A method and system for intelligently generating product solutions

Abstract

This invention discloses a method and system for intelligently generating product solutions, comprising the following steps: key element information acquisition; product solution analysis and design and dynamic prompt word construction; candidate solution generation and explanatory recommendation; interactive confirmation and feedback capture; the system includes an interactive display and feedback module, a product solution generation module, a product solution reference data acquisition module, a data storage and model fine-tuning module, and a large model service; this solution features improved solution design accuracy, enhanced solution generation quality and consistency, improved decision-making efficiency and scientific rigor, and enhanced continuous evolution capabilities.

Description

Technical Field

[0001] This invention relates to the field of intelligent business plan generation, and in particular to a method and system for intelligently generating product solutions. Background Technology

[0002] In startup financing and corporate strategic planning, the business plan is the core vehicle for showcasing project value and attracting investors. The "Product Solution" section, in particular, aims to clarify what products the company offers, whose pain points it solves, and why it solves those pain points—this is the essence of the business plan. However, many entrepreneurs face the following technical bottlenecks when writing this section: (1) Lack of structured methodology in product solution design: Most existing business plan writing tools are static templates or general text editors, which cannot conduct in-depth analysis on specific product types and segmented demographic profiles, resulting in entrepreneurs "not knowing what specific problems their products are supposed to solve" and falling into the dilemma of "talking to themselves" in solution design.

[0003] (2) Insufficient intelligence in the decomposition and analysis of core information: Traditional methods rely on human experience to decompose market demand, user pain points and product characteristics. They lack a systematic analysis framework and it is difficult to identify the relationship between key elements, which leads to the design of solutions deviating from the real market demand.

[0004] (3) Disconnect between solution generation and decision support: Existing AI-assisted tools can only generate a single solution or simply list multiple options, and cannot provide multi-dimensional comparisons or explanatory reasons for recommendations, making it difficult for entrepreneurs to make scientific decisions among multiple candidate solutions.

[0005] (4) Lack of a data closed-loop mechanism for continuous optimization: Most existing systems are in a "one-time" generation mode, which cannot capture users' selection preferences and feedback data, resulting in the model output quality not improving with the frequency of use, making it difficult to form a continuous accumulation of domain knowledge.

[0006] Therefore, there is an urgent need for an intelligent method and system that can guide users through the thought process from "fuzzy cognition" to "precise decision-making," solving the technical problems of insufficient information processing, opaque generation logic, low user adoption rate, and difficulty in forming a high-quality data closed loop in existing technologies. Summary of the Invention

[0007] Therefore, there is a need for a method and system for generating dynamic prompt product solutions based on multi-dimensional feature perception. This addresses the technical issues of users being unable to structurally organize product solutions and the lack of interpretability and specificity in AI-generated results. This method and system can assist users in accurately locating and confirming the core solutions of their products, while simultaneously constructing a self-evolving data loop.

[0008] To achieve the above objectives, the inventors provide a method for intelligently generating product solutions, comprising the following steps: S1, Obtain structured information on key product elements; S2, based on the key element information of structured products, constructs dynamic reasoning prompts, uses prompt construction modules to dynamically assemble data, and outputs standardized and structured prompts; S3. Input the prompt words into the artificial intelligence model to generate a list of several candidate product solutions, and generate explanatory recommendation reasons for at least one candidate solution in the list; S4: Display a list of several candidate product solutions and corresponding explanatory reasons for recommendation, and receive confirmation from the user regarding their selection of one of the candidate solutions. As a preferred embodiment of the invention, S5: Data feedback and model fine-tuning; based on the user-confirmed data and key element information, collect positive and negative sample data to perform supervised fine-tuning of the underlying model.

[0009] In a preferred embodiment of the present invention, in step S1, the key product information includes product type, segmented customer profile, and pain point issues.

[0010] In a preferred embodiment of the present invention, step S2 includes using a product solution analysis and design tool to analyze the acquired key element information, outputting several potential product solution reference data, and using a prompt word constructor to output standardized and structured prompt words.

[0011] As a preferred embodiment of the present invention, the candidate product solution includes a product solution name, a detailed description of the solution, a recommended solution and a reason for the recommendation.

[0012] In a preferred embodiment of the present invention, step S4 includes rendering a clear list of several solutions and corresponding solution descriptions on the front end, highlighting preferred recommendations and reasons, guiding the user to select, and confirming the required product solution.

[0013] To achieve the above objectives, the inventors also provide a system for intelligently generating product solutions, comprising: The interactive display and feedback module is used to obtain key product information input by the user, display a list of candidate product solutions and explanatory reasons for recommendation, and allow the user to perform operations. The product solution generation module is used to analyze the key product element information obtained and generate a list of candidate product solutions and explanatory reasons for recommendation. The product solution reference data acquisition module is used to acquire candidate product solutions and build a product solution vector library and knowledge graph library, and retrieve relevant product solution data based on the search data. The data storage and model fine-tuning module is used to store key information of user input, a list of generated candidate product solutions and explanatory reasons for recommendations, as well as the solution selected by the user, to continuously optimize the model generation effect; Large model service, used to generate product solution results.

[0014] As a preferred embodiment of the present invention, the key element information includes product type and segmented user profiles, pain points, and related issues.

[0015] As a preferred embodiment of the present invention, the product solution reference data includes existing product solution case data.

[0016] As a preferred embodiment of the present invention, the result of the product solution includes the product solution name, detailed description of the solution, recommended solution and explanation of reasons.

[0017] Unlike existing technologies, the above technical solution achieves the following beneficial effects: (1) This method and system effectively improves accuracy: Through structured elements and a dedicated solution analysis and design tool, entrepreneurs’ vague product ideas can be transformed into solution designs that accurately match the pain points of the target audience.

[0018] (2) This method and system effectively enhance the quality and consistency of the generated content: the dynamic prompt word builder assembles a specific prompt for the field of enterprise strategic planning and business plan production based on the input element information and multiple scheme design contents, avoiding the problem of large quality fluctuations in manual writing and ensuring the professionalism and standardization of the output scheme.

[0019] (3) This method and system effectively improve decision-making efficiency and scientificity: multiple candidate solutions combined with explanatory recommendation reasons provide entrepreneurs with quantifiable and comparable decision-making basis, changing the decision-making difficulties caused by traditional single output or unordered lists, shortening the solution confirmation cycle and reducing trial and error costs.

[0020] (4) This method and system effectively improve the ability to continuously evolve: the data feedback and supervision fine-tuning mechanism form a closed-loop optimization, breaking through the limitation of existing tools generating data at one time, enabling the model to accumulate domain knowledge and user preferences with the frequency of use, and achieving self-improvement in recommendation accuracy and solution quality. Attached Figure Description

[0021] Figure 1 A flowchart illustrating the method described in the specific implementation; Figure 2 The system framework diagram described in the specific implementation method; Figure 3 The flowchart of the method and system described in the specific implementation is shown below. Detailed Implementation

[0022] To explain in detail the technical content, structural features, objectives, and effects of the technical solution, the following description is provided in conjunction with specific embodiments and accompanying drawings.

[0023] like Figure 1 As shown, this embodiment provides a method for intelligently generating product solutions, including the following steps: S1, Obtain structured information on key product elements; S2, based on the key element information of structured products, constructs dynamic reasoning prompts, uses prompt construction modules to dynamically assemble data, and outputs standardized and structured prompts; S3. Input the prompt words into the artificial intelligence model to generate a list of several candidate product solutions, and generate explanatory recommendation reasons for at least one candidate solution in the list; S4 displays a list of several candidate product solutions along with corresponding explanatory reasons for recommendation, and receives confirmation from the user regarding their selection of one of the candidate solutions.

[0024] In this embodiment, in step S1, the key information elements include product type, segmented user profile, and pain point issues.

[0025] Step S2 includes using a product solution analysis and design tool to analyze the acquired key product element information, outputting several potential product solution reference data, and using a prompt word constructor to output standardized and structured prompt words. Candidate product solutions include product solution names, detailed solution descriptions, recommended solutions, and justifications. Step S4 includes rendering a clear list of several solutions and corresponding solution descriptions on the front end, highlighting preferred recommendations and their reasons, guiding the user to select, and confirming the desired product solution. In some embodiments, step S5 is also included: data feedback and model fine-tuning. Based on the confirmed data and key element information, positive and negative sample data are collected, and the underlying large model is periodically subjected to supervised fine-tuning to continuously learn user decision preferences and domain-specific business logic, resulting in more accurate subsequent options and higher recommendation accuracy. like Figure 2 As shown, this embodiment also provides a system for intelligently generating product solutions, including: The interactive display and feedback module is used to obtain key product information input by the user, display a list of candidate product solutions and explanatory reasons for recommendation, and allow the user to perform actions. Key product information includes product type and segmented user profiles, pain points, such as: solving customer pain points in specific scenarios.

[0026] The product solution generation module is used to analyze the key product information obtained and generate a list of candidate product solutions and explanatory reasons for recommendation.

[0027] The product solution reference data acquisition module is used to acquire candidate product solutions and construct a product solution vector library and a knowledge graph library, and retrieve relevant product solution data based on the search data; the product solution reference data includes existing product solution case data.

[0028] The data storage and model fine-tuning module is used to store key information of user input, a list of generated candidate product solutions and explanatory reasons for recommendations, as well as the solution selected by the user, to continuously optimize the model generation effect; The large model service generates product solution results by calling the large model service through an interface. The results include the product solution name, detailed solution description, recommended solution and reasoning.

[0029] To illustrate the specific implementation process of the methods and systems in the above embodiments in detail, the following examples are provided: Example: Figure 3 As shown, the premise is that the product solution reference data acquisition module has prepared the data and built multiple retrieval services for retrieving product solutions. First, the original data of product solution cases is prepared. Retrieval service 1: The product solution vector library is built using vector database tools such as Milvus. The original data is organized into a certain format as "[{"content":"""Pet hospital membership plan: For pet owners visiting the hospital for the first time, a health record + vaccination reminder service is launched. By establishing electronic health records, data similar to "" and "pet_category":"dog", "pain_point_tag":"difficulty in retaining members", and "solution_summary":"health records + grading system + community operation, member retention rate increased by 35%", is imported into the Milvus database and indexed for quick retrieval of product solutions; Retrieval Service 2: A knowledge graph retrieval service is built using the Neo4j knowledge graph tool. First, it retrieves data including node types: segmented audiences, pain point tags, service solutions; relationship types: encountered pain points, used solutions, similar solutions, etc. Then, it prepares data according to a specified format, such as "pain point node data: pain point ID, pain point description, pain point type; solution node: solution ID, solution name, detailed content, success rate; solution-pain point relationship node: solution ID, pain point ID, solution effect rating..." and imports it into the knowledge graph library. Relevant data can then be retrieved through industry and pain point keywords; Retrieval Service 3: Search results are obtained by calling the search engine API using keywords.

[0030] The interactive display and feedback module receives key information from the user: product type, target audience segment, and pain points. For example, the user might fill in the product type as "pet membership management platform"; the target audience segment as "individual user - female - 25-34 years old - middle income - pet shop owner - urban single person"; and the pain points as "pet shop owners struggle to convert casual customers into stable members due to a lack of offline scenarios and online engagement"; and "urban single women with pets want to expand their community, but encounters are rare, platforms are fragmented, and trust is difficult to build." The interactive display and feedback module then sends the data to the product solution generation module.

[0031] After receiving the data, the product solution generation module uses NLP technology to extract key information from the text, such as "Product type: pet, membership management platform; Pain points: difficulty in retaining members and building trust; Target audience: women, 25-34 years old, middle income, pet shop owners, urban singles"; then it calls the product solution reference data acquisition module, using the key information "pet, membership management platform, difficulty in retaining members, difficulty in building trust" to retrieve search results: 1. Using the Milvus vector database, retrieve the search results data, such as "[{"content":"""Pet hospital membership program: For pet owners visiting the hospital for the first time, a health record + vaccination reminder service is offered. By establishing electronic health records, "pet_category":"dog","pain_point_tag":"difficulty in retaining members","solution_summary":"health records + tiered system + community operation, improve member retention rate by 35%"}"; 2. Then, using different keywords, call the Neo4j knowledge graph tool to obtain multiple result data from the knowledge graph: such as based on "pain point = difficulty in retaining members" and "segmented audience = women, 25-34 years old, middle income, pet shop owners, urban single people", multiple result data are obtained: "[{"solution name":"health records + user system","details":"Establish electronic health records, automatically push vaccine reminders, set member levels to enjoy priority appointments. Introduce a referral mechanism and hold offline pet health lectures every month. 3. If the data obtained from the Milvus vector database and Neo4j knowledge graph tool is insufficient, the search engine API will be used to combine pain point keywords to form the search keywords "pet store membership difficulty, pet, membership management platform product solution" search results data, such as "{"title": "pet store membership operation management solution","link":"https: / / www.youzan.com / chanpin","snippet":"Product introduction\nThe pet store membership operation management solution is a product for pet stores and brands with offline stores and online malls"]"; 4. Based on the above obtained results data, the search results data will be summarized and fed back to the product solution generation module.

[0032] The product solution generation module utilizes a prompt word constructor to dynamically load data and output a standardized, structured prompt word, such as "You are a product solution design expert, skilled at extracting core value from product descriptions provided by entrepreneurs, referencing retrieved product solution data, and accurately positioning the product to output multiple product solutions that address user pain points, etc." Product-related introduction: Product: Pet Membership Management Platform; Target audience: Individual users - women - 25-34 years old - middle income - pet shop owners - urban singles; Pain points: Pet shop owners struggle to convert casual customers into stable members due to a lack of offline scenarios and online engagement; Urban single women with pets want to expand their community, but encounters are rare, platforms are fragmented, and trust is difficult to establish. Then, the interactive display and feedback module sends the data... Submitted to the product solution generation module;\n“[{"content":"""Pet Hospital Membership Program: For pet owners visiting the hospital for the first time, a health record + vaccination reminder service is launched. By establishing electronic health records"","pet_category":"dog","pain_point_tag":"difficulty in retaining members","solution_summary":"health records + tiered system + community operation, member retention rate is increased by 35%"},{"Solution Name":"Health Records + User System","Detailed Content":"Establish electronic health records, automatically push vaccination reminders, set membership levels to enjoy priority appointments. Introduce a referral mechanism, and hold offline pet health lectures every month. ","Success Rate": 0.92}]\n, Requirements: Solve user pain points, provide 10 solutions, and provide the basis and explanation of the solutions based on the retrieved data; in addition, provide recommended solutions and reasons for recommendation, and output the results in a standardized JSON format.

[0033] The product solution generation module calls the large model service to generate multiple product solution results based on pre-constructed prompts. These results include the product solution name, detailed solution description, optimal recommendation, and rationale. For example, "{"Option List":["Lightweight Membership Mini-Program Embedded in Pet Store's Membership System, One-Click Conversion of Casual Customers into Reachable Members","Pet Store Owner's Exclusive Community Operation Toolkit", etc.],"Option Interpretation":{"Lightweight Membership Mini-Program Embedded in Pet Store's Membership System":"- **Feature Interpretation**: When a customer makes their first purchase through the mini-program by scanning a QR code to pay, an electronic member profile is automatically generated, and the user is guided to join the member service group, etc. **Business Analysis:** This is the lightest 'hook'—it doesn't increase the operational burden on store owners, yet it allows customers to become members every time they scan a code. [Recommended Option:] "A lightweight membership mini-program embedded in a pet store's membership system, instantly converting casual customers into reachable members." [Recommendation Reason:] "It directly addresses the store owner's biggest pain point—'no offline scenarios, no online leverage.' It doesn't rely on users actively downloading an app or joining a group; the mini-program is 'embedded and ready to use,' with an extremely short conversion path—casual customers can become members simply by scanning a code."

[0034] The product solution generation module sends the result data to the interactive display and feedback module. The interactive display and feedback module shows the user a list of product solutions and an explanation of each solution to help the user understand and choose. In addition, it provides recommended options by highlighting them and providing reasons for the recommendations to guide the user to select and confirm the product solution. For example, if the user finally selects "Lightweight membership mini-program embedded in the pet store POS system, one-click conversion of casual customers into reachable members".

[0035] In different embodiments, the interactive display and feedback module sends a list of product solutions and the interpretation of each solution, as well as the result data of user selection, to the product solution generation module. The product solution generation module sends the data to the data storage and model fine-tuning module. The data storage and model fine-tuning module takes the data selected by the user as positive samples and the unselected data as negative samples. Then, according to a unified format, it converts the result data into corpus data that can be used for training large models, and uses it to train large models periodically.

[0036] It should be noted that although the above embodiments have been described herein, this does not limit the scope of patent protection for this invention. Therefore, any changes and modifications made to the embodiments described herein based on the innovative concept of this invention, or equivalent structural or procedural transformations made using the description and drawings of this invention, directly or indirectly applying the above technical solutions to other related technical fields, are all included within the scope of patent protection for this invention.

Claims

1. A method of intelligently generating a product solution, the method comprising: Includes the following steps: S1, Obtain structured information on key product elements; S2, based on the key element information of structured products, constructs dynamic reasoning prompts, uses prompt construction modules to dynamically assemble data, and outputs standardized and structured prompts; S3. Input the prompt words into the artificial intelligence model to generate a list of several candidate product solutions, and generate explanatory recommendation reasons for at least one candidate solution in the list; S4 displays a list of several candidate product solutions along with corresponding explanatory reasons for recommendation, and receives confirmation from the user regarding their selection of one of the candidate solutions.

2. The method of intelligently generating a product solution of claim 1, wherein, It also includes step S5, data feedback and model fine-tuning, which involves collecting positive and negative sample data based on user-confirmed data and key element information, and performing supervised fine-tuning of the underlying model.

3. The method of intelligently generating a product solution of claim 1 or 2, wherein, In step S1, the key product information includes product type, segmented user profile, and pain point issues.

4. The method of intelligently generating a product solution of claim 1 or 2, wherein: Step S2 includes using a product solution analysis and design tool to analyze the acquired key product element information, outputting several potential product solution reference data, and using a prompt word constructor to output standardized and structured prompt words.

5. The method for intelligently generating product solutions according to claim 1 or 2, characterized in that: The candidate product solutions include the product solution name, detailed solution description, recommended solution and reason explanation.

6. The method for intelligently generating product solutions according to claim 1 or 2, characterized in that: Step S4 includes rendering a clear list of several solutions and corresponding solution descriptions on the front end, highlighting preferred recommendations and reasons, guiding users to select and confirm the required product solution.

7. A system for intelligently generating product solutions, characterized in that, include: The interactive display and feedback module is used to obtain key product information input by the user, display a list of candidate product solutions and explanatory reasons for recommendation, and allow the user to perform operations. The product solution generation module is used to analyze the key product element information obtained and generate a list of candidate product solutions and explanatory reasons for recommendation. The product solution reference data acquisition module is used to acquire candidate product solutions and build a product solution vector library and knowledge graph library, and retrieve relevant product solution data based on the search data. The data storage and model fine-tuning module is used to store key information of user input, a list of generated candidate product solutions and explanatory reasons for recommendations, as well as the solution selected by the user, to continuously optimize the model generation effect; Large model service, used to generate product solution results.

8. The system for intelligently generating product solutions according to claim 7, characterized in that: The key information elements include product type and segmented user profiles, and pain points.

9. The system for intelligently generating product solutions according to claim 7, characterized in that: The product solution reference data includes existing product solution case data.

10. The system for intelligently generating product solutions according to claim 7, characterized in that: The product solution results include the product solution name, detailed solution description, recommended solution and reasoning.

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