A real-time recommendation method based on meta-universe user behavior capture
By capturing user behavior and generating real-time recommendations in the metaverse, the problem of inconvenient shopping in physical stores is solved, and users can conveniently select products in the metaverse.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING ERFA AUTOMATION EQUIP CO LTD
- Filing Date
- 2026-05-01
- Publication Date
- 2026-07-21
AI Technical Summary
In existing technologies, people need to expend physical energy to select goods when purchasing them in physical stores, which makes shopping inconvenient and prevents convenient product recommendations.
Order information is generated through the user client, and the server's behavior capture module receives and stores user behavior information. The construction module builds the metaverse module, and the generation module displays the order information and images of user operations. Information is pushed according to user preferences to achieve real-time recommendations.
Users can select their favorite products through the client in the metaverse, avoiding blind purchases and improving the shopping experience.
Smart Images
Figure CN122432418A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of behavior capture technology, specifically a real-time recommendation method based on metaverse user behavior capture. Background Technology
[0002] The metaverse is a virtual world created and linked through technological means, mapping and interacting with the real world. It's a digital living space with a new social system. Essentially, the metaverse is the virtualization and digitization of the real world, requiring significant modifications to content production, economic systems, user experience, and physical world content. However, the development of the metaverse is gradual, supported by shared infrastructure, standards, and protocols. It is ultimately formed through the continuous integration and evolution of numerous tools and platforms. It provides immersive experiences based on extended reality technology, generates mirror images of the real world based on digital twin technology, and builds an economic system based on blockchain technology. It closely integrates the virtual and real worlds in terms of economic, social, and identity systems, allowing each user to produce content and edit the world. Currently, people typically purchase goods through physical stores, requiring physical effort to select items, which is inconvenient. Therefore, a real-time recommendation method based on metaverse user behavior capture is needed to address the proposed technical issues. Summary of the Invention
[0003] The purpose of this invention is to provide a real-time recommendation method based on metaverse user behavior capture. Users select goods and generate order information through a user client. The server's behavior capture module receives the behavior information and then uses a construction module to build a metaverse module based on the user's behavior information, realizing the user's actions. A generation module then generates behavior data from the constructed modules, which is displayed on the user client as order generation information and images. Furthermore, information is pushed according to the user's preferences, allowing the user to see content they like through the user client, thus solving the problems mentioned in the background art.
[0004] To achieve the above objectives, the present invention provides the following technical solution: a real-time recommendation method based on metaverse user behavior capture, comprising a user client, a server, and a merchant client; the user client includes a user output module, a user receiving module, and a generation module; the server includes a behavior capture module, a storage module, and a transmission module; and the merchant client includes an analysis module, a construction module, a merchant receiving module, and a merchant output module.
[0005] A real-time recommendation method based on metaverse user behavior capture includes the following steps: Step 1, user behavior capture; Step 2, behavior information transmission; Step 3, information analysis; and Step 4, user information update. In step one above, the user uses the user client to operate the order information. Then, the user's operation behavior is transmitted through the user output module. The server's behavior capture module receives the transmitted behavior information, captures the behavior information, and stores it. In step two above, the server transmits the stored user behavior information through the transmission module, and the merchant client receives and stores the transmitted behavior information through the receiving module. In step three above, the received user behavior information is then analyzed by the analysis module, and then the user behavior information is used to build a metaverse module to realize user behavior operations. In step four above, the merchant client transmits the constructed behavior module through the merchant output module. The server's transmission module then receives the constructed behavior information and transmits it to the user client, where it is received by the user receiving module. Finally, the generation module generates behavior data from the constructed module, which is then displayed by the user client.
[0006] Preferably, the user behavior operations of the user client consist of the user using the client to operate on purchase order information, images, etc.
[0007] Preferably, the behavioral data is transmitted to the server for storage in the form of electrical signals to avoid the loss of behavioral information.
[0008] Preferably, the merchant client's construction module builds a module for user behavior information orders, generates order data instructions which are pushed from the server to the user's client to realize the user's behavior operations.
[0009] Preferably, the generation module captures the user's daily preferences based on the user's behavior and pushes information according to the user's preferences, so that the user can see the information content they like through the user's client.
[0010] Compared with the prior art, the beneficial effects of the present invention are: This invention allows users to select goods and generate order information via a client application. The server's behavior capture module then receives this behavior information and uses a construction module to build a metaverse-like module based on the user's behavior information, enabling user actions. A generation module then generates behavior data from the constructed modules, which is displayed on the user's client application along with order information and images. Furthermore, information is pushed to users based on their preferences, allowing them to select goods without leaving home, making it more convenient for them to choose their favorite items and avoiding blind purchases. Attached Figure Description
[0011] Figure 1This is a schematic diagram of the behavior capture process of the present invention. Detailed Implementation
[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0013] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," "front end," "rear end," "both ends," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0014] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.
[0015] Please see Figure 1 One embodiment provided by the present invention: Example
[0016] A real-time recommendation method based on metaverse user behavior capture includes a user client, a server, and a merchant client. The user client includes a user output module, a user acceptance module, and a generation module. The server includes a behavior capture module, a storage module, and a transmission module. The merchant client includes an analysis module, a construction module, a merchant acceptance module, and a merchant output module.
[0017] A real-time recommendation method based on metaverse user behavior capture includes the following steps: Step 1, user behavior capture; Step 2, behavior information transmission; Step 3, information analysis; and Step 4, user information update. In step one above, the user uses the user client to operate the order information. Then, the user's operation behavior is transmitted through the user output module. The server's behavior capture module receives the transmitted behavior information, captures the behavior information, and stores it. In step two above, the server transmits the stored user behavior information through the transmission module, and the merchant client receives and stores the transmitted behavior information through the receiving module. In step three above, the received user behavior information is then analyzed by the analysis module, and then the user behavior information is used to build a metaverse module to realize user behavior operations. In step four above, the merchant client transmits the constructed behavior module through the merchant output module. The server's transmission module then receives the constructed behavior information and transmits it to the user client, where it is received by the user receiving module. Finally, the generation module generates behavior data from the constructed module, which is then displayed by the user client.
[0018] Furthermore, user behavior operations on the user client consist of the user using the client to operate on purchase order information, images, etc.
[0019] Furthermore, behavioral data is transmitted to a server for storage via electrical signals to prevent the loss of behavioral information.
[0020] Furthermore, the merchant client's building module constructs modules for user behavior information and orders, generates order data instructions that are pushed from the server to the user's client to enable user actions.
[0021] Furthermore, the generation module captures users' daily preferences based on their behavior and pushes information accordingly, allowing users to see the information they like through their client. Example
[0022] The user selects products through the client and generates order information. Then, the server's behavior capture module receives the behavior information and uses the construction module to build a metaverse module based on the user's behavior information to realize the user's actions. The generation module then generates behavior data from the constructed modules, which is displayed on the user's client along with order generation information and images. Furthermore, information is pushed to users based on their preferences, allowing users to select products without leaving home. This makes it more convenient for users to choose their favorite products, avoids blind purchases, and increases users' purchasing interest.
[0023] Any aspects of this invention not described in detail are well-known to those skilled in the art.
[0024] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications and equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A real-time recommendation method based on metaverse user behavior capture, comprising a user client, a server, and a merchant client; the user client includes a user output module, a user receiving module, and a generation module; the server includes a behavior capture module, a storage module, and a transmission module; the merchant client includes an analysis module, a construction module, a merchant receiving module, and a merchant output module.
2. A real-time recommendation method based on metaverse user behavior capture, comprising: step one, user behavior capture; step two, behavior information transmission; step three, information analysis; and step four, user information update; characterized in that: In step one above, the user uses the user client to operate the order information. Then, the user's operation behavior is transmitted through the user output module. The server's behavior capture module receives the transmitted behavior information, captures the behavior information, and stores it. In step two above, the server transmits the stored user behavior information through the transmission module, and the merchant client receives and stores the transmitted behavior information through the receiving module. In step three above, the received user behavior information is then analyzed by the analysis module, and then the user behavior information is used to build a metaverse module to realize user behavior operations. In step four above, the merchant client transmits the constructed behavior module through the merchant output module. The server's transmission module then receives the constructed behavior information and transmits it to the user client, where it is received by the user receiving module. Finally, the generation module generates behavior data from the constructed module, which is then displayed by the user client.
3. The real-time recommendation method based on metaverse user behavior capture according to claim 2, characterized in that: The user behavior operations of the user client consist of the user using the client to operate on purchase order information, images, etc.
4. The real-time recommendation method based on metaverse user behavior capture according to claim 2, characterized in that: The behavioral data is transmitted to the server for storage in the form of electrical signals to avoid the loss of behavioral information.
5. The real-time recommendation method based on metaverse user behavior capture according to claim 2, characterized in that: The merchant client's construction module builds up user behavior information and orders, generates order data instructions which are pushed from the server to the user's client to enable user actions.
6. The real-time recommendation method based on metaverse user behavior capture according to claim 2, characterized in that: The generation module captures users' daily preferences based on their behavior and pushes information accordingly, allowing users to see content they like through their client application.