Patent AI

CODIOM

Technologies to be Used:

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Table of Contents

• Overview
• Team
• Problem
• Solution
• Key Features
• Tech Stack
• System Architecture
• Data Sources
• Roadmap

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Overview

Patent AI is an intelligent, AI-driven platform designed to act as a "Patent Difference Analysis and Innovation Consultant."

The traditional patent research process is often long, complex, and prohibitively expensive. Entrepreneurs, researchers, and R&D teams struggle to determine if their ideas are truly novel, frequently relying on systems that only support English or offer basic keyword-based search results without intelligent context.

Patent AI transforms this process by:

• Semantic Analysis: Utilizing Deep Learning and Vector Search (FAISS, SentenceTransformers) to understand the meaning of an invention, not just the keywords.
• Difference Detection: Automatically comparing user ideas against existing patent databases to highlight specific similarities and unique differentiators.
• Bilingual Support: bridging a critical gap by providing native support for Turkish patent data alongside global English databases.
• Smart Consultation: Leveraging LLMs (Llama 3/GPT-4) to evaluate innovation potential and provide strategic, actionable recommendations.

Team

Role	Member	LinkedIn
Deep Learning & Team Lead	Berat Erol Çelik
Backend & API	Emre Aldemir
Frontend & UI/UX	Umut Odabaş
Machine Learning	Ömer Altıntaş
LLM Specialist	Efkan Çıtak

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Problem

Patent processes are lengthy, complex, and costly. Entrepreneurs, researchers, or R&D teams struggle to understand whether their ideas have been patented before, which areas have high application volumes, or which parts truly represent innovation.
Current systems:

• Only work in English and do not cover Turkish patent data.
• Remain at the level of search tools and do not provide users with smart suggestions.
• Do not track similar applications after the patent is granted.

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Solution

PatentAI

• Analyzes intellectual property or patent documents
• Finds similar patents and summarizes their differences
• Evaluates innovation potential
• Provides smart recommendations for entrepreneurs, R&D teams, and researchers

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Key Features

Feature	Description	Status
Patent Gap Analysis	Compares ideas with existing patents	✅ Completed
LLM-Based Semantic Analysis	Intelligent interpretation with Llama 3/GPT-4	✅ Completed
Patentability Assessment	Evaluates innovation potential	✅ Completed
Turkish Patent Support	First system to perform Turkish patent analysis	✅ Completed
Density & Gap Analysis	Identifies crowded and empty technology areas	✅ Completed
Patent Monitoring	Tracks similar applications after patenting	✅ Completed
Strategic Recommendations	Technical and market-oriented advice	✅ Completed
Multi-User Reports	Customized reports for different user types	✅ Completed

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Tech Stack

Backend & API

Artificial Intelligence & Machine Learning

Front End & User Interface

Database & Distribution

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System Architecture

Patent AI is an AI-powered "patent gap analysis and innovation consultant." The system analyzes an inputted idea or patent document, compares it with existing patents, identifies differences, and evaluates its innovation potential.

└── /
    ├── ai_models
    │   ├── embeddings
    │   ├── evaluation
    │   ├── llm_analysis
    │   └── similarity
    ├── backend
    │   └── app
    ├── data
    │   ├── processed
    │   ├── raw
    │   └── vectors
    ├── deployment
    │   └── deployment.py
    ├── docs
    │   ├── api
    │   ├── technical
    │   └── user_guide
    └── frontend
        ├── assets
        └── components

API Uç Noktaları

Endpoint	Method	Description
/api/analyze	POST	Analyzes the patent idea and finds similarities
/api/similar	GET	Finds similar patents
/api/report	POST	Generates an analysis report
/api/health	GET	System health check

Data Flow

1. Input: User submits an idea or patent text.
2. Processing: Text is converted into vectors using SentenceTransformers.
3. Search: Scans the patent database using FAISS similarity search.
4. Analysis: LLM processes differences and innovation potential.
5. Output: A structured report containing recommendations.

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Data Sources

PatentAI works with both Turkish and English patent data. Resources used in the initial MVP version:

• Google Patents (English and Turkish Patents) - Main data source
• EPO (European Patent Office) - Main data source

Roadmap

• Task 1: Analysis & Planning
• Task 2: Data Collection & Modeling Initialization
• Task 3: Flask API & Backend Development
• Task 4: Interface + Reporting (Python-based)
• Task 5:Testing, Demo & Presentation

Technologies:

• Python 3.x
• Flask / FastAPI
• SentenceTransformers (all-MiniLM-L6-v2)
• FAISS or cosine similarity
• PostgreSQL (data records)
• Optional: Elasticsearch (for fast text search)

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Example Data Flow Scenario

1. The user enters an idea or a patent summary.
2. The backend converts the text into embeddings (SentenceTransformers).
3. The system searches for similar patents in the database (cosine similarity / faiss)
4. An LLM (e.g., Llama 3 or GPT-4) interprets the differences and innovation aspects.
5. Results return to the frontend as a JSON or HTML report.

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