Generative AI: A Powerful Tool for Comparing Inventions and Patents
By Alex Paikada
Our species progresses by the cumulative process of technological advancement. An invention is a novel idea or a new solution to a problem that is not obvious to someone skilled in the relevant field. It could be a product, a process, a machine, or even a method of doing something that is new, useful, and non-obvious. Inventions can be tangible objects or intangible processes and methods.
Invention and innovation happen through a lethal combination of experience and imagination. Innovation encompasses conceptualization, creativity, non-obviousness, and variety, incorporating products, methods, processes, machines, and even new chemical compounds, each contributing distinctive features to the seminal aspects of innovation.
Relecura, a force to reckon with in the IP and innovation space, has been relentlessly exploring Generative AI and looking for ways to incorporate it in intuitive ways, into its products and services. As a way to share its findings on the numerous applications of the technology that have immense value for its customers, it has decided to share an overview of its findings in a series of articles beginning with this one. The idea is to start with a generic view of how Generative AI can accelerate innovation before delving into slightly more detailed aspects of its use cases.
Analytics for managerial ease
For fair play and to preempt duplication and friction, it is important to compare and weigh down the patent assets. Finding similarities and differences between patents or innovations can be a complex task that often involves a combination of qualitative analysis and quantitative methods. When it comes to qualitative analyses, the operations involved are reading and understanding the patents in question to identify the key components, processes or methods, generation of a table for their comparison, and identification of unique features. It also involves the examination of citations and references, the legal status of a patent (granted, pending, expired), and the studied opinion of the concerned subject matter experts.
The job's quantitative aspect involves text mining and Natural Language Processing (NLP). This is used to analyze the frequency of terms in each patent, seeking commonalities and differences. Patent databases and AI-powered tools like Relecura are utilized for comparing patents side by side. Additionally, the comparison includes patent classification codes to identify similarities in technological domains. Citation analysis is performed since frequently cited patents may share similarities in their technological approach. Claims analysis is also conducted, as analyzing claims can reveal similarities and differences in protected inventions. Moreover, visualization tools are accessed to create diagrams or graphs. These representations depict the relationships between different patents based on their components, methods, or technologies.
The debut of Generative AI into the domain
Generative AI can be a powerful tool for comparing inventions and patents by automating various aspects of the analysis process. In all likelihood, Generative AI capabilities are going to take over many focused analytical operations, saving man-hours and preempting lapses and manual errors. AI emerged as a vast domain of computer science that requires the prodding of human intelligence. Generative AI, however, has evolved as a promising subset of AI and it involves creating models capable of generating new content, such as images, text, audio, or other types of data. The term "generative" refers to the ability of these models to generate new, original content rather than simply classifying or recognizing existing data.
The procedural dynamics of Generative AI in the comparison and differentiation process have the following stages:
1. Data collection and processing: Here, the basic raw material to build on includes patent texts, diagrams, and other relevant documents. At this stage, the data jumble is purified and made amenable to a seamless process by removing special characters, standardizing text formats, and tokenizing the text into smaller units for analysis to make the information consistent.
2. Selection of the most effective Generative Model: Many organizations have hit the markets with different models after the one touted by OpenAI. Companies like Relecura have advanced a great deal on this road.
3. Fine tuning of the working model: The model could be made more robust and the performance parameters could be optimized using a smaller dataset specific to the domain.
4. Identification and generation synopses: Summarizing patents can involve condensing lengthy documents into concise, informative paragraphs. This removes a great deal of physical strain and focused labor.
5. Comparison: The model finalized is employed to compare the generated patent summaries and it highlights similarities, differences, and unique features in the inventions; also, technical aspects, applications, and potential implications are brought out.
6. Patent claim analysis: This part is to understand the scope and uniqueness of the invention in question, as well as to identify overlaps and differences in the inventions.
7. Visual analysis: In certain cases, generative models are handy in analyzing diagrams and bringing out unique features.
8. Post-processing scrutiny and verification: This is to cross-check the veracity and accuracy of the information extracted.
9. Iteration: Based on the test results and insights, the model parameters are adjusted and queries are modified to optimize the results. This iteration cycle is continued until the right results are generated.
Once the system is conditioned to perform the analyses and bring forth insights that are in conformity with the pre-designed quality and accuracy, it is possible to extract information catering to different interests.
1. Generation of relevant technical descriptions: Generative AI can create detailed technical descriptions of inventions mentioned in patents. This can help in comparing the intricate technical aspects of different inventions.
2. Novelty and prior art search: The uniqueness of a particular invention, if any, can be brought out by comparing the generated content with existing patents and prior art.
3. Automation of prior art searches: Generative AI can automate searching for prior art by generating queries and summarizing relevant patents.
4. Analysis of patent citations: It is made possible by generating summaries or analyses of the patents being cited. Understanding why certain patents are cited can provide valuable context for comparing inventions.
5. Language translation for comparison: When you are analyzing multinational and multi-linguistic patent-scape, automated translation enables easy analyses transcending language barriers.
6. Predictive analytics: By analyzing fairly large datasets, Generative AI can predict trends in patent filings and innovations. By analyzing large datasets, the model can generate insights into emerging technologies and their potential impact on existing inventions.
Another advantage of Generative AI-powered tools is to have specific formats such as charts, graphs, or reports. Customized formats can make it easier for stakeholders to interpret and act upon the comparative analysis results. Also, it is possible to integrate interactive user interfaces for high efficiency and adaptability. Another possibility is to automate the patent evaluation process. Predicting the patent portfolio's value can be of great advantage to businesses and investors.
In the final reckoning, the credibility and reliability of results brought out by Generative AI depends on the quality and diversity of the data availed of for training and comparison. It is equally important to update the model with the latest data or extracting contemporaneously relevant actionable information. Insights are at the mercy of the quality and credibility of input data for training the model.