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AI&Brevets: the new reflexes of innovators

5 min

Patents are a mine of technical information, covering decades of innovation on a global scale. Yet making effective use of this mass of data remains a challenge for R&D and technology watch professionals. The information overload is such that weak signals heralding innovations can easily go unnoticed. With the growing volume of documents (patents, articles, posts, etc.), manual analysis is becoming cumbersome and time-consuming.

Fortunately, artificial intelligence (AI) now makes it possible to automate scientific monitoring and make the most of patent information. This article explores these new digital reflexes through concrete examples, to show how AI transforms a complex task into a strategic lever for innovators.

Contents :

Effortlessly monitoring weak patent signals
Mapping a technological landscape
Reading between the lines… without reading everything
Moving from a mass of information to a usable summary

Find out more

Aller au chapite :

Monitoring weak patent signals effortlessly

Mapping a technological landscape

Reading between the lines… without reading everything

Moving from a mass of information to a usable summary

1. Monitor weak patent signals effortlessly

Monitoring patents makes it possible to track technical advances, identify emerging trends, spot new players or partners, and prevent the risks associated with intellectual property. Thanks to alert apps such as Opscidia, this strategic monitoring can be automated. Real-time alerts analyse new publications and flag up relevant developments. It is thus possible to configure alerts on technical keywords, competitors or emerging fields, and to receive the relevant patents immediately.

For example, within the Opscidia platform, an intelligent alert system can automatically warn an R&D team as soon as a competitor company files a patent in a strategic field. This type of tool makes it possible to pick up on weak signals before anyone else.

Renault, for example, a leader in a number of technological areas relating to electric motors and on-board systems, uses this type of monitoring to make sure it doesn’t miss out on any major innovations in its key areas.

The aim is to quickly detect whether a competitor is starting to accelerate in a particular area, such as high-efficiency motors or centralised electronic architectures. By monitoring patent filings in these areas, Renault ensures that it stays ahead of the technological race.

2. Mapping a technological landscape

Beyond detecting specific alerts, AI excels at providing an overview of a technical field by exploiting datavisualisation. By aggregating thousands of patents, we can map out a complete technological landscape, showing which players are dominant, which companies publish the most patents in the field, how patent activity has evolved over time, and where the innovation hubs are located geographically.

In the very fashionable field ofgenerative AI, the number of annual patents has literally exploded in just a few years, rising from just 411 patents in 2014 to more than 2154 in 2024.

Breakdown of patents in generative AI by country of origin. This visual illustrates the predominance of the United States, which accounts for almost 40% of AI-related patent filings, i.e. around six times more than China.

Similarly, a mapping by players would reveal that technology companies (IBM, Samsung and Canon) hold the largest patent portfolios in this area, alongside a number of major players such as Amazon, Microsoft and Google…

Thanks to interactive graphs generated in just a few clicks (for example with the Ecosystem Chart function on the Opscidia platform), users can explore this data from a variety of angles.Datavisualisation makes information immediately perceptible that a table of raw figures would not bring out.

3. Reading between the lines... without reading everything

A patent is a dense document, often consisting of dozens of pages of technical descriptions and legal claims. For a human being, analysing several patents in search of a specific piece of information can quickly become tedious. That’s where AI comes in: thanks to advances in automatic language processing (NLP), it can analyse patents in depth and extract the essential information without having to read everything.

Specialised tools now make it possible toquery a patent corpus directly by asking a question. The user formulates a request (e.g. what solutions have been proposed to improve energy density?), and the AI identifies the relevant answers in the documents, quoting the relevant passages. This approach transforms research: instead of reading dozens of patents, the analyst obtains a targeted, sourced and contextualised response. This type of interaction saves considerable time, while ensuring the traceability and reliability of the results.

AI-based patent analysis tools don’t just summarise: they can also extract specific elements without human intervention. Examples of relevant information that can be automatically retrieved include:

Key bibliographic data: names of inventors, applicant, filing date, technical classifications, etc., for each patent.
Relationships and citationsreferences to other patents or articles, priority links, patent families – useful for tracing the lineage of an innovation.
The heart of the technical contentIdentification oftechnical problemsaddressed by the invention andsolutionsoften scattered throughout the description.
The innovative points claimedextraction of the main claims, possibly reworded in plain language.

Thanks to these tools, “questioning” a set of patents becomes much less tedious. AI guides the analyst towards the essential, filtering out noise and redundancy, while preserving the technical substance.

4. Moving from a mass of information to a usable summary

Extracting information from patents is not enough: it has to be synthesised in a clear and useful way. AI facilitates this stage by generating comprehensible deliverables from complex sources. For an innovation expert, this means you can entrust AI with the creation of a state of the art or a thematic report, and get back a structured, sourced, ready-to-use document.

The uses are varied. In a funding application (CIR, grant, collaborative project, etc.), a state of the art is often requested to prove that the product is new. Summarising patents helps to set the technological context and justify the innovation. On the R&D side, a sectoral summary can identify gaps or opportunities that have not yet been fully explored.

Recent AI tools excel at multi-source synthesis. For example, the Opscidia platform offers a Report Assistant that creates a plan from a bibliography, produces summaries by theme, and integrates a chatbot to enrich the content. The result is a sourced, structured report produced in record time, saving up to 60% of your time.

These tools are not simply text generators. They assist the expert, who remains in control of the content. The AI proposes a structure and summaries, but it is the professional who validates and refines them. This collaboration means that reliable deliverables can be produced quickly: state of the art, watch notes or ready-to-use summaries.