Prior art search is mandatorily conducted before filing a patent application. This is done to verify if the innovation in question adequately meets the prerequisites of patentability, i.e., novelty, inventiveness, non-obviousness, etc. Prior art search is considered a due diligence task, defining the scope of protection of patent claims. The objective behind conducting a detailed prior art patent search is to: Avoid filing patents and prosecution proceedings if the innovation is less novel or obvious; this can save plenty of money in the future. It helps to ensure that a patent holder will not be indulged further in patent litigations after a patent grant. Inform the applicant about everything related to the prior art in the same subject matter and provide useful insights into competitors’ products and corporate strategies. Enable the inventor to make informed and well-calculated decisions about the feasibility of applying. Some of the traditionally used approaches for prior art search are keyword search, citation search, name search, classification search, boolean search, positional search, date search, patent reference or identification number search, etc. Shortcomings Of Conventional Prior Art Search Strategies In this ever-growing, fast-paced world, millions of patent documents are being published globally. Scrutinizing a vast database manually for prior art searches is an arduous task as well as time-taking. It involves searching, analyzing, and investigating several patent publications. Extracting all the relevant patent and non-patent literature sources available in multiple formats, languages, and references is a very laborious and tedious task to perform. The most common challenges in employing conventional techniques for patentability searches are data processing errors, errors caused by language pitfalls, incorrect syntax, classification errors, the considerable possibility of false positives and false negatives, time-consuming, excessive costs, etc. To rule out all these limitations, several minds have been employing new technologies like artificial intelligence, machine learning, etc. Advantages Of Automation In Prior Art Searching Automated prior art search techniques using Artificial Intelligence, Machine Learning, and Neural Network language models can effectively address the drawbacks of manual search methods. These technologies improve the process in the following ways: Increased quality and precision of the prior art search. Considers researcher’s intent appropriately. Provides in-depth insight into competitor’s strategies. Reduces false positives and false negatives. Increases the overall efficiency and accuracy. It saves plenty of time. Dramatically reduces the involved cost. How To Deploy AI Techniques For Prior Art Search AI techniques effectively automate patent-related searches by replacing conventional search methods like Boolean search, keyword search, etc., with AI-enabled semantic search. This approach helps to retrieve documents that consist of similar concepts and logic. Search results’ accuracy is enhanced using these technologies as the complete patent application is compared with all the existing publications offered by the patent database. By this means, much more relevant information is extracted. This information considers and includes keywords used within the respective searched documents. This addresses the drawbacks of the keyword-based patent search that often delivers inaccurate and incomplete results because it does not consider synonyms or abstract terms related to the given keyword. The patent search engines such as ESPACENET, PATENTSCOPE, TOTAL PATENT, Google Patent Search, USPTO Web Patent databases, etc., fail to deliver relevant documents. These shortcomings are only because the specific keyword is taken into account and not the entire patent application text. A Deep Belief Neural Network, a semantic model comprising concepts and topics in mathematical vectors, extracts various logics and meanings from patents. Neural Network is a subset of Machine Learning that enables a computer to scrutinize large volumes of literature sources and further derives meaning from the examined data. How To Assign A Vector To Each Searched Document: Obtain a dataset of related patents from the patent database. Categorize patent publications using a system that emerged from Neural Network analysis of the literature. Emphasize the various categories and concepts present in the entire data piece. Assign numerical vectors to patent texts and documents according to the category. Compute full-text similarities between two patent documents based on these categories, making the process automatic and reliable. In a nutshell, AI automates prior art patent searches by intensively comparing the patent application’s full text with the existing patent and non-patent literature. After this, a similarity score is obtained based on which one categorizes and ranks the patents. This is done according to the degree of resemblance with the concerned patent application. The sources exceeding a certain threshold are regarded as prior art. Since AI techniques deployed for patent searching are based on a semantic approach, the artificial neural network algorithms tend to encode concepts and logics of a document into highly similar semantic vectors. This step is performed regardless of the terminology used for searching patent documents. This overcomes the pitfalls of a language barrier involved in manual keyword searches as AI displays results beyond the vocabulary used. It is conducted based on the codes associated with the concepts attached to the inserted keyword. To further enhance the accuracy of the obtained results with less noise and fewer false positives, it is always advisable to incorporate a language model and a literature knowledge graph. To Conclude Prior art or patentability search is mainly conducted before proceeding to file a patent application. It is usually done by consulting a patent attorney to ensure that patentability’s prerequisites are met appropriately. Patent attorneys and experts mostly rely on keyword-based search engines that often deliver erroneous results consisting of many false positives and false negatives while leading the patent search. While false positives further add to the patent examiners’ stress as they exclude irrelevant documents, false negatives may lead to the patent’s fallacious granting. To remove the hurdles associated with manual prior art search techniques AI techniques are employed increasingly for prior art patent search. With the help of AI-based semantic search engines, the patent application’s entire text is compared with the database’s pre-existing literature. The semantic-based model primarily emphasizes the idea and key concepts rather than the search query’s expressions. As a result, more precise and relevant outputs are extracted, avoiding irrelevant patents. Artificial Intelligence and Machine Learning techniques prove to be very useful for conducting a patent search. Thus, it dramatically decreases the manual workload, increasing the search results’ accuracy and efficiency, and saves time by understanding the researcher’s intent.