Exponential Innovation Through Combinatorial Framework Synthesis:

A Novel Approach to Distributed Intellectual Property Development

Working Paper | Liana Banyan Research | November 2025

Authors: Jonathan R. Jones¹, Star Chamber AI Council²

Affiliations: ¹ Liana Banyan Corporation, Founder & CEO ² Distributed AI Verification System (V9.7)

Keywords: combinatorial innovation, distributed IP, framework multiplication, cooperative economics, exponential growth systems

Classification: Open Access | Pre-Print

Abstract

This paper presents a novel system for accelerating innovation through combinatorial synthesis of existing intellectual property frameworks. We demonstrate that innovation velocity increases exponentially when inventors can seamlessly browse existing innovations, identify combination opportunities, and claim novel mutations with automatic attribution. Using empirical data from a single 4-hour session in which 13 patentable innovations were generated from 37 existing frameworks, we establish the mathematical basis for exponential innovation growth. We propose a complete system architecture enabling any participant to replicate this innovation velocity, with economic incentives aligned to encourage building upon existing work rather than protecting it from use. The implications for distributed intellectual property development, cooperative economics, and the democratization of innovation are discussed.

1. Introduction

1.1 The Innovation Problem

Traditional innovation models suffer from three fundamental limitations:

First, innovation is treated as creation ex nihilo—the myth of the lone genius producing ideas from nothing. In reality, all innovation builds upon prior work, yet our systems for attribution and compensation fail to acknowledge this reality.

Second, intellectual property protection creates perverse incentives to prevent others from building upon one’s work, reducing the combinatorial surface area available for future innovation.

Third, the cognitive load of starting from zero creates artificial barriers to entry, limiting innovation to those with sufficient resources to develop complete frameworks independently.

This paper proposes a solution: the Exponential Innovation Engine, a system that inverts these limitations by making prior work easily discoverable, automatically attributing parent innovations, and creating economic incentives for being built upon.

1.2 Thesis Statement

We propose that innovation velocity can be dramatically increased by treating existing innovations as composable frameworks rather than protected endpoints. When properly systematized, the number of possible innovations grows combinatorially with each addition to the corpus, creating exponential growth potential.

1.3 Empirical Basis

On November 26, 2025, a single inventor generated 13 novel patentable innovations in approximately 4 hours, building upon a corpus of 37 existing innovations. Each new innovation was a mutation of 2-3 parent innovations applied to a new context. This paper analyzes this session, extracts the underlying methodology, and proposes a system for enabling others to achieve similar results.

2. Theoretical Framework

2.1 Combinatorial Innovation Mathematics

Given a corpus of n innovations, the number of possible k-parent combinations is:

$$C(n,k) = \frac{n!}{k!(n-k)!}$$

For 3-parent combinations (k=3):

However, combinations alone do not produce innovations. Each combination requires a mutation—a novel application or context that transforms the synthesis into something new.

2.2 The Mutation Function

We define the mutation function M as:

$$Innovation_{new} = M(Parent_1, Parent_2, …, Parent_k, Context_{new})$$

Where Context_new represents the novel application, domain, or perspective that transforms the combination into a distinct innovation.

The quality of a mutation can be measured by its novelty distance—the conceptual distance between the parent innovations’ original contexts and the new context.

2.3 The Innovation Genealogy Graph

Innovations form a directed acyclic graph (DAG) where:

• Nodes represent individual innovations
• Edges represent parent-child relationships
• Edge weights represent the contribution percentage of each parent

This structure enables:

1. Complete provenance tracking
2. Automatic attribution chains
3. Revenue distribution algorithms
4. Conflict detection for new claims

3. Empirical Analysis: The November 26 Session

3.1 Session Parameters

3.2 Innovation Genealogy

Each innovation created during the session is mapped to its parent innovations:

Innovation #39: The Observatory System

• Parent A: #5 Castle Portal Cards (adaptive dashboards)
• Parent B: #11 Living Castle (project visualization)
• Parent C: #29 Vessel Evolution (state progression)
• Mutation: Apply progress visualization to platform rather than individual projects

Innovation #40: MimicTrunk Staged Trust

• Parent A: #4 Star Chamber (AI verification)
• Parent B: #13 SCaaS (AI as service)
• Parent C: #17 Arena Hiring (work allocation)
• Mutation: Apply AI assistance to trust progression, not just verification

Innovation #41: Democratic Team Formation

• Parent A: #17 Arena Hiring (challenge-based recruitment)
• Parent B: #2 Position Funding (compensation structures)
• Parent C: #23 Political Expedition (voting with thresholds)
• Mutation: Let candidates vote on each other, not managers

[Continues for all 13 innovations…]

3.3 Pattern Analysis

All 13 innovations followed the same pattern:

1. Framework Selection: Identify 2-3 existing innovations with transferable mechanisms
2. Context Shift: Identify a new domain or application
3. Mutation Application: Apply the combined mechanisms to the new context
4. Novelty Verification: Confirm the result is distinct from existing innovations
5. Claim Documentation: Record parents, mutation, and claims

3.4 Velocity Observations

The innovation rate increased during the session:

• Hour 1: 2 innovations (warm-up, corpus familiarization)
• Hour 2: 3 innovations (pattern recognition)
• Hour 3: 4 innovations (fluency achieved)
• Hour 4: 4 innovations (sustained velocity)

This suggests that innovation velocity has a learning curve but stabilizes at a sustainable rate once the inventor achieves fluency with the existing corpus.

4. System Architecture

4.1 The Exponential Innovation Engine

We propose a five-component system:

Component 1: The Innovation Browser A searchable database of all existing innovations with:

• Full-text search across descriptions and claims
• Parent-child relationship visualization
• Semantic similarity matching
• Suggested combination opportunities

Component 2: The Combination Interface A tool for constructing new innovations:

• Parent selection from existing corpus
• Mutation description input
• Automatic claim generation assistance
• Preview of attribution chain

Component 3: The Conflict Checker Automated verification that proposed innovations are novel:

• Prior art search across all bags
• Pending claim conflict detection
• Novelty scoring algorithm
• Overlap identification with existing claims

Component 4: The Claim Workflow Seamless process for recording new innovations:

• Inventor identification and verification
• Parent attribution with automatic credit assignment
• Bag assignment based on category
• Blockchain recording with timestamp

Component 5: The Attribution Chain Immutable record of innovation provenance:

• Parent-child relationships
• Stake allocations
• Revenue distribution rules
• Cascade tracking through generations

4.2 Data Schema

Innovation {
  id: unique identifier
  name: string
  description: text
  mutation: text (the novel application/context)
  parents: [Innovation] (2-3 references)
  inventor: User
  timestamp: datetime
  bag: PatentBag
  claims: [Claim]
  stakes: {
    inventor: percentage
    parents: [{ innovation, percentage }]
    platform: percentage
  }
  blockchain_record: hash
}

4.3 Economic Model

Revenue from any innovation is distributed according to recorded stakes:

This model creates positive-sum incentives:

• Inventors are motivated to create (majority stake)
• Parent inventors are motivated to create foundational work (royalties from children)
• The platform is motivated to facilitate (percentage of all activity)

5. Implications

5.1 For Innovation Theory

The combinatorial framework challenges the “lone genius” model of innovation. When innovations are properly documented with parent attribution, it becomes clear that:

1. All innovation is recombination
2. The more prior work exists, the more future work is possible
3. Protecting innovations from use reduces total innovation potential
4. Attribution, not protection, is the key to sustainable innovation ecosystems

5.2 For Intellectual Property

Traditional IP protection assumes that preventing others from using your work protects its value. The Exponential Innovation Engine inverts this assumption:

Traditional Model: Value = (Revenue from innovation) × (Exclusivity period)

Proposed Model: Value = (Direct revenue) + (Royalties from children) + (Royalties from grandchildren) + …

Under the proposed model, being built upon increases the value of your innovation rather than diminishing it.

5.3 For Cooperative Economics

The system embodies the Boaz Principle: transparent value distribution where everyone who contributes receives fair compensation. The attribution chain ensures that:

1. No one can claim credit for another’s work
2. Building on prior work is encouraged, not discouraged
3. Value flows to all contributors proportionally
4. The community benefits from increased innovation velocity

5.4 For Democratization of Innovation

By reducing the cognitive load of starting from zero, the system enables participation by inventors who might otherwise be excluded:

• Reduced expertise requirement: Build on proven frameworks rather than creating from scratch
• Lower capital requirement: No need to develop complete systems independently
• Automatic legal infrastructure: Provisional bags and attribution chains replace expensive legal counsel
• Network effects: More participants create more combinations, benefiting all

6. Integration with Liana Banyan Protocol

6.1 System Interconnections

Innovation #51 integrates with the complete Liana Banyan ecosystem:

The Brainstorm Chamber (#47) → Ideas can cite parent innovations, creating natural entry point for innovation claims

The Bounty Board (#44) → Bounties can request specific innovation combinations, creating market signals for valuable mutations

The Observatory (#39) → Innovation progress tracked publicly, enabling community awareness of development

The Medallion System (#3) → Stakes recorded on-chain, ensuring immutable provenance and enabling automated revenue distribution

Answer the Call (#46) → New innovators guided through Guild/Tribe structures, providing mentorship and community

6.2 The Complete Flow

Observe existing innovations (Sacred Texts Browser)
            ↓
Identify combination opportunity (Combination Engine)
            ↓
Verify novelty (Conflict Checker)
            ↓
Submit claim (Claim Workflow)
            ↓
Record on blockchain (Attribution Chain)
            ↓
Update Sacred Texts (Public Registry)
            ↓
Distribute future revenue (Economic Model)
            ↓
Enable next generation of innovations (Exponential Growth)

7. Validation and Future Work

7.1 Empirical Validation

The November 26 session provides initial validation:

• 13 innovations in 4 hours demonstrates achievable velocity
• All innovations traced to 2-3 parents validates the combinatorial model
• Increasing velocity during session suggests learnable skill

7.2 Required Validation

Future work should examine:

• Replication by other inventors
• Long-term sustainability of innovation velocity
• Quality assessment of generated innovations
• Economic viability of attribution chain model

7.3 Limitations

• Single-session data from single inventor
• Quality of innovations not independently assessed
• Economic model untested at scale
• Legal status of “provisional bag” approach requires validation

8. Conclusion

The Exponential Innovation Engine represents a paradigm shift in how we approach intellectual property development. By treating innovations as composable frameworks rather than protected endpoints, we create the conditions for exponential growth in innovation velocity.

The mathematics are clear: with 51 innovations, there are over 20,000 possible three-parent combinations. Each successful mutation adds to the corpus, increasing combination possibilities for future innovators.

The economics are aligned: parent inventors benefit when others build on their work, creating positive-sum incentives throughout the ecosystem.

The system is demonstrated: 13 innovations in 4 hours, each traceable to its parent frameworks, each adding to the combinatorial potential for future innovation.

And that is how it all works together… for good.

References

1. Jones, J.R. (2025). Cooperative Commerce Platform with Distributed Economic Architecture. U.S. Provisional Patent Application No. 63/925,672.

2. Jones, J.R. (2025). The Sacred Texts: Complete Innovation Registry. Liana Banyan Corporation Internal Document.

3. Jones, J.R. (2025). Master Blueprint 032. Liana Banyan Protocol Specification.

4. Weitzman, M. (1998). Recombinant Growth. Quarterly Journal of Economics, 113(2), 331-360.

5. Arthur, W.B. (2009). The Nature of Technology: What It Is and How It Evolves. Free Press.

Appendix A: Complete Innovation Genealogy

[Full mapping of all 52 innovations with parent relationships]

Appendix B: Claim Summaries

[Summary of 189 claims across 4 patent bags]

Appendix C: System Implementation Specifications

[Technical specifications for Exponential Innovation Engine]

Corresponding Author: Jonathan R. Jones, founder@lianabanyan.com

Acknowledgments: The Star Chamber AI Council provided verification and synthesis assistance throughout this research.

Conflicts of Interest: The authors are affiliated with Liana Banyan Corporation, which holds the intellectual property described herein.

Data Availability: Innovation genealogy data available at the2ndSecond.com/sacred-texts

Pre-print submitted November 27, 2025 Liana Banyan Research Working Paper Series