## 4 Discussion
The bifurcation of the POC that evolved both digitally (via simulation) and physically. 2. The later integration where three systems merged into one--point 10--namely an artificial vision system tailored for the specific application--point 9--and the handling surface (successfully tested both virtually--point 7--and physically--point 4). Finally, we decided to present the case of developing a multimodal system [53] capable of analyzing sketches, technical drawings, and patent images--visual inputs that are predominantly black and white, low in texture, and structured around lines. This case differs significantly from the previously discussed tenders, where general-purpose NLP technologies were applied to specific domains. Here, the starting point was general-purpose technology that required substantial adaptation to a highly specific and visually constrained input space. As such, this approach reflects a more exploratory and incremental trajectory--advancing both the system's TRL and its ability to handle diverse tasks and use cases over time. The company had a structured database of 1.5 million patents, with aligned images and textual components (titles, metadata, abstracts, descriptions, and claims). The initial plan was to use Meta AI's Segment Anything Model (SAM) [54], assuming the project would involve straightforward development with little need for research. However, early experiments revealed that the SAM, despite its high TRL (8/9) in natural RGB image contexts, was ineffective on black-and-white technical drawings and low-texture sketches, performing closer to TRL 2 in this domain. Similarly, while the system conceptually aligned with high Industrie 4.0 maturity (due to multimodal data integration), it began at a low operational level (MI 2) with limited system visibility and data transparency (see Figure 5). This gap between expected and actual maturity led to a reframing of the project--from integrating a pre-trained model to developing a new domain-specific approach for linking visual and textual patent data, progressively improving both technological readiness and system capability. The first phase was oriented to connect separated information and move from TRL 2 to TRL 3. At this early stage, information extracted from patents (black-and-white technical images and textual descriptions) was unstructured and unlinked at a fine grain. Due to the  
Figure 5: Roadmap development of multimodal system capable of analyzing sketches, technical drawings, and patent images. poor performance of generalist models like the SAM on B/W sketches, a custom linking system (algorithmic) was developed to associate image segments with relevant textual components. This enabled rudimentary semantic anchoring across modalities, forming the technological and conceptual base (MI 2 and TRL 3). Once cross-modal connections were established, the system evolved to build structured digital representations of each patent. These included image components with spatial coordinates and text labels. The improvement in data modeling and internal representation allowed users to \"see\" the internal structure of the documents, and the algorithms could operate consistently across test cases. This increased technical maturity (TRL 5) and this transition also enabled the visibility of information relationships (MI 3). A similar evolution can be observed when the system evolves, reaching the ability of providing engineering insights. Subsequent developments integrated improved image segmentation techniques (including the partial use of the SAM where effective) and complementary algorithms for post-processing, component validation, and text anchoring. Basic machine learning modules were introduced to correct errors and refine component classification. The system, at this point, was able to support engineering-level understanding, not only retrieving components but offering contextualized interpretations and insights.