Neural Network

Understanding which of your competitors own patents relating to your technologies is key to any competitive intelligence function. Cipher automates the analysis of competitive intelligence to support strategic patenting decisions. Cipher has a range of benchmarking metrics and has been optimised for analysing competitors and new entrants using your technology lens.

Artificial Researcher is an information technology company and a start-up in the text mining industry. We are specialists in developing novel and innovative cross genre scientific text mining systems tailored to the needs of researchers and information search professionals, such as client customized indices for local deployment.

Part of the text mining technology was developed during our CEO Linda Andersson's PhD work at Technical University of Vienna (TU Wien), for example the passage retrieval, the automatic query formulation, and a merging method of document index and paragraph index. Our system solutions offer our customers and clients a market advantage, we reduce their efforts in terms of time spent searching and reviewing information.

1. Focus can give you unparalleled strategic insight into your competitive environment and the players in it. With Focus, you can hone your IP strategy, and effectively communicate its value to the board.
2. Founded in 2019 (seed funding). Pilots in 2020. Scoring portfolios. SaaS launch Q1 2021

Amplified was built to make working with patent information simpler and more effective. By reading full text and leveraging data such as examination history, Amplified learns what patents mean not just what they say.

Our deep learning AI has been trained on over 140 million records so you get get professional grade results without complex queries and distracting noise. Just describe an idea in plain language or enter a patent number. Powerful filters make it easy to direct search results to particular technical features for maximum control and efficiency.

Amplified also makes it easy to access your previous work by keeping the data you review organized and searchable. Add notes and annotations, keep track of patents you've seen before, and collaborate with your team on shared projects.

IPRally offers a completely new way of searching patents: Knowledge graphs and supervised deep learning AI.

All features of +80M prior art specifications and claims are automatically extracted into a graph format and the AI trained to compare an input graph with the pre-converted prior art graphs. The deep learning AI is trained with real Patent Examiner citations to find the most relevant pieces of prior art. You can focus on the core of the technology of interest and let the AI do the tedious feature matching with deep level understanding of technology.

An intelligent search engine that thinks like a patent professional is born.

Offers a global IP database along with AI-powered tools customized to various workflows including prior art search, IP landscaping, patent categorization, technology tracking, competitor analysis, IP commercialization, and automated patent alerts. Ideal for IP professionals in corporations, IP law firms, R&D firms and academic institutions. The tools include Novelty, Tech Tracker, Categorizer, Tech Explorer, and Enterprise WebTool.

Making Prior art search accessible to all

PQAI is a collaborative open source initiative to build a shared AI-based prior-art search tool. At PQAI, we envision a prior art search engine that is:

1. Accessible by all, no need for extensive training or funds
2. Powered by AI to provide extremely effective search results
3. Capable of searching combinational prior art (103), the most frequent reason for rejection at the USPTO
4. Supports natural language queries
5. Simple implementation using a distributed architecture eliminating the need to create and maintain a large database.
   

The vision is big and it can’t be realized without community  support from across the globe. Here is how you can help support the project:

1. Advisors - Patent professionals who can test the tool, share their feedback and help PQAI improve and promote the adoption of PQAI.
2. NLP practitioners - The plan is to make PQAI open source just as LINUX, developers can branch out the code, create different versions. For example: Currently PQAI cannot process the data related to chemistry patents, maybe one of the developers can build a version that caters specifically to chemical patents.
3. Patent Offices - Patent offices can contribute and benefit by providing PQAI access to their examiners and inventors to help grow the user base and If all the patent offices can have an instance of PQAI search engine and they become a part of the distributed network; the amount of data in which the prior art search shall happen will be humongous, almost universal.
   

If you wish to participate in this initiative, kindly write to sam@projectpq.ai

Sam Zellner, a prolific inventor, retired executive director innovation at AT&T is the product lead for PQAI.