A technology enterprise focused on the research and development of autonomous driving decision algorithms is facing dual challenges in the process of technological innovation, including patent layout and academic achievement transformation. The "Dynamic Path Planning Algorithm Based on Multi Sensor Fusion" developed by the company has achieved an accuracy rate of over 98% in identifying complex road conditions. However, in the early stages, it only focused on technical implementation and did not synchronize patent protection and academic paper publication, resulting in the leakage of core algorithm details in industry exchanges and facing the risk of counterfeiting; Meanwhile, due to the lack of high-quality academic paper support, enterprises lack competitiveness in high-end talent recruitment and industry university research cooperation.

After our intervention, we first conducted technical disclosure and core innovation point sorting, and found that the algorithm has 12 key innovations in environmental perception modeling, decision logic optimization, and real-time response mechanism. In response to the core requirement of balancing patent protection and academic disclosure, we have developed a collaborative strategy of "layered protection+hierarchical disclosure": for the 5 innovative points involving core parameters and underlying architecture, priority will be given to applying for invention patents for strict protection, and the scope of protection will be expanded by using a "higher-level concept+specific implementation examples" approach when writing claims; Six non core innovations, including algorithm application scenarios and performance optimization, are planned as academic paper materials and will be published after the patent application is made public to avoid damaging the novelty of the patent due to early publication of the paper.

In terms of patent layout, we assisted enterprises in completing 15 patent applications, including 9 invention patents covering core algorithm modules, including core technologies such as "an obstacle prediction method based on spatiotemporal feature fusion", forming a complete patent protection network. In terms of paper publication, I guided the R&D team to extract innovative points and wrote three academic papers in a standardized manner, including two papers included in the IEEE Transactions on Intelligent Transportation Systems and one paper presented orally at a top academic conference. The papers cleverly avoided patent core technology details and only disclosed application effects and algorithm frameworks.

After the implementation of the project, the coverage of the enterprise's patent portfolio increased from 35% to 92%, and two potential infringement cases were successfully intercepted. After the publication of academic papers, the company's academic influence has significantly increased, attracting three PhDs to join the R&D team and establishing joint laboratories with two universities, receiving a funding of 8 million yuan for industry university research projects. The core patent "Multi sensor Fusion Decision System" has become an important bargaining chip for enterprises to participate in the formulation of national level autonomous driving standards, with technology conversion profits exceeding 50 million yuan.