Do Semantic “Similarity” Searches Produce Better and Faster Results?

The Prior Art Search Challenge

It is difficult to identify novelty of an invention without a thorough understanding of previous work in the field. Prior art search is essential to define the boundaries between a potential invention as claimable in a patent and the published prior art. It is common practice to perform keyword-based and/or classification-based searches of the disclosed concept. Getting a comprehensive result including hidden or unexpected prior art using classic methods is challenging, particularly when working under time constraints. The quality of the search greatly depends on how much time is spent on the search, and on the technical background and skill of the searcher.

Semantic search, similarity search or citation analysis approaches could be combined to overcome the above challenges in many cases. This article emphasizes the benefits of similarity search for finding potential prior art.

Similarity Search

A number of patent databases (both public and paid) provide similarity search options. Similarity search engines may operate in one or more of the following ways:

1. Perform text mining and machine learning to extract contextual similarities between the target patent and the assets stored in the patent databases;
2. Search for patents that have common citations or share citations within the same family;
3. Retrieve a list of similar records leaving out stop words and common words such as “method”, “process” or “device”;
4. Display potentially relevant prior art documents ranked based on the relevancy.

MaxVal validated the effectiveness of a similarity search engine¹.  MaxVal conducted similarity search for a set of three patents filed under different IPCs viz. US8315756B2, US8838292B2 and US9149609B2. In each case, the similarity search retrieved a large number of patents ranked in downward order of relevancy score, starting with the most similar with 100% rating. The top results with rating above 90% relevancy were filtered using either keyword or classification-based restriction to retrieve a handy number such as 200-300 that were easy to review. In each of the three cases, the examiner-cited references were retrieved with rating above 90% relevancy through the similarity search as shown in the table below.

Target Patent	Technology	Examiner Cited References	Examiner Cited References Retrieved through Similarity Search
US8315756B2	G01C 22/00 (Decentralised systems, e.g. inter-vehicle communication)	US20100121518A1	✓
		US20140039716A1	✓
		US20130325210A1	✓
		US9092987B2	✓
		US9070022B2	✓
US8838292B2	G05D 1/00 (Control of position or course in two dimensions specially adapted to land vehicles…)	US20030225477A1	✓
		US20040024527A1	✓
		US20040254729A1	✓
		US20080269992A1	✓
		US20060106538A1	✓
		US20070043502A1	✓
		US8315756B2	✓
US9149609B2	A61M 25/06 (Guide tubes)	US4611594A	✓
		US5074871A	✓
		US5968057A	✓
		US5688234A	✓
		US20020026203A1	✓
		US5908435A	✓
		US20020068954A1	✓
		US7169154B1	✓
		US6517551B1	✓
		US20060135987A1	✓
		US20050119668A1	✓
		US20060253145A1	✓
		US20070191878A1	✓
		US20070208351A1	✓

In summary, similarity search augments keyword/classification-based searches and dramatically increases the quality of the results set that needs to be examined. Thus, similarity search function could enhance the efficiency of prior art search by minimizing the probability of missing relevant prior art in given time.

¹MaxVal used Questel Orbit to test the functionality of Similarity search

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MaxVal’s Litigation Databank to supplement Aistemos’ IP Analytics Platform, Cipher

Los Altos, CA – MaxVal Group, Inc. is pleased to announce its latest partnership with Aistemos, Europe’s leading IP analytics company, for its patent litigation data. US-based MaxVal licences its curated patent litigation data and the databank application programming interface (API) for integration into Aistemos’ global IP intelligence platform, Cipher.

MaxVal’s Patent Litigation Databank is a comprehensive collection of cases (71,000+) of the US District Court, Court of Appeals for the Federal Circuit, International Trade Commission, Supreme Court, and the Patent Trial and Appeal Board. It has fully searchable databases related to patent litigation from ECFs, PACER, ITC, etc.

Major benefits of MaxVal’s Patent Litigation Databank are proactively monitoring and querying whether patents of interest have been litigated; gaining analytical insights on plaintiffs, defendants, filing trends and courts, and staying updated with patent litigation alerts.

Paybacks of the API include easy access to complex litigation data without incurring any PACER expenses; and querying and analyzing using over 20 litigation fields including patents, products, plaintiff, defendant, court, judge, law firm, and outcome of cases.

Cipher is the world’s first IP analytics solution to aggregate, analyze and visualize IP data relating to patents and related events including litigation and licensing. MaxVal’s Patent Litigation Databank will seamlessly integrate into Cipher and be available to Aistemos’ clients immediately.

MaxVal’s CEO, D. Bommi Bommannan noted that combining good quality patent litigation data with IP analytics, as done by Cipher, provides clients a powerful mechanism to assess and manage patent litigation risk. Cipher combines the power of machine learning, AI and powerful visualization methods to help businesses understand and communicate IP strategy instantly. 

Commenting on the partnership, Nigel Swycher, CEO of Aistemos, said: “The production of world class IP analytics requires best in class data and MaxVal has done excellent work curating litigation and dispute data from a range of US venues.”