Over the past few years, research using artificial intelligence has become an important part of engineering and scientific activity. Modern algorithms can analyze huge volumes of technical information, including scientific publications, development reports and millions of patent documents. As a result, the use of AI in scientific research is gradually becoming the standard for companies, universities and development departments.
Today a neural network helps not only to analyze data, but also to speed up the search for technical solutions, identify technology trends and find promising areas of development. The role of AI is especially noticeable in patent search, where algorithms can process large patent information databases faster and more accurately than a human.
How engineers use patents as a base of technical solutions
According to estimates by international patent organizations, more than 70–80% of engineering knowledge is published in patent documents. That is why, for engineers, searching for patent information becomes an important part of their work.
Patent databases contain detailed descriptions of designs, technologies, materials and production methods. When a specialist conducts patent research, they can see which solutions have already been proposed, which approaches other companies have used and which technologies are at the stage of active development.In the past, such analysis required a significant amount of time: thousands of documents had to be studied manually. Today, artificial intelligence makes it possible to automatically analyze large volumes of data and find relevant solutions.
A real case example: how the AI engineer “POSINT” automates search across patent databases
Let us consider how this works in practice.
An engineer uses the “POSINT” platform to analyze a technology for deep processing of petroleum residues. The task is to conduct an international patent search, identify analogues and assess the novelty of the development.
A description of the technology is uploaded to the system: a multistage process for processing heavy hydrocarbon feedstock, including solvent deasphalting, hydrodynamic cavitation, ozonation, membrane filtration and electrodialysis.
The engineer uploads the technology description to the system. Next, AI for patent search comes into play. The algorithm does not simply look for word matches. It analyzes the essence of the development itself. The system identifies the key elements: the use of centrifugal extractors with a high separation factor, cavitation treatment in a Venturi-type reactor, the use of an ozone-oxygen mixture instead of hydrogen peroxide, and a closed cycle of reagent regeneration through electrodialysis.
Then the most important thing happens: «POSINT» starts an AI search of patent documents by meaning. It finds solutions that at first glance may be described quite differently. For example, processes for extracting metals from hydrocarbon feedstock, hydroconversion technologies or methods for processing petroleum residues.
At the same time, the system compares not just the names of processes, but their internal logic. As a result, it becomes clear that individual elements do occur in the known solutions — extraction or thermal treatment. But there is no comprehensive combination of features like that in the original technology.
It is at this moment that the engineer gets the key insight. Thanks to patent research carried out with the use of AI, it becomes clear that the development does not duplicate existing solutions. Moreover, it combines several approaches in a way that has not previously been described in patent literature.
In addition, the AI assistant “POSINT” makes it possible to quickly assess risks — to conduct a freedom to operate (patent clearance) search and understand whether there are any overlaps with valid patents.
As a result, what used to require weeks of work and high expertise in patent analytics is now done in hours. And most importantly, the engineer gets not just a list of documents, but a structured understanding of where the known prior art ends and a truly new development begins.
Searching for analogues and alternative designs
When developing a new product, it is important for an engineer to understand whether similar solutions exist. For this purpose, a search of patent documents and an analysis of analogues are carried out.
With the traditional approach, specialists search for patents by keywords and classifications. However, this method often gives limited results, since different developers may use different terminology.
Modern AI systems for patent search work differently. They analyze the meaning of the text, technical parameters and relationships between technologies. This makes it possible to find even those solutions that are described in other words.
As a result, the engineer can more quickly discover alternative designs, new approaches to solving the problem and even unexpected technological combinations.
An example from the practice of an AI engineer: how the service found analogues for a pharmaceutical development
This is clearly seen in the example of a pharmaceutical development — the creation of new antibacterial drugs based on modified sulfonamides. The engineer’s task was to increase the bioavailability of the compounds by converting them into an amorphous state and at the same time to improve permeability through bacterial membranes.
Technologically, the solution looked quite complex: spray drying from a binary solvent mixture at a controlled temperature was used, as well as chemical modification of molecules with the introduction of hydrophobic fragments. In parallel, continuous microreactor synthesis was applied, followed by integrated purification and drying, which made it possible to sharply shorten the duration of the technological cycle.
When AI is used for patent search, the system analyzes the very structure of the technology. It “understands” that this concerns pharmaceutical substances in an amorphous state, spray drying processes, flow reactors without moving parts and chemical modification of heterocyclic compounds with nitrogen and sulfur atoms.
As a result, the service finds relevant analogues: processes of continuous synthesis of sulfonamides in microchannel reactors, technologies for obtaining amorphous dosage forms, spray drying methods and microreactor processing. At the same time, it becomes clear that individual elements are already known, but their combination within a single continuous process is encountered extremely rarely.
It is at this stage that it becomes clear that the development does not simply repeat known solutions, but combines several technological approaches into a new system. This makes it possible to speak of compliance with the criteria of novelty and inventive step, and therefore of the prospects for patenting and successfully bringing the product to market.
Such an approach to conducting patent research allows the engineer not only to find analogues faster, but also to better understand the technological landscape in which the new development is being created.
Check your idea for uniqueness: search for analogues with the patent attorney’s AI assistant “POSINT”
If you are developing a new technology, it is important to understand as early as possible whether a similar solution already exists.
Conducting a search for patent information at an early stage helps to avoid duplication of developments, reduce risks and save resources. At the same time, a classical patent search carried out on your own often takes weeks and requires high expertise.
Service «POSINT»service uses AI for patent search to find analogues in a short time, assess novelty and show where your development stands relative to the world state of the art.
Leave a request — and you will receive a preliminary analysis of your idea and recommendations for further steps.
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How AI helps avoid duplication of developments
One of the main tasks of conducting a patent search is preventing duplication of developments. Sometimes companies spend months or even years creating a technology that has already been patented.
The use of AI in scientific research helps to avoid such situations. Algorithms analyze patent databases and identify existing solutions at an early stage of development. This allows engineers to:
- determine the level of novelty of the idea;
- identify existing analogues;
- conduct a freedom to operate (patent clearance) search;
adjust the design before filing an application.
Conclusion:
In this article we examined how the use of AI in scientific research is changing the approach to patent search and the analysis of technical solutions.
Today engineers no longer need to spend weeks manually reviewing thousands of documents. This task is taken over by AI systems for patent search, allowing them to focus on development and creative tasks rather than routine.
The AI assistant “POSINT” was created precisely for this. You can quickly find analogues, conduct a patent clearance search and be confident in the novelty of your solutions.
While some continue to work “the old way”, others are already accelerating their scientific research and technology development with the help of AI. Do not fall behind your competitors. Try “POSINT”
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