How a hybrid approach can optimize success


“Although AI-based patent search tools are agile and user-friendly, they can be integrated with manual searches conducted by experienced analysts for better results. “

With the expected growth in the size of the global artificial intelligence (AI) market, it is evident that AI is rapidly becoming the solution to most software and service needs. AI has even infiltrated our homes. For example, we are seeing more and more smart home systems that integrate Internet of Things (IoT) technology with an AI master virtual assistant.

Without a doubt, technology has also made room in the intellectual property-based services sector. For example, to support patent search, there are many AI-based automated patent search tools. Although many of them are still in the formative stage, these tools are likely to mature. The next question for innovators is whether to take advantage of affordable AI patent search tools or invest in outsourced manual patentability searches.

With AI-powered patent search tools available in subscription-based models at affordable prices, the dilemma mentioned above becomes even more compelling. So while AI-based patent search tools are nimble and user-friendly, they can be integrated with manual searches conducted by experienced analysts for better results. The following article compares the two options and defines scenarios in which these models can be deployed.

Manual patent search still wins overall

  1. Comprehensive research – Artificial intelligence and machine learning (ML) algorithms are still in their rudimentary stages of training and development. Therefore, they are still unable to perfectly replicate the full range of capabilities of a true human analyst while performing exhaustive research. Therefore, human intelligence can be deployed to optimize results.
  2. Opinion – Although AI-based patent search tools can give innovators a quick idea of ​​whether to innovate, they do not provide a comprehensive analysis of all the innovation white spaces in the landscape especially patents. Manual research can provide such an opinion to innovators.
  3. Improved quality – Additionally, to improve the quality of prior art claims, innovators seek patentability notices for their drafted claims. Therefore, manual searches are also needed to improve the quality of the patent application.
  4. Reduced time – From Invention Disclosure Forms (IDFs) to patent filing, manual searches take less time for drafts of independent claims. However, when it comes to speed in general, automated searches are always faster.
  5. Seamless refinement – When it comes to refining search strategies according to a particular topic, human intellectual property analysts with expertise in the respective discipline have the upper hand. The human element often adds sophistication to patent search results. However, in automated searches, we usually only get a list of results and no relevant analysis to go with it.
  6. Mother tongue research – As the AI ​​is not yet trained in several native languages, patent searches in this case are only possible with manual intervention. Translators and native speakers should be involved in these patent searches.
  7. Identification of non-patent literature – Automated searches are still not sufficiently competent to search for non-patent literature in different technological disciplines. Therefore, in this context, manual patent searches are much more complete and precise.
  8. Learn from the prosecution history – In a scenario where invalidity research is required, we must learn from the state of the art lawsuit history closest to the technology under development. Analysis of these, which must be performed by a human, can help identify unique areas of innovation that can be used further for innovative product development.

When to choose automated search

  • Quick validation: Automated searches can be used to quickly validate concepts at the ideation stage. Such AI-based automated research tools give innovators access to existing prior art so that they can build their innovations around these existing patents.
  • Prior Art for IDS Submission: When submitting the IDS, the relevant prior art must be submitted to validate the innovation. Automated searches can be used to include prior art in the IDS and to reduce the processing time for patent applications.
  • New technological fields: Inventions that are unique and groundbreaking often pose a problem for in-house Subject Matter Experts (SMEs) when searching for relevant new prior art. In such cases, the subject may not have direct prior art, but AI-based patent search tools can be used to understand and draw similarities from the latest technologies to identify prior art. existing potential.
  • Cost reduction: Since most AI-based patent search tools are relatively affordable, there is no need to maintain a budget for outsourcing patent search services. Many AI-based patent search tools use a pay-as-you-go model for patent searches.

When to choose manual search

  • Crowded patent spaces: For patent spaces that are crowded, manual searches can help differentiate between existing patents and prior art for the given invention. Since the subtle differences in this prior art give rise to patentability, it is rather advantageous to differentiate this subject matter from the available prior art.
  • Interchangeable terminologies: Likewise, these crowded patent spaces often have terminologies that overlap with those of the invention. Some areas of technology have terms that are used interchangeably. To perform a comprehensive prior art search, all of these terms must be searched manually – in a manner relevant to the given context.
  • New fields of application: Inventions which are not new in themselves, but which are new in their application, require extensive manual research of the prior art to identify any prior art similar to the given application. In doing so, the claims can be worded in such a way as to ensure a high license value in the future.
  • Contemporary research: With a constantly expanding patent market and increasing competition in patent filings, the need to understand the state of the art (SOA) in terms of validity has become quite important. Therefore, contemporary research that incorporates both novelty and SOA research is needed to understand competitor patent filings. It also allows innovators to draft their patent claims by adapting their patent filing strategy to that of competitors.
  • Avoid 103 rejections: To avoid obvious rejections under 35 USC §103, manual searches can help innovators analyze the patent lawsuit history of similar patents. This can help them learn from patent prosecution strategies that have similar combinations of technologies. Such a personalized patent search can thus ensure that the chances of obtaining a rejection 103 are reduced.

Complementary use of automated and manual searches – the hybrid model

For organizations with large research and development (R&D) teams working on many innovations simultaneously, manual research done during the initial stages of concept ideation and approval can be time consuming. AI-based tools solve this current problem by providing rapid results of prior art research so that these R&D teams can take advantage of the role of intellectual property in their processes. Even though most of these AI-based tools are still in their infancy in training and data entry, they can supplement manual patent searches in some cases. While AI-based tools are economical and fast, manual research is more reliable and relevant. Using a combination of the two can guarantee truly superior results.

Prasad Summit

Sumit Prasad is an innovation strategist by profession with over 8 years of experience as a patent expert and intellectual property consultant. He plays a key role in cultivating innovation and raising awareness of intellectual property among startups, MSMEs, GCoE R&D groups and helps them strategize for their intellectual property activities. At Sagacious, he leads the “IT for IP” initiative which allows him to leverage his experience in intellectual property to create algorithms using AI, ML, NLP, automation, and more. and develop tools / software that can help solve problems in the intellectual property sector. Sumit also helped Sagacious automate IP processes and development tools for increased efficiency and better interaction with customers.


Norma A. Roth