Free-Text Search User Guide - Lite

This guide covers the basic steps of carrying out a Free-Text search for an invention, when you have an Invention Disclosure/Project Scope Description; Image or any other Technical Description. It will also cover what is not possible to do with Free-Text.

English is the native language of IPRally and optimal as input. German, French, Spanish, Chinese, Japanese, Italian, Dutch, Danish, Finnish and Swedish are supported via machine translation (with a maximum 5000 characters). The translation happens inside IPRally, making it secure.

Free-Text Search takes your input (see above) and curates a Knowledge Graph for searching. The results are shown by ranking (using the AI Score)

Free-Text Search Is Not a Generative Ai Search - meaning, you must describe the object(s) and features you are searching for, and NOT a 'ChatGPT' style prompt expecting the Ai engine to curate a more robust description (with potential hallucination).

The AI Score

The maximum score is 100 and it describes the overall graph similarity. The score is calculated as a vector distance between the query graph and prior art graph after AI processing of the graphs. With short (e.g. claim) query graphs, no scores higher than 60 are usually reached as there are many irrelevant features producing noise in the prior art graphs.

FAQ's/Best-Practice for Free-Text Searching

When using the Free-Text tool, please use the below insights to drive your searches:

The AI cannot (yet 😊) read your mind: Therefore, give it enough
- Context information:
  - E.g. “device” -> “mobile communication device” / “method” -> “method for processing signals”
- Technical details:
  - Detailed is better than general
  - Functional relationships are important (and possible!) in IPRally
  - E.g. “elongated member engages with circular member to oscillate pendulum”
The AI is trained with real claims and specifications:
- Don’t be afraid of “patent jargon” (e.g. “means for …” / “first element”): The AI understands it!
- Claims-level of details is a good starting point
In free-text search, use natural and consistent language:
- Full sentences, include articles, internally coherent part names, etc.
Common abbreviations are recognised but often longer format is preferred
- e.g. “SEM” -> “scanning electron microscope”

**Examples*:**

Good: "The invention is a vehicle-to-vehicle (V2V) communication system for autonomous vehicles, using both wireless technologies (WiFi, LTE, 5G) and RFID. It features a dual-module framework that dynamically switches between high-bandwidth and low-bandwidth communication modes to optimize data transmission. This system enhances safety, efficiency, and passenger interaction in autonomous vehicles."

Poor: "A V2V system for autonomous vehicles using wireless tech and RFID to enhance safety."

Why? The good example has technical details around the objects, and how they interact to deliver the function being searched. There are functional relationships described, and the language is descriptive vs. prompting. The poor example is too brief, lacking functional relationships, and doesn't describe the 'novelty' well enough. The results will be too 'vague' compared to the good input.

What not to Do:

Don't use a 'ChatGPT' style Generative Ai Prompt with Free-Text search.

Please...

It will greatly diminish the ability for our Graph-Ai to understand the subject, and therefor provide lower-quality results.

Example of a poor 'Prompt' inspired search input:

"A V2V system for autonomous vehicles using wireless tech (WiFi, LTE, 5G) and RFID to enhance safety. Focus on highlighting the dual-module communication approach that integrates high-bandwidth wireless technologies for real-time data and low-bandwidth RFID for stable communication. Emphasize how the system dynamically switches between these modes to optimize performance, targeting improvements in safety, efficiency, and passenger experience in autonomous vehicles."

Why? Our Graph Ai is not a LLM (As all GPT's are), but rather a Knowledge Graph Neural Network. This requires the input to be similar to the patent documents being searched, and does not operate as an LLM would in 'filling in the blanks'