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Similar Family Searching

Identify patent families based on their similarity

What is a Similar family search for patent data?

Similar family search with respect to patent data refers to a search algorithm or method that is used to identify patent families or related patents based on their similarities. This may involve searching for patents that are similar in terms of their technical specifications, patent claims, or other features.

A patent family includes all of the patents and patent applications that share the same priority application, which means they are related to the same invention. These related patents may be filed in different countries, but they share the same priority date, which is the date of the first patent application filed for that invention.

The purpose of this type of search is to identify other patents that may be relevant to the same technology or invention, which can be useful for patent analysis, and understanding the patent landscape. You can find similar patents from your own unpublished inventions and understand what already exists.

How Cipher Similar Family Search works

Why the data provided by Cipher is best in class for similar family searching?

Cipher uses a very sophisticated proprietary patent linguistic algorithm that has been tried and tested over the past 2 years across our Universal Technology Taxonomy (“UTT”) classification. The development of UTT took more than 4 years and is more advanced than most other systems on the market. Therefore, It will typically provide better results than other similarity search tools available on the market.

By leveraging vectorization, we can efficiently calculate the similarity between patent documents and identify similar patents based on their content. This approach allows for fast and scalable processing of large patent datasets.The cost to vectorise all patents is significant, and typically a barrier for suppliers to implement a robust similarity searching system.

Cipher has the advantage of having done much of this vectorisation for UTT, so the incremental workload has been substantially reduced compared to a new entrant developing this.

Semantic v/s Similar Search

Where similarity searching is used with multiple source patents, these patents must be related as the algorithm will assume you are providing it with patents about a similar topic. The sophisticated algorithmic approach provides a greater accuracy than similar semantic search driven approach.

For example: imaging looking for “portable solar powered devices.” You can provide one solar powered fan example, and one solar powered pump example, and it will “figure out” that it’s the solar powered bit that’s important (overlap).



Infographic depicting similar family search result for patent number C0051975087

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Frequently Asked Questions

Why has Cipher’s ML suggested these results?

Similarity searching starts with vectorising every patent family in the universe (think of this like
giving each patent family a unique fingerprint).

Each patent family can then have its vector (fingerprint) compared against others to identify vectors
(patent families) that is closest to it, returning the closest results based on the chosen sample size
(50, 100, 1000 etc.).

What parts of the patent are considered when finding a match?

Cipher’s deep learning model (“algorithm”) is specifically designed for patent linguistic tasks and
uses the patent title, abstract, and claims to generate a vector for each individual patent family. It is a
similar process to how the Chat GPT model operates.

What makes Cipher similarity searching better than competing systems?

The processing power, cost, and time required to vectorise all patents are significant, and a barrier for
suppliers to implement a robust similarity searching system. Cipher has leveraged the in-house
vectorisation in classifying every patent family in the world for use in our UTT, so the
testing and heavy lifting have already been done.

Detailed Case Studies

How does Cipher work in real-life situations? How has it helped IP and patent departments, or legal professional service teams to put together information for their clients?

Read how Cipher customers obtained specific data for projects, managed their patent portfolios, and measured patent risk.

Our Case Studies

How law firms can use data analytics to build long term partnerships with their clients

Calum Smyth, Partner at Wiggin LLP, explains how law firms can become true advisers to their clients by harnessing the latest advances in technology and data analytics.
Read more

CFC Underwriting on being data led to accurately assess IP insurance risk

Maddi Brown, Intellectual Property Practice Leader at CFC Underwriting Ltd, shares how Cipher has helped to understand and quantify IP insurance risk.
Read more

Seagate on keeping up with technology changes

Chief Intellectual Property Counsel at Seagate, Bob Pechman explains the pressures of supporting the business during a period of rapid technological change and how Cipher helps see the patent landscape through Seagate’s own technology lens.
Read more

Centrica on the value of Cipher

Charles Clark, Director of Intellectual Property at Centrica, discusses the efficiency gains from using Cipher and the tasty bits of data gleaned from using Cipher’s AI over conventional search.
Read more

Richardson Oliver Insights on the challenges of IP Teams

COO of Richardson Oliver Insights, Erik Oliver, talks about the challenges faced by IP teams with managing high value portfolios and the increased demand for both better data and better ways to communicate the value of patents.
Read more

Red Hat on using the Cipher Optimisation Model

Jared Engstrom, Head of Patent Development, talks about the growth in Red Hat’s portfolio, how he has used Cipher’s custom classifiers and how the Cipher Optimisation model has improved their understanding of the competitive landscape.
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Cipher Vision Podcast

Cipher’s monthly podcast, Cipher Vision is now into its third season.  Each episode has co-hosts Nigel Swycher (CEO) and Francesca Levoir (Head of Marketing) and a notable guest discuss an IP or patents-related topic.

Guests have covered a range of subjects, from valuation and sustainability through to diversity and machine learning.

Cipher Vision episodes are under 25 minutes and are ideal to listen to during a short break or journey.

Cipher Vision podcast index

Democratization of Innovation

Episode 9 in Season 3 features Keith Bergelt, CEO, Open Invention Network, who joined us to explore the relationship between IP and open source, sharing the benefits and challenges of collaboration and OIN’s role in preserving patent freedom. 
Read more

Putting Passion in IP

Episode 8 in Season 3 features Anjanette Lecher, Director, Intellectual Asset Management at Corning Incorporated. She shared with us her love of intellectual property, discussing how to take a customer centric approach when analysing and presenting data, adopting the correct tools to support efficiency and visualizations.
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SDGs through a patent lens

Episode 7 in Season 3 features Marco Richter, Global Head of Customer Success at LexisNexis Intellectual Property. He highlights the important role that patents play in helping us to become more sustainable, discussing the benefit of measuring innovation in relation to the UN Sustainable development Goals (SDGs).
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Flat, Fast and Focused

Episode 6 in Season 3 features Lorie Goins who shares with us how to approach innovation, by adopting a questioning, inclusive and analytical mindset, drawing on tools to form evidence-based decisions that enhance your business and IP strategy.
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Promoting SEPs Transparency

Episode 5 in the Cipher Vision Podcast series features Tim Pohlmann, CEO, LexisNexis IPlytics. He joins us to discuss the world of SEPs, from areas of conflict to new opportunities.
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Protecting Innovation with Director Vidal

Episode 4 in Season 3 features Director Kathi Vidal of the United States Patent and Trademark Office, who shares her thoughts on how we can improve the innovation ecosystem.
Read more

Improve your patent strategy now

Speak to the Cipher team today.

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