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AI Versus Manual Patent Searching: How a Hybrid Approach Can Optimize Success

Home / Blog / AI & LLM / AI Versus Manual Patent Searching: How a Hybrid Approach Can Optimize Success

Artificial Intelligence (AI) is a developing and widely accepted technology in almost all sectors of the world including banking, security, healthcare, education, information technology, manufacturing, production, etc. Even in our own homes, AI has made its own space and we can see multiple examples in the form of IoTs, smartphones, and AI-based virtual assistants. 

The IPR industry stays no exception to this, and AI can bring a revolutionary change in the IPR industry too. But considering, that the AI-based patent searching tools are still in the developing stage, manual intervention is still required in order to attain higher accuracy. 

To understand how beneficial AI can or cannot be for patent searches when combined with human intelligence, let’s first understand the needs and benefits of doing a patent search.  

 A patent search is basically performed before an inventor invests a lot of time and money in the process of filing the patent to ensure that the idea or innovation is novel and unique and to avoid rejection from the patent offices at a later stage and for checking validity or invalidity of a granted patent. A patent search also must be done before a company decides to develop/launch a new product to check if their product might infringe any of the patents that already exist that help in taking a decision so as to avoid spending time and money on any expensive lawsuit which can be filed by the inventor/assignee of the prior art patent for infringing their patent. 

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How is AI used for patent searching? 

AI is a union of multiple technologies like Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), and Natural Language Processing (NLP). These technologies can be used individually or in combinations for patent categorization and for obtaining relevant results. NLP is used for suggesting innovation context-specific keywords and their synonyms that prove to be very useful in query formations. AI models are trained using keyword datasets, but AI is still effective in patent searching as it also works based on context rather than just focusing only on keywords. Many AI tools contain reactive knowledge through which the AI model improves its learning and senses with every followed search performed on them. 

Now let’s discuss individually, the advantages of using AI and human intelligence in patent searching and then we may move forward and discuss how manual intervention may help to overcome the gaps left by AI in the patent search industry thus explaining a hybrid approach of using AI plus human intelligence for searching patents. 

Advantages of AI: 

  1. Increases time efficiency: Patent searches are required in various fields such as engineering, medical, chemical, and pharmaceutical domains and to perform various types of searches like invalidity searches, patentability searches, Freedom-to-operate (FTOs), and landscape searches. AI can also help in faster searching of patents by suggesting keywords and their context-specific synonyms to the analyst for the formation of search queries. Hence, with the use of AI, both human effort and time spent searching for patents is significantly reduced which helps in increasing efficiency.
  2. Faster validation of innovations: AI-based patent search tools are automated and hence they provide a faster result set of prior arts related to the invention so that the innovator can know quickly and in advance which part of the claim elements they can modify to get their invention granted easily and quickly.
  3. Reduces cost: AI-based patent search tools are not much expensive when compared to getting patent searches done through a human analyst. Many patent search tools which are based on AI also provide a facility of pay-per-use to the customers which is a very cost-effective option.
  4. Patent relevancy by raking patents: AI-based tools not only perform patent searching but also rank the resulting patents in the order of relevance with respect to the context of innovation. 

Advantages of manual searching: 

  1. Higher refinement capabilities increase relevancy: Manual searching uses human intelligence which can understand the concept of a patent beyond keywords and in-depth, which can bring out a better and more relevant result set when compared to AI-based searching only. Using only automation may give a result set with no or very low relevancy. 
  2. Searches in native languages: AI-based tools lack training in native languages; therefore, AI-based automated patent search tools are not that efficient. Hence, there is still a need for native language manual searching. 
  3. Can perform NPL searches better: AI-based patent search tools are not well-trained for searching non-patent literature in different technology domains. Even if it is used it is incapable of providing accuracy when compared with manual searching. 
  4. Humans have the capability of learning the novelty from the file wrapper of the invention: Learning the novelty of patents is a vital part of invalidity searches. Understanding the novelty of the patent helps the searcher to perform a search by focusing on the specific part. Also, for effectively granting the patent, the novelty of the prior art can be extracted from the file wrapper and more innovation can be performed in that area. Hence, reading the file wrapper of the prior art always needs to be done manually as AI is incapable to locate the exact novelty of the prior art. 
  5. Terminology variations and interchangeability: Various technology domains include terms that are used interchangeably. In order to increase the relevancy of prior-art search, terminology variation, analogy and diversification can be done in a better way by a manual searcher. 
  6. Can avoid 103 rejections: Manually reading and understanding the file wrapper also help to avoid obvious rejections as manual searching can differentiate between similar technologies or their combinations. Hence, manual searching helps in customizing patent searches which ensures a reduction of 103 rejections. 

A hybrid model: Combining AI and human intelligence 

During the initial stages of development which comprise the ideation stage and validation of the concept, technology, and application domains, manual searching can consume a lot of time and effort for an analyst. Therefore, at this stage, analysts must leverage the benefits of AI as it can make this process faster. AI-based patent search tools are also cost-effective and faster compared to manual searching. These tools can significantly reduce the time for searching prior arts from multiple days to a few hours although, manual searching has an upper hand when it comes to the accuracy of search results, finding relevant and context-specific results, and avoiding obviousness-based rejections as per 35 U.S.C. 103. So, AI can provide a helping hand at the start of a manual search to speed up the process. 

Hence, AI should be used to assist manual searchers to save time and cost and to provide high-quality results at the same time. This makes way for a researcher to use a hybrid approach to get the best out of both methods of patent searching. 

We at TT Consultants, provide different services such as validity/invalidity searching, patentability searching, patent portfolio/ranking, landscape, FTO, etc. using an amalgamation of manual and AI-based in-house tool (i.e., XLSCOUT). 

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