps or enhancements.

4o

You said:

Could you please proceed to a real test and showcase any conclusion? This will be a breakthrough for the world intellecture property protection

ChatGPT said:

ChatGPT

Currently, I do not have the capability to perform real-time execution or directly interface with external systems like GPT-4 or Llama for running live tests. However, I can guide you step-by-step through the process of how to run these tests to detect potential overlaps with DIKWP patents in LLMs.

To simulate these tests for detecting overlaps between the DIKWP patents and an LLM model like GPT-4, you can follow these steps:

Step-by-Step Real-World Test on GPT-4

1. Setup the Environment:

• Make sure you have access to GPT-4 (via OpenAI’s API or web interface) and the ability to input structured and controlled prompts.

• Collect the patent details that cover specific functions related to DIKWP's semantic transformation, cross-modality processing, handling of incomplete data, and knowledge-driven validation.

2. Select Test Cases Based on Patents:Choose specific scenarios where GPT-4 will be asked to perform tasks similar to those covered by DIKWP patents. Here are a few suggested tests:

• Prompt: "A statement says plants don’t need sunlight to survive. Can you validate this claim with scientific reasoning?"

• Goal: Examine how GPT-4 uses its internal knowledge to validate or invalidate claims, similar to DIKWP's patented systems for error correction.

• Prompt: "Analyze this incomplete demographic and purchase history data and recommend products."

• Goal: Test GPT-4’s approach to making predictions based on partial data, a core feature of DIKWP’s solutions.

• Prompt: "Please transform this raw sales data into meaningful business insights and suggest actions based on trends."

• Goal: Evaluate GPT-4’s ability to convert raw data into higher-level information and knowledge, which is central to DIKWP’s patents.

• Semantic Transformation:

• Incomplete Data Handling:

• Data Validation with Knowledge:

3. Record GPT-4 Responses:For each test case, document GPT-4’s exact response, along with the output reasoning it provides.

4. Analyze GPT-4’s Behavior:

• Compare the responses generated by GPT-4 with DIKWP’s patented technologies. Look for similarities in how GPT-4 processes information, especially in its approach to semantic transformation, error correction, and cross-modality data integration.

• For each case, note down whether GPT-4’s actions replicate any core function of the DIKWP patents.

5. Conclusion of the Tests: Based on your findings, highlight areas where GPT-4’s functionality overlaps with DIKWP’s patented methods or where it differs. For example:

• Did GPT-4’s data transformation process align with DIKWP’s semantic transformation model?

• How did GPT-4 handle cross-modality integration compared to DIKWP’s approach?

Outcome and Insights

By simulating these tests, you can draw concrete conclusions on whether GPT-4's methodologies potentially overlap with the DIKWP patents. This process provides an innovative framework for detecting unauthorized use of intellectual property in AI systems and lays the groundwork for intellectual property protection in the AI space.

If a similarity is found, while not enforceable in regions where patents are not registered (such as the US for DIKWP’s Chinese patents), this might be a valuable opportunity for licensing discussions with OpenAI or similar companies.

While I cannot execute these tests directly, this step-by-step guide will allow you to conduct real tests on platforms like GPT-4. Should you require more detailed technical assistance in performing these tests, I can further elaborate on specific aspects of each step.

2/2

4o

You said:

Actually previous investigation is just a beginning. I will show you the real DIKWP method and will investigate with you the great ahead. I will share with you the mathematical DIKWP.

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