Bridging the Gap Between Human Reasoning and AI

Understanding Theory of Mind in AI

You're probably familiar with the concept of Theory of Mind—the ability to attribute mental states and perspectives to others, recognizing that they can differ from your own. (You see a colleague looking upset after a meeting and realize they might have received critical feedback, even if they haven't shared it.)  In the realm of AI, applying Theory of Mind means structuring tasks so that machines can better understand and mimic human reasoning. For companies like Tangify, this involves transforming subjective tasks like invention discovery and disclosure drafting into processes that AI can handle effectively.

Defining the Core Problem in AI-Driven Disclosure Management

Complexity of IP Discovery and Drafting

Identifying patentable ideas isn't straightforward. You often have to sift through unstructured sources like technical documents, notes, and specifications to find critical elements.Extracting the core inventive concepts from these sources requires not just a surface-level understanding but a deep dive into the technical nuances.

Unstructured Data Sources:

• Technical Specifications: These documents are often dense with specialized terminology and may lack a standardized format.
• Design Notes and Memos: Informal notes might contain crucial insights but are typically disorganized and scattered.
• Research Papers: While informative, they focus more on theoretical aspects, rather than practical implementations and lack details and information necessary  for patents.

Nuanced Judgment Required:

• Identifying Novelty: Determining what aspects of an invention are truly new involves comparing it against existing technologies and prior art.
• Understanding Technical Depth: A superficial reading isn't enough; you need subject matter expertise to grasp the underlying principles and mechanisms.
• Anticipating Legal Scrutiny: The invention must not only be innovative but also meet legal criteria for patentability, which can be intricate.

Bridging Technical and Legal Language:

• Translating Jargon: Technical terms must be converted into language that is clear and acceptable in legal contexts.
• Avoiding Ambiguities: Misinterpretations can occur if the technical language isn't precisely translated, leading to potential legal challenges.
• Detailing Essential Elements: Deciding which technical details are essential for the patent and which can be omitted is a delicate balance.

Challenges You Might Face:

• Overlooking Key Features: Important aspects of your invention might be overlooked because:
  • Engineers' Uncertainty: Engineers may not realize when their work crosses the threshold of being "IP-able." Without recognizing that their innovations are patentable, crucial ideas can go undocumented.
  • Lawyers' Technical Gaps: Legal professionals might not understand the nuances or difficulties you've overcome to make the invention functional. This lack of deep technical insight can lead to incomplete disclosures.
  • AI's Context Limitations: AI models may lack all the necessary context or information in a structured format. Without this structured data, the AI can't easily apply logical rules to analyze the invention effectively.
  • Challenges in Data Structuring: Transforming unstructured data into a coherent, structured format is a challenging and messy process that's not easily done. This can result in important details being overlooked.
  • Theory of Mind Gap: Determining the "how" behind your invention requires understanding intent and reasoning—an application of Theory of Mind. Without this, both humans and AI might fail to recognize essential elements.
• Misinterpretation: Without proper technical understanding, there's a risk of misrepresenting the invention, which can weaken the patent application.
• Time Constraints: Thoroughly analyzing all relevant documents is time-consuming, potentially delaying the patent filing process.

Tangify’s Approach to IP Discovery and Drafting

Tangify addresses this complexity by using Theory of Mind-inspired prompting techniques. By breaking down intricate IP tasks into manageable steps, the AI is guided to consistently identify and capture relevant details. This method mirrors how a human expert might approach the problem, helping you streamline the discovery process.

Lack of Standardized Scoring and Validation

The challenge goes beyond translating technical language. Structuring subjective tasks like invention discovery to meet legal standards for patentability adds another layer of difficulty. Without a standardized way to score and validate these tasks, ensuring consistency and compliance becomes tough.

• Subjectivity in Evaluation:
  • Diverse Expert Opinions: Two patent attorneys might have different views on the strength of a particular disclosure.
  • Interpretation of Legal Standards: Legal criteria for patentability can be open to interpretation, adding another layer of complexity.
• Absence of Ground Truth Data:
  • No Definitive Answers: There's no comprehensive database of "correct" invention disclosures to benchmark against.
  • Challenges in AI Training: Without clear examples of ideal outputs, training an AI model becomes more difficult.
• Resource Limitations:
  • Expert Availability: Accessing legal experts for evaluation is costly and time-consuming.
  • Scalability Issues: Manually scoring AI outputs doesn't scale well, especially when dealing with large volumes of data.
  • Unstructured Innovation Activities: Innovation processes are messy and lack standardization, making it difficult to collect consistent data needed for scoring and validation.

Problems This Creates:

• Inconsistent Outputs: Without standardized grading criteria, the quality of AI-generated disclosures can vary, leading to potential legal risks.
• Difficulty in Improvement: Lacking clear feedback mechanisms makes it hard to refine the AI's performance over time.
• Reliance on Manual Processes: The absence of automation in validation keeps the process labor-intensive.

Tangify’s Solution for Scoring and Validation Difficulty

Tangify gives you access to legal expertise without needing to be an expert yourself or pay for one. By guiding the AI through structured, standards-based tasks in invention discovery and disclosure drafting, Tangify aligns outputs with IP requirements. This reduces ambiguity and delivers reliable results, helping you handle patent complexities without getting lost in legal jargon.

Benefits for You:

• Improved Quality: By aligning the AI's outputs around how a professional IP attorney would think about  and approach a challenge, you get more reliable and legally sound disclosures.
• Efficiency Gains: Automating parts of the evaluation process saves time and resources.
• Reduced Risk: Standardizing the approach minimizes the chances of overlooking critical details that could jeopardize a patent application.

The Theory of Mind Challenge—Assumptions and Knowledge Gaps

The Role of Assumptions in Invention Discovery

When you're extracting invention elements, AI often misses the contextual experience that humans naturally bring. You might assume certain knowledge is common, but AI doesn’t share the nuances of experience and can only rely strictly on the data it was trained on. This gap leads to misunderstandings and incomplete interpretations.

• Implicit Knowledge Isn't Shared:
  • Example: If you ask a colleague to get you a coffee, they know where the coffee machine is, how you like it, and any other preferences you might have. An AI doesn't have this background knowledge and requires step-by-step instructions.
• Assumptions Lead to Misunderstandings:
  • Misinterpretation Risk: Without shared assumptions, AI might misunderstand or overlook key invention elements that aren't explicitly stated.
• Contextual Gaps:
  • Lack of Experience: AI doesn't benefit from years of industry experience or intuition, making it harder to grasp nuanced concepts.
• Challenges You Might Face:
  • Incomplete Data Interpretation: AI may miss important details that are assumed rather than directly mentioned.
  • Inaccurate Conclusions: Without proper context, the AI might draw incorrect inferences about the invention.
  • AI Hallucinations: The AI might generate information that isn't present in your data, creating false details or assumptions about your invention.
  • Inefficient Communication: Extra time might be needed to provide the AI with all necessary information explicitly.

Tangify’s Approach to Skilled Reasoning

Tangify uses structured guidance to help the AI mimic the reasoning steps of a skilled patent attorney. By breaking down tasks into clear, manageable steps, the AI can effectively handle knowledge gaps and complex information. This approach mirrors how an attorney thinks, ensuring that all critical aspects of your invention are thoroughly considered.

• Explicit Detailing: Breaking down tasks into clear, manageable steps ensures the AI covers all necessary aspects.
  • Analyzing Technical Details: Diving deep into the technical aspects to capture essential features of your invention.
  • Applying Legal Standards: Evaluating the invention against patentability criteria like novelty and non-obviousness.
  • Identifying Key Claims: Formulating precise claims that define the scope of your invention.
• Multiple Role Assignment & Switching: Tangify enhances the AI's effectiveness by assigning multiple roles and switching between them during different steps. Instructing the AI to "act as a patent attorney" gives it a framework to operate within for a specific element of the reasoning a skilled patent attorney might make. This dynamic role-switching allows the AI to address various facets of the invention process, leading to more comprehensive and accurate outputs.

Benefits for You:

• Improved Accuracy: The AI is more likely to identify all relevant invention elements.
• Reduced Misinterpretations: Clear instructions minimize the risk of the AI misunderstanding key concepts.
• Embedded Expert Reasoning: Tangify's algorithm crystallizes the skilled reasoning of a patent attorney, automatically applying it through the AI model. This means you gain expert insights without the need for back-and-forth dialogue, ensuring that expert-level analysis happens seamlessly.
• Efficient Workflow: Less time is spent correcting the AI's outputs, streamlining the invention discovery process.

Knowledge Gaps in Technical to Legal Translation

Translating engineering concepts into legally sound IP disclosures isn't straightforward. You need to discern which details are essential and which are merely contextual. AI often struggles with this differentiation.

• Differentiating Essential Details:
  • Overload of Information: Engineers might provide extensive technical data, but not all of it is pertinent to the invention workflow document and the
• Lack of Legal Understanding:
  • Missing Legal Criteria: AI models typically don't have an inherent grasp of what makes an invention patentable under law.
• Risk of Omissions and Irrelevancies:
  • Incomplete Disclosures: Important legal elements might be left out if the AI doesn't recognize their significance.
  • Inclusion of Unnecessary Details: The AI might include technical jargon that doesn't add value to the legal document.

Challenges You Might Face:

• Insufficient Depth and Nuance: Without deep knowledge of legal requirements, you might struggle to provide the level of detail needed for a patent attorney to determine if the invention meets all necessary criteria. This isn't about non-compliance but about lacking the comprehensive information that enables an expert evaluation.
• Information Gathering Limitations: Collecting and organizing all relevant technical details is challenging. The AI model might not have access to all the structured information it needs, making it difficult to apply logical rules effectively.
• Preparation for Legal Review: Your goal is to prepare the invention disclosure thoroughly for a lawyer to review, not to make legal judgments yourself. Missing or poorly organized information can hinder a legal expert's ability to make accurate assessments, potentially impacting the patent application process.

Tangify’s Solution for Bridging Knowledge Gaps 

Tangify assists by gathering and structuring the necessary information, ensuring that your invention disclosure is detailed and organized for legal review. Tangify's AI learns to distinguish between essential invention elements and supplementary information.

• Focused Prompts: Directing the AI to concentrate on specific aspects of the invention improves accuracy.
• Iterative Refinement: Continuously adjusting the prompts based on output quality helps fine-tune the AI's performance. I’d also say our system since “the AI” is OpenAI at this point.
• Source Tracking & Supporting Evidence: Tangify enhances transparency by meticulously tracking source information and supporting evidence throughout the invention discovery process. By distinguishing between implicit and inferred data, Tangify ensures that every piece of information is verifiable and traceable.

Benefits for You:

• Transparency in Information Source: You can see exactly where each piece of information comes from, making it easier to verify and trust the data.
• Structuring Unstructured Information: Tangify organizes messy, unstructured data into a format that's ready for IP processing, simplifying the patent preparation process.
• Ideation Support and Prompt-Based Triggers: The AI provides prompts that help engineers recall and document issues they've resolved, sparking new ideas and ensuring nothing is overlooked.
• Reduced Cognitive Load for Engineers: By handling the documentation process, Tangify lets engineers focus on innovation rather than paperwork.
• Reduced Risk of IP Slippage or Leakage: With thorough tracking and organization, the risk of important IP slipping through the cracks or leaking is minimized.

Identifying the critical elements that make an invention patentable requires nuanced understanding. AI doesn't inherently know what legal standards demand. By addressing these knowledge gaps, Tangify bridges the divide between human reasoning and AI capabilities. Using Theory of Mind as a foundation, Tangify enhances the AI's ability to understand and execute tasks that typically rely on human intuition.

Prompt Testing and Iterative Refinement—A Necessity in Legal IP Contexts

Applying Theory of Mind principles in AI requires continuous prompt testing and iterative refinement, especially in the complex field of legal IP.

Developing and Testing Effective Prompts

Creating prompts that guide AI to handle complex, subjective tasks accurately presents several challenges.

• Subjectivity in Legal Interpretation:
  • Diverse Expert Opinions: Legal IP tasks often involve nuanced judgment. Different attorneys might interpret the same information in various ways, making it hard to create prompts that yield consistent AI outputs.
  • Ambiguity in Language: Legal documents are full of subtle language cues that can be misinterpreted by AI without precise guidance.
• Lack of Standardized Evaluation Metrics:
  • No Clear Benchmarks: Unlike factual tasks with definitive answers, assessing the quality of AI-generated legal content lacks standardized metrics.
  • Difficulty in Measuring Success: Without clear criteria, it's tough to determine if a prompt is effective or if the AI's output meets the necessary legal standards.
• Dynamic Legal Environment:
  • Constantly Evolving Laws: Intellectual property laws and regulations change over time. Prompts need regular updates to stay relevant.
  • Jurisdictional Variations: Legal standards differ between regions, adding another layer of complexity to prompt creation.
    Resource Constraints:
  • Time-Intensive Process: Developing and testing prompts is labor-intensive and requires expertise in both AI and IP law.
  • Limited Access to Experts: Smaller organizations might struggle to find or afford professionals who can bridge the gap between technology and law.

Tangify's Approach to AI Prompt Development

Tangify addresses this by continuously developing and testing prompts to guide the AI effectively.

• Iterative Prompt Design:
  • Breaking Down Tasks: Tangify deconstructs complex tasks into smaller steps, making it easier to create targeted prompts that incorporate Theory of Mind concepts.
  • Role-Specific Instructions: By instructing the AI to "act as a patent attorney" or other roles, the prompts provide context that improves output quality.
• Testing and Refinement:
• Controlled Experiments: Tangify tests prompts in various scenarios to evaluate their effectiveness.
• Measuring Outcomes: By setting specific goals for each prompt, Tangify can assess whether the AI meets the desired standards.

Challenges You Might Face with AI Prompt Development:

• Time-Consuming Process: Developing effective prompts requires significant effort and experimentation.
• Expertise Required: Understanding both AI capabilities and legal requirements is necessary to craft suitable prompts.

Iterative Feedback Loops for Improved Outputs

Refining AI outputs is an ongoing process that benefits from iterative feedback, helping the AI better emulate human reasoning.

Challenge: AI models need continuous refinement to handle subjective tasks effectively. Without iterative feedback, the quality of outputs may not improve.

• Adapting to New Information: Legal standards evolve, so prompts must be updated accordingly.
• Handling Edge Cases: Unique scenarios may require special attention in prompt design.

Tangify's Iterative Feedback Prevention Measures

• User-Centric Feedback Integration:
  • Real-World Input: Gathering feedback from actual users helps identify practical issues the AI might encounter.
  • Responsive Adjustments: Prompt modifications are made based on user experiences and suggestions.
  • Collaboration with Legal Professionals:
  • Expert Insights: Involving IP attorneys in the feedback loop ensures that the AI's outputs meet legal expectations.
  • Validation and Verification: Legal experts can confirm the accuracy and relevance of the AI's responses.
  • Adaptive Learning Strategies:
  • Environment Monitoring: Keeping an eye on changes in IP law to update prompts accordingly.
  • Scenario Testing: Running the AI through various hypothetical situations to assess performance and adaptability.

Benefits for You:

• Enhanced Accuracy: Iterative refinement leads to more precise and reliable AI outputs.
• Adaptability: The AI system becomes better equipped to handle a variety of legal IP tasks.
• Efficiency Gains: Improved prompts reduce the need for extensive revisions, saving time.

Reinforcement Learning from Human Feedback (RLHF) and Its Limitations in IP

While Reinforcement Learning from Human Feedback is a common method for training AI models, it has limitations in the IP field.

Challenge: RLHF requires access to proprietary information and subject matter experts, which may not be readily available or affordable for startups.
Resource Intensive: Implementing RLHF is costly and time-consuming. Sharing sensitive IP information for training purposes also raises confidentiality issues.

Tangify's Perspective on RLHF

• Alternative Approaches: Instead of relying on RLHF, Tangify develops its own prompt testing methodologies based on Theory of Mind principles.
• Scalability Considerations: Tangify's methods are designed to be scalable without requiring extensive resources.
• Practical Solutions: By focusing on prompt refinement and feedback loops, Tangify achieves improvements without the drawbacks of RLHF.

Focusing on developing and testing effective prompts and implementing iterative feedback loops helps Tangify enhance the AI's ability to handle complex legal IP tasks. This approach, grounded in Theory of Mind concepts, overcomes many challenges associated with AI training in the IP domain and provides practical solutions that benefit you.

Bridging the Gap Between Human and AI Reasoning with Prescriptive Guidance

Applying Theory of Mind concepts helps bridge the gap between how you think and how AI models process information. With prescriptive guidance, you enhance the AI's ability to handle complex, subjective tasks within IP law.

Breaking Down Subjective Tasks into Objective Steps

Many IP tasks are subjective and involve intricate details. For AI to assist effectively, these tasks need to be broken down into clear, objective steps.

Challenges:

• Complexity of Subjective Tasks: Subjective tasks like invention discovery involve nuanced judgment and can't be easily quantified. AI models struggle with ambiguity and require explicit instructions.
  • Example: Determining the novelty of an invention involves understanding subtle differences from existing technologies, which is hard to define in objective terms.
• Information Overload: Technical documents often contain excessive details. AI may focus on irrelevant information if not guided properly.
  • Example: An AI might spend time analyzing background information rather than the core invention.
• Lack of Contextual Understanding: AI lacks the innate ability to prioritize information based on context, which humans do intuitively.
  • Example: Recognizing which aspects of an invention are critical for patentability requires contextual judgment.

Tangify “Solves” Subjectivity with Prescriptive Guidance

Tangify uses prescriptive guidance to deconstruct complex tasks like invention discovery and disclosure drafting into manageable steps.

• Step-by-Step Instructions: The AI follows a structured process, focusing on one aspect at a time.
• Objective Criteria: By converting subjective judgments into specific questions or criteria, the AI provides more accurate results.

Benefits for You:

Improved Clarity: Breaking tasks down makes it easier for you to see how the AI reaches its conclusions.
Enhanced Accuracy: The AI is less likely to miss important details when following a clear structure.

Role-Based Instruction for Improved Consistency

Different team members may contribute to IP documents, leading to inconsistencies in style and content. Providing role-based instructions guides the AI to produce consistent outputs.

Challenges:

• Variability in Human Input: Engineers, researchers, and legal professionals may use different terminology and focus on different aspects.
  • Example: An engineer might emphasize technical specifications, while a legal professional focuses on compliance.
• Inconsistent Document Structure: Without a standardized format, the AI may struggle to organize information coherently.
  • Example: Mixing technical jargon with legal terms can confuse the AI.
• Difficulty in Maintaining Standards: Making sure that all contributors adhere to the same guidelines is challenging.
  • Example: Team members may have varying interpretations of what information is essential.

Tangify’s Defined Roles

Tangify incorporates role-based prompting, instructing the AI to "act as a patent attorney" or other specific roles.

• Contextual Awareness: The AI adapts its responses based on the assigned role.
• Standardized Language: This unifies terminology across documents.
• Situational Context: The AI recognizes the context and adjusts its output accordingly.

Benefits for You:

• Consistency: Role-based instructions lead to more uniform documents.
• Efficiency: Reduces the time you spend editing and aligning contributions.
• Team Alignment: Helps different team members provide information in a consistent format.

Working Without Archives or Historical Data

There is a lack of training data or "ground truth" answers when it comes to IP and invention documentation. Without extensive archives or standardized examples, AI models can't rely on traditional training methods.

Challenges:

• No Standardized Examples: Without extensive archives, the AI can't learn from past cases.
  • Example: There may be limited publicly available invention disclosures to use as references.
• Difficulty in Benchmarking: Without concrete standards, assessing the AI's performance is hard.
  • Example: It's challenging to determine if the AI's output meets legal requirements.
• Reliance on Proprietary Information: Accessing necessary data may involve confidential or proprietary information.
  • Example: Using internal documents for training could raise privacy concerns.

Tangify's Solution to Working Without Archives or Historical Data

Recognizing this gap, Tangify developed its own system to mimic the processes of IP professionals.

• Agentic System: The AI follows a framework that simulates the reasoning steps of an IP expert.
• Custom Guidance: Without relying on external datasets, the AI uses internally developed prompts and structures.
• Validation Mechanisms: Incorporates checks to verify outputs meet necessary standards.

Benefits for You:

• Reliability: Even without traditional training data, the AI produces dependable results.
• Adaptability: The system handles a variety of IP tasks without needing extensive retraining.
• Confidence: You can trust that the AI's outputs align with professional practices.

By applying Theory of Mind through prescriptive guidance and role-based instructions, Tangify bridges the gap between human reasoning and AI capabilities. This approach allows you to leverage AI effectively, even in the complex and nuanced field of intellectual property.

Future Directions in AI-Driven IP Management

So what's in store for the future?  Continued bridging the gap between human reasoning and AI through advanced prompting techniques inspired by Theory of Mind, means you can expect significant developments in AI-driven IP management.

AI tools are poised to handle more complex IP tasks by integrating Theory of Mind concepts.

• Enhanced Reasoning: Future AI systems may better mimic human thought processes, allowing for deeper understanding.
• Managing Intricacy: With improved contextual awareness, AI can tackle intricate tasks that require nuanced comprehension.

Expanding Feedback Mechanisms

Incorporating nuanced human feedback is essential for refining AI capabilities, especially in legal contexts.

• Collaborating with Legal Experts: Partnering with professionals provides valuable insights that enhance AI performance.
• Detailed Feedback Loops: Precise input helps in crafting better prompts and improving the AI's responses.

Implications for Other Technical Fields

• The techniques used in IP management can be applied to other areas where specialized language translation is critical.
• Medical Applications: AI could assist in translating complex medical terminology into understandable language.
• Technical Communications: Similar strategies can bridge gaps between technical experts and non-specialists in various industries.

Expanding AI’s Role in IP Through Improved Prompting Techniques

Advancements in prompting methods may allow AI to handle a broader range of IP processes.

• Comprehensive IP Management: AI might evolve from assisting with discovery and drafting to managing the entire IP lifecycle.
• Streamlined Systems: This progression offers a more efficient and seamless experience for IP professionals.

Integrating Tangify with Broader IP and AI Tools

• Synergy with Existing Solutions: By structuring subjective IP tasks, Tangify adds value to tools like prior art search and portfolio analysis.
• Unified Ecosystem: Integration promotes better collaboration and efficiency across different IP management platforms.

Ready to supercharge your IP pipeline with invention discovery and disclosure drafting efficiency? Schedule time with Tangify now.