The "personalized intelligent teaching system based on knowledge graph" developed by a certain educational technology enterprise has achieved significant results in promotion and application, but it faces problems such as insufficient proof of core technology innovation and weak data support for achievement transformation when applying for provincial scientific and technological progress awards. The system achieves dynamic learning path planning by constructing a knowledge graph covering 12 subjects in primary and secondary schools. The average grades of students in pilot schools have increased by 15%, and the efficiency of teacher lesson preparation has increased by 40%. However, the enterprise only listed functional descriptions in the preparation of award materials, did not systematically present technical breakthroughs, and lacked intellectual property layout as evidence of innovation. The initial application did not pass the initial evaluation.

After our intervention, we first conducted a deep deconstruction of the system technology and extracted three core innovative points: firstly, using adaptive learning algorithms to achieve precise matching of knowledge nodes, solving the problem of "one size fits all" in traditional teaching; Secondly, we will develop lightweight knowledge graph construction tools to shorten the subject graph production cycle from 6 months to 1 month; The third is to develop a multi terminal collaborative teaching module that supports seamless integration between classroom interaction and personalized learning after class. In response to these innovative points, we have assisted enterprises in supplementing intellectual property proof materials for 12 invention patents and 8 software copyrights, among which the patent for "a knowledge graph dynamic updating method based on learning behavior analysis" has been recognized as a core technological breakthrough.

In terms of constructing the evidence chain for achievement transformation, we guided enterprises to collect pilot application data and form application reports covering 20 districts and 100 schools. Through comparative analysis, we presented the improvement effect of the system on students at different levels, and compiled more than 30 supporting materials such as government procurement contracts and school usage feedback. At the same time, optimize the structure of award materials, highlight the logical loop of "technological innovation - intellectual property protection - large-scale application", hire experts in the field of educational technology to write recommendation opinions, and strengthen the industry influence proof of achievements.

After targeted optimization, the project successfully won the third prize of provincial scientific and technological progress in the second application. The person in charge of the enterprise stated, "The layout of intellectual property not only provides protection for technological innovation, but also becomes a key evidence of innovation when applying for awards. The transformation data sorted out by the professional team makes the value of the results more convincing." After winning the award, the system was included in the provincial-level education informationization recommended product catalog, with new sales exceeding 80 million yuan.