AI-based product originality automatic generation method and terminal
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FUJIAN TQ DIGITAL
- Filing Date
- 2024-11-12
- Publication Date
- 2026-05-22
AI Technical Summary
Traditional methods of generating product ideas manually are limited by the designer's experience, are time-consuming and labor-intensive, lack systematic and data support, make it difficult to quickly respond to individual user needs, have high innovation costs, and have a slow market response speed.
By predefining product goals, collecting and preprocessing data from multiple channels, training an AI creative generation model, and combining machine learning and AI model deployment, personalized and innovative creative designs are generated in real time.
It improved the innovation efficiency and market competitiveness of product design, reduced time and costs, increased the speed of response to market changes, and enhanced systematic and data support capabilities.
Smart Images
Figure CN122072980A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of AI technology, and in particular to an AI-based method and terminal for automatically generating product ideas. Background Technology
[0002] AI-powered product idea generation methods and technologies can automatically draw inspiration from massive amounts of design examples and user feedback, generating creative designs that align with market trends and meet user needs. By integrating technologies such as machine learning, natural language processing, and generative adversarial networks, this method efficiently analyzes user needs and creates personalized and innovative design solutions. This intelligent design approach not only improves the efficiency of product design innovation but also significantly enhances the product's competitiveness in the market, helping companies gain an advantage.
[0003] Disadvantages of Manual Creation: Using manually generated product ideas has significant drawbacks. First, the quality of the ideas is limited by the designer's experience and ability, easily encountering bottlenecks. Second, manually generated ideas are time-consuming and labor-intensive, resulting in slow market response, a lack of systematization and data support, a subjective element, and difficulty in optimization using big data and machine learning. Furthermore, manually generated ideas struggle to quickly analyze and respond to individual user needs, leading to insufficient design personalization. Finally, innovation costs are high, requiring substantial human resources, and multiple trial-and-error processes increase both time and financial costs. Summary of the Invention
[0004] The technical problem to be solved by the present invention is to provide an AI-based method and terminal for automatically generating product ideas, which can automatically generate personalized and innovative design schemes, thereby improving the innovation efficiency and market competitiveness of product design.
[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: An AI-based method for automatically generating product ideas includes the following steps: S1. Predefine product objectives and collect data from multiple channels based on the product objectives; S2. Perform text preprocessing and image preprocessing on the collected text data and image data respectively; S3. Train an AI creative generation model based on the preprocessed text data and image data; S4. Based on the real-time input requirements and parameters, the trained AI creative generation model automatically generates the final creative concept for the product in real time.
[0006] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: An AI-based product idea automatic generation terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the AI-based product idea automatic generation method described above.
[0007] The beneficial effects of this invention are as follows: It provides an AI-based method and terminal for automatically generating product ideas. By collecting data that meets the predefined goals of the product and performing data preprocessing to form a dataset, an AI idea generation model is trained. It combines advanced artificial intelligence technologies such as machine learning and AI model deployment, aiming to automatically draw inspiration from a large number of design cases and automatically generate personalized and innovative creative designs that meet market trends and user needs through the AI idea generation model. This improves the innovation efficiency and market competitiveness of product design, overcomes the bottlenecks of traditional manual idea generation, reduces time and costs, increases the speed of response to market changes, and enhances the systematization and data support capabilities of product design. Attached Figure Description
[0008] Figure 1 This is an overall flowchart of an AI-based automatic product idea generation method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an AI-based product idea automatic generation terminal according to an embodiment of the present invention.
[0009] Label Explanation: 1. An AI-based terminal for automatically generating product ideas; 2. Memory; 3. Processor. Detailed Implementation
[0010] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.
[0011] Please refer to Figure 1 A method for automatically generating product ideas based on AI, including the following steps: S1. Predefine product objectives and collect data from multiple channels based on the product objectives; S2. Perform text preprocessing and image preprocessing on the collected text data and image data respectively; S3. Train an AI creative generation model based on the preprocessed text data and image data; S4. Based on the real-time input requirements and parameters, the trained AI creative generation model automatically generates the final creative concept for the product in real time.
[0012] As can be seen from the above description, the beneficial effects of the present invention are as follows: It provides an AI-based method for automatically generating product ideas. By collecting data that conforms to the predefined goals of the product and performing data preprocessing to form a dataset, an AI idea generation model is trained. It combines advanced artificial intelligence technologies such as machine learning and AI model deployment, aiming to automatically draw inspiration from a large number of design cases and automatically generate personalized and innovative creative designs that conform to market trends and user needs through the AI idea generation model. This improves the innovation efficiency and market competitiveness of product design, overcomes the bottleneck of traditional manual idea generation, reduces time and cost, increases the speed of response to market changes, and enhances the systematization and data support capabilities of product design.
[0013] Furthermore, the product objectives include improving user experience and adding product functionality; The multiple channels mentioned include domestic websites, online forums, and WeChat official accounts.
[0014] As described above, setting specific product goals ensures that all participants have a clear understanding of the product, thereby ensuring that the creative direction of the subsequently generated product is clear.
[0015] Furthermore, the text preprocessing includes word segmentation, stop word removal, and stemming. Step S2 also includes: After stemming the text data, topic identification and clustering are performed.
[0016] As described above, text preprocessing is used for sentiment analysis to deeply identify user needs and pain points, ensuring that the creative ideas of the subsequently generated products meet the personalized needs of users.
[0017] Furthermore, the topic identification specifically involves extracting the main competing gameplay from the stemmed samples, and the clustering involves merging the extracted main competing gameplay.
[0018] As described above, the main competitive gameplay refers to the gameplay that attracts players or the best gameplay design. By extracting the main competitive gameplay from the keyword sample, that is, by extracting the key design elements that make players feel good through the game itself, since there may be multiple module designs in the same competitive product, and different competitive products may also reflect the same fun points, it is necessary to extract them.
[0019] Furthermore, the image preprocessing includes image enhancement and normalization, specifically: The image data is classified and its features are extracted using a convolutional neural network, and the image data is analyzed using style transfer technology to obtain product design style and visual trends.
[0020] As described above, image preprocessing provides visual inspiration for training the AI creative generation model, further ensuring that the creative ideas of products automatically generated based on the AI creative generation model meet the personalized needs of users.
[0021] Furthermore, the step between S3 and S4 also includes: S34. Deploy the trained AI creative generation model to the production environment and integrate it into the development process of existing products, adding a requirement input interface tool to the development process.
[0022] As described above, by deploying and developing interfaces for the trained AI creative generation model, product managers, designers, and users can easily use it to generate personalized product ideas.
[0023] Further, step S4 specifically includes: S41. Based on the user's real-time input of requirements and parameters in the requirement input interface tool, the AI creative generation model is used to automatically generate multiple initial creative ideas for the product in real time. S42. Use predefined criteria to screen multiple initial ideas to obtain the final ideas that meet the requirements of innovation and feasibility.
[0024] Furthermore, the predefined criteria include whether the generated ideas conform to the target product and whether they deviate from the target product's category or gameplay.
[0025] As described above, by filtering multiple initial ideas, the most personalized and innovative creative design that best meets user needs can be obtained, effectively improving the innovation efficiency and market competitiveness of product design.
[0026] Furthermore, after step S4, the method further includes: S5. Generate a visualization report based on the final creative idea, and monitor and update the text data and image data in real time based on user feedback and market dynamics. Perform hyperparameter tuning on the AI creative idea generation model periodically and iteratively optimize the AI creative idea generation model.
[0027] As described above, the text and image data used to train the AI creative generation model are updated through real-time user feedback and market research, thereby iteratively optimizing the AI creative generation model to ensure the innovation and feasibility of the ideas.
[0028] Please refer to Figure 2A product idea automatic generation terminal based on AI includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the product idea automatic generation method based on AI as described above.
[0029] As can be seen from the above description, the beneficial effects of the present invention are as follows: Based on the same technical concept, and in conjunction with the above-mentioned AI-based product idea automatic generation method, an AI-based product idea automatic generation terminal is provided. This terminal trains an AI idea generation model by collecting data that conforms to predefined product goals and performing data preprocessing to form a dataset. It combines advanced artificial intelligence technologies such as machine learning and AI model deployment, aiming to automatically draw inspiration from a large number of design cases. Through the AI idea generation model, it automatically generates personalized and innovative creative designs that conform to market trends and user needs, thereby improving the innovation efficiency and market competitiveness of product design. This overcomes the bottlenecks of traditional manual idea generation, reduces time and costs, increases the speed of response to market changes, and enhances the systematization and data support capabilities of product design.
[0030] This invention provides an AI-based method and terminal for automatically generating product ideas, primarily applied in intelligent design scenarios for creative design schemes in product design. The following detailed description is provided in conjunction with specific embodiments: Please refer to Figure 1 Embodiment 1 of the present invention is as follows: An AI-based method for automatically generating product ideas, such as Figure 1 As shown, the steps include: S1. Predefine product objectives and collect data from multiple channels based on those objectives.
[0031] Product objectives include improving user experience and adding product features. These can be clarified in advance through communication with relevant departments to ensure that all participants have a clear understanding of the product, thereby ensuring that the creative direction of the subsequent product is clear. Multiple channels can include various acquisition methods such as domestic websites, Baidu Tieba, and WeChat official accounts, which are not limited here.
[0032] S2. Perform text preprocessing and image preprocessing on the collected text data and image data respectively.
[0033] In this embodiment, text preprocessing includes word segmentation, stop word removal, and stemming. Therefore, step S2 further includes: After stemming the text data, topic identification and clustering are performed.
[0034] Specifically, topic recognition involves extracting the main competing gameplay from the stemmed samples, and clustering involves merging the extracted main competing gameplay.
[0035] The main competitive gameplay refers to the gameplay that primarily attracts players or the best gameplay design. By extracting the main competitive gameplay from the keyword sample, we can identify the key design elements that make players feel satisfied through the game itself. Since there may be multiple modules in the same competitive product, different competitive products may also reflect the same satisfying point. For example, if multiple modules in game A reflect the sensory stimulation of blood / bursts, and this satisfying point also exists in 4 other competitive games, then it needs to be extracted. This indicates that this satisfying point exists in most popular games and needs to be taken into account. This is called the common satisfying point.
[0036] Meanwhile, in this embodiment, image preprocessing includes image enhancement and normalization, specifically: Convolutional neural networks are used to classify and extract features from image data, and style transfer techniques are used to analyze the image data to obtain product design styles and visual trends.
[0037] The process involves identifying and extracting elements from competitor images, such as those featuring mecha designs. This allows for the analysis of potential differences and similarities in mecha design across different competitors. Image preprocessing provides visual inspiration for training an AI-powered creative generation model, ensuring that the resulting product designs automatically generated by this model meet personalized user needs. Style transfer technology utilizes images of competitor props to generate more images in different styles, aiding in subsequent creative output. For example, if certain mecha props are deemed appealing, AI can be used to generate designs in different styles for new products.
[0038] S3. Train an AI creative generation model based on preprocessed text and image data.
[0039] In this embodiment, the model training process requires continuous verification of the model's inference quality. If the inference quality is poor, it can be continuously trained by adjusting the hyperparameters. Therefore, in this step, the model's hyperparameters can be appropriately tuned. Hyperparameters are the parameters used for model training. The model can be trained using open-source pre-trained models as a foundation to create our own AI creative generation model. The hyperparameters are adjusted based on the test results after each round of training. For example, after training the model for one round and testing it, if it is found to be unsatisfactory, the parameters need to be adjusted and the model retrained.
[0040] S4. Based on real-time input requirements and parameters, the trained AI creative generation model automatically generates the final product concept in real time.
[0041] In this embodiment, an AI creative generation model is trained by collecting data that meets the product's predefined goals and performing data preprocessing to form a dataset. This model combines advanced artificial intelligence technologies such as machine learning and AI model deployment, aiming to automatically draw inspiration from a large number of design cases and automatically generate personalized and innovative creative designs that meet market trends and user needs. This improves the innovation efficiency and market competitiveness of product design, overcomes the bottlenecks of traditional manual creative generation, reduces time and costs, increases the speed of response to market changes, and enhances the systematization and data support capabilities of product design.
[0042] Embodiment 2 of the present invention is as follows: An AI-based method for automatically generating product ideas, based on the above embodiment one, further includes the following step between S3 and S4: S34. Deploy the trained AI creative generation model to the production environment and integrate it into the development process of existing products, adding requirement input interface tools to the development process.
[0043] In this embodiment, by deploying and developing interfaces for the trained AI creative generation model, product managers, designers, and users can easily use it to generate personalized product ideas.
[0044] In this embodiment, step S4 specifically involves: S41. Based on the requirements and parameters input by users in real time through the requirements input interface tool, an AI-powered creative generation model automatically generates multiple initial product ideas in real time. This allows team members to input requirements and parameters, generate ideas in real time, and perform initial screening. An interactive platform is provided to support the team in modifying and optimizing the generated ideas. Alternatively, dedicated software can be used for real-time input of requirements and parameters, allowing the model to be built into a platform or packaged into a client product for others to use.
[0045] S42. Multiple initial ideas are screened using predefined criteria to obtain final ideas that meet the requirements of innovation and feasibility. These predefined criteria include whether the generated ideas align with the target product and whether they deviate from the target product's category or gameplay. For example, if the target product is a top-down shooter, the generated ideas might not be from a first-person perspective. Subsequent user surveys and A / B testing can be used to evaluate the market potential and user acceptance of the ideas. Based on feedback, the generation model is adjusted and optimized, iterating to generate new ideas and ensuring the final ideas succeed in the market.
[0046] This means that by filtering multiple initial ideas, the most personalized and innovative creative design that best meets user needs can be obtained, effectively improving the innovation efficiency and market competitiveness of product design.
[0047] In addition, in this embodiment, step S4 is followed by: S5. Generate a visualization report based on the final creative idea, use data visualization tools to display the creative idea generation process and results, evaluate the creative idea and provide optimization suggestions to help decision-makers choose the best solution; and monitor and update text and image data in real time based on user feedback and market dynamics, regularly perform hyperparameter tuning on the AI creative idea generation model, iteratively optimize the AI creative idea generation model, improve creative idea generation capabilities, adapt to market changes, and continuously output competitive product ideas.
[0048] This involves updating the text and image data used to train the AI creative generation model through real-time user feedback and market research, thereby iteratively optimizing the AI creative generation model to ensure the innovativeness and feasibility of the ideas.
[0049] AI technology can quickly analyze large amounts of data, extract valuable information, and combine it with market trends and user needs to generate creative ideas that meet market demands. This helps companies respond quickly to market changes, continuously launch competitive products, and enhance their market competitiveness.
[0050] Please refer to Figure 2 Embodiment 3 of the present invention is as follows: An AI-based product idea automatic generation terminal 1 includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it completes the steps in the AI-based product idea automatic generation method described in Embodiment 1 or Embodiment 2.
[0051] In summary, the present invention provides an AI-based method and terminal for automatically generating product ideas. This method trains an AI idea generation model by collecting and preprocessing data that conforms to predefined product goals into a dataset. Combining advanced artificial intelligence technologies such as machine learning and AI model deployment, it aims to automatically draw inspiration from numerous design cases and automatically generate personalized and innovative creative designs that meet market trends and user needs. This improves the innovation efficiency and market competitiveness of product design, overcomes the bottlenecks of traditional manual idea generation, reduces time and costs, increases the speed of response to market changes, and enhances the systematization and data support capabilities of product design.
[0052] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.
Claims
1. A method for automatically generating product ideas based on AI, characterized in that, Including the following steps: S1. Predefine product objectives and collect data from multiple channels based on the product objectives; S2. Perform text preprocessing and image preprocessing on the collected text data and image data respectively; S3. Train an AI creative generation model based on the preprocessed text data and image data; S4. Based on the real-time input requirements and parameters, the trained AI creative generation model automatically generates the final creative concept for the product in real time.
2. The method for automatically generating product ideas based on AI according to claim 1, characterized in that, The product objectives include improving user experience and adding product features; The multiple channels mentioned include domestic websites, online forums, and WeChat official accounts.
3. The method for automatically generating product ideas based on AI according to claim 1, characterized in that, The text preprocessing includes word segmentation, stop word removal, and stemming. Step S2 also includes: After stemming the text data, topic identification and clustering are performed.
4. The method for automatically generating product ideas based on AI according to claim 3, characterized in that, The topic identification specifically involves extracting the main competing gameplay from the word stem samples, and the clustering involves merging the extracted main competing gameplay.
5. The method for automatically generating product ideas based on AI according to claim 1, characterized in that, The image preprocessing includes image enhancement and normalization, specifically: The image data is classified and its features are extracted using a convolutional neural network, and the image data is analyzed using style transfer technology to obtain product design style and visual trends.
6. The method for automatically generating product ideas based on AI according to claim 1, characterized in that, Between steps S3 and S4, the following is also included: S34. Deploy the trained AI creative generation model to the production environment and integrate it into the development process of existing products, adding a requirement input interface tool to the development process.
7. The method for automatically generating product ideas based on AI according to claim 6, characterized in that, Step S4 specifically involves: S41. Based on the user's real-time input of requirements and parameters in the requirement input interface tool, the AI creative generation model is used to automatically generate multiple initial creative ideas for the product in real time. S42. Use predefined criteria to screen multiple initial ideas to obtain the final ideas that meet the requirements of innovation and feasibility.
8. The method for automatically generating product ideas based on AI according to claim 7, characterized in that, The predefined criteria include whether the generated ideas match the target product and whether they deviate from the target product's category or gameplay.
9. The method for automatically generating product ideas based on AI according to claim 7, characterized in that, The process following step S4 also includes: S5. Generate a visualization report based on the final creative idea, and monitor and update the text data and image data in real time based on user feedback and market dynamics. Perform hyperparameter tuning on the AI creative idea generation model periodically and iteratively optimize the AI creative idea generation model.
10. A product idea automatic generation terminal based on AI, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of an AI-based product idea automatic generation method as described in any one of claims 1 to 9.