system

Abstract

The system according to this embodiment aims to recommend the most suitable products based on the user's interests and purchasing intentions. [Solution] The system according to the embodiment comprises a collection unit, a trend collection unit, an analysis unit, and a provision unit. The collection unit collects user information. The trend collection unit collects product trends based on the user information collected by the collection unit. The analysis unit analyzes the information collected by the collection unit and the trend collection unit and recommends the most suitable products to the user. The provision unit presents the products recommended by the analysis unit to the user.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, optimal products have not been sufficiently recommended based on the interests and purchase intentions of users, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend optimal products based on the interests and purchase intentions of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a trend collection unit, an analysis unit, and a provision unit. The collection unit collects user information. The trend collection unit collects product trends based on the user information collected by the collection unit. The analysis unit analyzes the information collected by the collection unit and the trend collection unit and recommends the most suitable products to the user. The provision unit presents the products recommended by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can recommend the most suitable products based on the user's interests and purchasing intentions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The product selection support system according to an embodiment of the present invention is a system that collects user information, analyzes product trends, and recommends the most suitable product to the user. The product selection support system collects user information (interests, purchase intentions, etc.) and product trends in the market, and a generating AI analyzes this data to recommend the most suitable product to the user. This mechanism allows users to easily find products based on their interests and purchase intentions. For example, the product selection support system collects information such as the user's attributes, life events, purchase intentions, purchase experience, and interests. This information is inferred based on the user's behavior history using a specific data base. For example, data such as keywords the user has searched for in the past and advertisements they have clicked are collected. Next, the product selection support system uses a specific trend map to predict products that are currently trending or likely to become popular from search data and visualizes them by category. This makes it possible to grasp current trending products. Based on the collected user information and product trends, the generating AI performs analysis. The generating AI recommends the most suitable product based on the user's interests and purchase intentions. For example, if the user is a "sweets lover," the generating AI recommends trending products such as "maritozzo." In this process, the generating AI selects the most suitable product by considering the user's past behavior history and current trend data. Finally, the AI ​​presents the recommended product to the user. For example, the chat AI might send a message like, "Have you heard of Maritozzo? It's a trendy dessert whose search volume has skyrocketed since the end of last year. I think it's perfect for you, someone with a sweet tooth and a curious mind!" This allows users to easily find products based on their interests and purchasing intentions. This system allows users to enjoy a new shopping experience and bring a "!" to their daily lives. In this way, the product selection support system makes it easy for users to find products based on their interests and purchasing intentions.

[0029] The product selection support system according to this embodiment comprises a collection unit, a trend collection unit, an analysis unit, and a provision unit. The collection unit collects user information. For example, the collection unit collects information such as user attributes, life events, purchase intentions, purchase experiences, and interests. The collection unit uses a specific data base to collect information that can be inferred based on the user's behavior history. For example, the collection unit collects data such as keywords the user has searched for in the past and advertisements they have clicked. The trend collection unit collects product trends. For example, the trend collection unit uses a specific trend map to predict products that are currently trending or likely to become popular based on search data, and visualizes them by category. The trend collection unit can grasp current trending products. The analysis unit analyzes the information collected by the collection unit and the trend collection unit and recommends the most suitable products to the user. The analysis unit uses generative AI to recommend the most suitable products based on the user's interests and purchase intentions. For example, if the user is a "sweets lover," the analysis unit recommends trending products such as "maritozzo." The analysis unit selects the most suitable product by considering the user's past behavior history and current trend data. The supply unit presents the product recommended by the analysis unit to the user. The supply unit sends a message recommending the most suitable product to the user, for example, using a chat AI. For example, the supply unit sends a message such as, "Have you heard of Maritozzo? It's a trendy dessert whose search volume has skyrocketed since the end of last year. I think it's perfect for you, who loves sweets and is very curious!" As a result, the product selection support system according to the embodiment can easily find products based on the user's interests and purchase intentions.

[0030] The data collection unit collects user information. For example, it collects information such as user attributes, life events, purchase intentions, purchase experiences, and interests. Specifically, it collects basic attribute information such as the user's age, gender, occupation, and place of residence. It also collects information on life events such as marriage, childbirth, and relocation to understand the user's living situation. Furthermore, it collects information on purchase intentions and purchase experiences, such as what products the user is interested in, what products they have purchased in the past, and what products they are currently considering purchasing. Regarding interests, it analyzes the user's search history, browsing history, and social media activity to understand what areas the user is interested in based on their hobbies and preferences. The data collection unit uses a specific data infrastructure to collect information that can be inferred based on the user's behavioral history. For example, the data collection unit collects data such as keywords the user has searched for in the past and advertisements they have clicked. This allows it to infer what products the user is interested in and collect more accurate information. The data collection unit centrally manages this information and makes it accessible to the analysis and provisioning units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to efficiently and effectively collect user information and improve the overall system performance.

[0031] The Trend Collection Department collects product trends. For example, it uses specific trend maps to predict which products are currently trending or likely to become trending based on search data, and visualizes this information by category. Specifically, the Trend Collection Department analyzes data collected from internet search engines, social media, and online shopping sites to understand current trending products. For instance, it analyzes search query data from search engines to identify rapidly rising keywords and phrases. It also analyzes social media posts, comments, and hashtag usage to identify products and topics of high user interest. Furthermore, it analyzes sales data, review counts, and ratings from online shopping sites to understand trends in popular and new products. Based on this data, the Trend Collection Department predicts which products are currently trending or likely to become trending and visualizes this information by category. For example, it lists current trending products for categories such as food, fashion, and home appliances, and displays them visually using graphs and charts. This allows the Trend Collection Department to provide users with the latest trend information and build a foundation for recommending products that will interest them.

[0032] The analysis unit analyzes information collected by the data collection unit and the trend collection unit to recommend the most suitable products to the user. The analysis unit uses generative AI to recommend the most suitable products based on the user's interests and purchase intentions. Specifically, the generative AI receives user attribute information, behavioral history, and trend data as input, and uses this data to predict the user's preferences and needs. For example, if the user is a "sweets lover," the generative AI will recommend trending products such as "maritozzo" based on past search and purchase history. The generative AI uses natural language processing technology to analyze text data such as the user's search queries, reviews, and comments to understand the user's interests. It also uses machine learning algorithms to analyze the user's behavioral patterns and purchase history to select the most suitable products. Furthermore, the generative AI considers the latest trend data provided by the trend collection unit to recommend the products that will be most interesting to the user. As a result, the analysis unit can select the most suitable products by considering the user's past behavioral history and current trend data, and provide personalized product recommendations to the user.

[0033] The service provider presents users with products recommended by the analysis department. For example, the service provider uses chat AI to send messages recommending the most suitable products to users. Specifically, the service provider generates personalized messages based on the user's interests and purchase intentions, and recommends products to the user. For example, the service provider might send a message such as, "Have you heard of Maritozzo? It's a trendy dessert that's seen a surge in searches since the end of last year. It's perfect for you, someone with a sweet tooth and a curious personality!" The chat AI uses natural language processing technology to recommend products through dialogue with the user and collects user reactions and feedback. Based on this feedback, the service provider can continuously improve the accuracy and effectiveness of its recommendations. For example, if a user shows interest in a recommended product, the service provider provides related products and additional information. If a user shows no interest in a recommended product, the service provider recommends a different product. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the sales department to quickly and reliably recommend products to users, thereby increasing their purchase intent.

[0034] The data collection unit can collect information such as user attributes, life events, purchase intentions, purchase experiences, and interests. For example, the data collection unit can collect attribute information such as the user's age, gender, and occupation. The data collection unit can also collect information on the user's life events, such as marriage, childbirth, and moving. The data collection unit can also collect information on the user's purchase intentions, such as the products the user plans to buy and when they are likely to be purchased. The data collection unit can also collect information on the user's purchase experience, such as products the user has purchased in the past and how often they have purchased them. The data collection unit can also collect information on the user's interests, such as their hobbies and categories of interest. By collecting detailed information about the user, it becomes possible to make more accurate product recommendations. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user behavior history data into AI, which can then estimate the user's attributes and interests.

[0035] The trend collection unit can use a specific trend map to predict which products are currently trending or likely to trend based on search data, and visualize them by category. For example, the trend collection unit can collect data such as sudden increases in sales or searches based on a specific trend map to predict which products are trending. The trend collection unit can use methods such as graph display or list display to visualize by category. This allows for an understanding of current trending products. Some or all of the above processing in the trend collection unit may be performed using AI, or it may be performed without AI. For example, the trend collection unit can input search data into AI, which can then predict trending products and visualize them by category.

[0036] The analysis unit can recommend the most suitable products based on the user's interests and purchase intentions. For example, if the user is a "sweets lover," the analysis unit will recommend trendy products such as "maritozzo." The analysis unit selects the most suitable products by considering the user's past behavior history and current trend data. This allows the system to recommend the most suitable products based on the user's interests and purchase intentions. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the user's behavior history data and trend data into the generation AI, which then recommends the most suitable products.

[0037] The service provider can use chat AI to send messages recommending the most suitable products to users. For example, the service provider could send a message such as, "Do you know Maritozzo? It's a trendy dessert that has seen a surge in searches since the end of last year. It's perfect for you, someone with a sweet tooth and a curious mind!" This allows the service provider to send messages recommending the most suitable products to users using chat AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider inputs product information recommended by the generation AI into the chat AI, and the chat AI sends a message to the user.

[0038] The data collection unit can analyze a user's past behavior history and select the optimal information collection method. For example, the data collection unit can collect relevant product information based on keywords that the user has frequently searched for in the past. The data collection unit can also analyze advertisements that the user has clicked on in the past and collect product information that the user might be interested in. The data collection unit can also collect relevant product information by referring to the user's past purchase history. In this way, the optimal information collection method can be selected by analyzing the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's behavior history data into the AI, and the AI ​​selects the optimal information collection method.

[0039] The data collection unit can filter information based on the user's current life stage and areas of interest. For example, if the user is newly married, the data collection unit can collect information on products needed for their new life. If the user is raising children, the data collection unit can also collect information on childcare-related products. If the user is retired, the data collection unit can also collect information on products related to hobbies and travel. By filtering information based on the user's current life stage and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs data on the user's life stage and areas of interest into the AI, and the AI ​​filters the information.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can collect information on local specialties and events in the area where the user is currently located. If the user is traveling, the data collection unit can also collect information on tourist attractions and restaurants in the travel destination. If the user is considering moving, the data collection unit can also collect information on living in the new area. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into the AI, which will then prioritize the collection of highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect product information related to posts that the user has "liked" on social media. The data collection unit can also collect product information introduced by influencers that the user follows. The data collection unit can also collect product information related to topics in groups and communities that the user participates in. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into AI, and the AI ​​can collect relevant information.

[0042] The trend collection unit can predict current trends by referring to past trend data during trend collection. For example, the trend collection unit can predict seasonal trend products based on past trend data. The trend collection unit can also predict trend products related to specific events or campaigns based on past trend data. The trend collection unit can also predict trend products popular with specific user groups based on past trend data. In this way, current trends can be predicted by referring to past trend data. Some or all of the above processing in the trend collection unit may be performed using AI or not. For example, the trend collection unit inputs past trend data into AI, and the AI ​​predicts current trends.

[0043] The trend collection unit can apply different trend collection methods to each product category when collecting trends. For example, in the fashion category, the trend collection unit can collect information on the latest fashion shows and magazines. In the technology category, the trend collection unit can also collect information on new product announcements and review sites. In the food category, the trend collection unit can also collect information on popular recipe sites and gourmet blogs. By applying different trend collection methods to each product category, more accurate trend information can be collected. Some or all of the above processing in the trend collection unit may be performed using AI or not. For example, the trend collection unit inputs data for each category into the AI, and the AI ​​applies the most suitable trend collection method for each category.

[0044] The trend collection unit can collect trends while considering geographical distribution. For example, the trend collection unit can collect product trends that are popular in a specific region. The trend collection unit can also collect product trends related to seasonal events in each region. The trend collection unit can also collect product trends related to culture and customs in each region. This allows for the collection of regional trend information by considering geographical distribution. Some or all of the above processing in the trend collection unit may be performed using AI or not. For example, the trend collection unit inputs regional data into the AI, and the AI ​​collects trends while considering geographical distribution.

[0045] The trend collection unit can improve the accuracy of trend collection by referring to relevant literature during the trend collection process. For example, the trend collection unit can collect trends by referring to the latest research papers and market research reports. The trend collection unit can also collect trends by referring to expert opinions and interview articles. The trend collection unit can also collect trends by referring to industry news and press releases. In this way, the accuracy of trend collection can be improved by referring to relevant literature. Some or all of the above processes in the trend collection unit may be performed using AI or not. For example, the trend collection unit may input data from relevant literature into AI, and the AI ​​may improve the accuracy of trend collection.

[0046] The analysis unit can recommend the most suitable products by referring to the user's past purchase history during analysis. For example, the analysis unit can recommend products similar to those the user has previously purchased. The analysis unit can also recommend related products based on the user's past purchase history. The analysis unit can also analyze the user's past purchase history and recommend the most suitable product. In this way, the optimal product can be recommended by referring to the user's past purchase history. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs the user's purchase history data into the generating AI, and the generating AI recommends the most suitable product.

[0047] The analysis unit can customize its product recommendation method based on the user's current life stage during analysis. For example, if the user is newly married, the analysis unit will recommend products necessary for starting a new life. If the user is raising children, the analysis unit can also recommend childcare-related products. If the user is retired, the analysis unit can also recommend products related to hobbies or travel. By customizing the product recommendation method based on the user's current life stage, it can recommend more appropriate products. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the user's life stage data into the generative AI, and the generative AI customizes the product recommendation method.

[0048] The analysis unit can recommend products while considering geographical distribution during analysis. For example, the analysis unit can recommend products based on local specialties and event information in the area where the user is currently located. If the user is traveling, the analysis unit can also recommend products based on tourist attractions and restaurant information in the travel destination. If the user is considering moving, the analysis unit can also recommend products based on lifestyle information in the new area. In this way, by considering geographical distribution, the optimal products for each region can be recommended. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the user's geographical distribution data into the generative AI, and the generative AI recommends the optimal products.

[0049] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit performs analysis by referring to the latest research papers and market research reports. The analysis unit can also perform analysis by referring to expert opinions and interview articles. The analysis unit can also perform analysis by referring to industry news and press releases. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processes in the analysis unit are performed using a generating AI. For example, the analysis unit inputs data from relevant literature into the generating AI, and the generating AI improves the accuracy of the analysis.

[0050] The delivery unit can select the most suitable message by referring to the user's past response history at the time of delivery. For example, the delivery unit can provide similar messages based on patterns of messages to which the user has previously responded favorably. The delivery unit can also provide new messages while avoiding patterns of messages to which the user has previously not responded. The delivery unit can also analyze the user's past response history and select the most effective message. In this way, the optimal message can be selected by referring to the user's past response history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit inputs the user's response history data into AI, and the AI ​​selects the optimal message.

[0051] The delivery unit can customize the content of messages based on the user's current life stage at the time of delivery. For example, if the user is newly married, the delivery unit can provide a message introducing products related to starting a new life. If the user is raising children, the delivery unit can also provide a message introducing products related to childcare. If the user is retired, the delivery unit can also provide a message introducing products related to hobbies or travel. By customizing the content of messages based on the user's current life stage, more appropriate messages can be provided. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's life stage data into the AI, and the AI ​​can customize the content of the messages.

[0052] The service provider can provide the most suitable message by considering the user's geographical location at the time of delivery. For example, the service provider can provide a message introducing local specialties and event information in the area where the user is currently located. If the user is traveling, the service provider can also provide a message introducing tourist attractions and restaurants in the travel destination. If the user is considering moving, the service provider can also provide a message introducing information about living in the new area. In this way, the service provider can provide the most suitable message by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into the AI, and the AI ​​can provide the most suitable message.

[0053] The service provider can analyze a user's social media activity and provide relevant messages at the time of delivery. For example, the service provider can provide messages introducing products related to posts the user has "liked" on social media. The service provider can also provide messages introducing products introduced by influencers the user follows. The service provider can also provide messages introducing products related to topics in groups or communities the user participates in. In this way, relevant messages can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can provide relevant messages.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The product selection support system may further include a purchase history analysis unit that analyzes the user's purchase history and recommends products based on that history. For example, it can recommend products similar to those the user has purchased in the past. If the user prefers a particular brand, it can also recommend new products from that brand. If the expiration date of a product the user has purchased in the past is approaching, it can also recommend products that encourage repurchase. This allows for the recommendation of more appropriate products based on the user's purchase history. Some or all of the above processing in the purchase history analysis unit may be performed using AI or not. For example, the purchase history analysis unit inputs the user's purchase history data into the AI, which analyzes the purchase history and recommends the most suitable products.

[0056] The product selection support system may further include a social media analysis unit that analyzes the user's social media activity and recommends products based on that activity. For example, it can recommend products related to posts that the user has "liked" on social media. It can also recommend products introduced by influencers that the user follows. It can also recommend products related to topics in groups or communities that the user participates in. This allows for the recommendation of more appropriate products based on the user's social media activity. Some or all of the above processing in the social media analysis unit may be performed using AI or not. For example, the social media analysis unit inputs the user's social media activity data into the AI, which analyzes the social media activity and recommends the most suitable products.

[0057] The product selection support system may also include a geographic information analysis unit that recommends products while considering the user's geographic location. For example, it can recommend products based on local specialties and event information in the area where the user is currently located. If the user is traveling, it can also recommend products based on tourist attractions and restaurant information in their destination. If the user is considering moving, it can also recommend products based on lifestyle information in the new area. This allows for the recommendation of more appropriate products by considering the user's geographic location. Some or all of the above processing in the geographic information analysis unit may be performed using AI or not. For example, the geographic information analysis unit inputs the user's geographic location information into the AI, which analyzes the geographic information and recommends the most suitable products.

[0058] The product selection support system may further include a life stage analysis unit that recommends products based on the user's life stage. For example, if the user is newly married, it can recommend products necessary for starting a new life. If the user is raising children, it can recommend childcare-related products. If the user is retired, it can recommend products related to hobbies or travel. This allows for the recommendation of more appropriate products based on the user's life stage. Some or all of the above processing in the life stage analysis unit may be performed using AI or not. For example, the life stage analysis unit inputs the user's life stage data into the AI, which analyzes the life stage and recommends the most suitable products.

[0059] The product selection support system may further include a reaction history analysis unit that analyzes the user's past reaction history and recommends products based on that history. For example, it can recommend products similar to those the user has shown a positive reaction to in the past. It can also recommend new products while avoiding those the user has shown no reaction to in the past. It can also analyze the user's past reaction history and recommend the most effective products. This allows for the recommendation of more appropriate products based on the user's past reaction history. Some or all of the above processing in the reaction history analysis unit may be performed using AI or not. For example, the reaction history analysis unit inputs the user's reaction history data into the AI, which analyzes the reaction history and recommends the optimal product.

[0060] The product selection support system may further include a life stage customization unit that customizes the product recommendation method based on the user's current life stage. For example, if the user is newly married, it may recommend products necessary for starting a new life. If the user is raising children, it may also recommend childcare-related products. If the user is retired, it may also recommend products related to hobbies or travel. By customizing the product recommendation method based on the user's current life stage, it is possible to recommend more appropriate products. Some or all of the above processing in the life stage customization unit may be performed using AI or not. For example, the life stage customization unit inputs the user's life stage data into the AI, and the AI ​​customizes the product recommendation method.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects user information. For example, the data collection unit collects information such as user attributes, life events, purchase intent, purchase experience, and interests. The data collection unit uses a specific data infrastructure to collect information that can be inferred from the user's behavioral history. For example, the data collection unit collects data such as keywords the user has searched for in the past and advertisements they have clicked. Step 2: The trend collection unit collects product trends. For example, the trend collection unit uses a specific trend map to predict which products are currently trending or likely to become popular based on search data, and visualizes them by category. The trend collection unit can then understand which products are currently trending. Step 3: The analysis unit analyzes the information collected by the data collection unit and the trend collection unit and recommends the most suitable products to the user. The analysis unit uses a generation AI to recommend the most suitable products based on the user's interests and purchase intentions. For example, if the user is a "sweets lover," the analysis unit will recommend trending products such as "maritozzo." The analysis unit selects the most suitable products by considering the user's past behavior history and current trend data. Step 4: The sales team presents the products recommended by the analysis team to the user. The sales team sends a message recommending the most suitable product to the user, for example, using a chat AI. For example, the sales team might send a message like, "Have you heard of Maritozzo? It's a trendy dessert that has seen a surge in searches since the end of last year. It's perfect for you, someone with a sweet tooth and a curious personality!"

[0063] (Example of form 2) The product selection support system according to an embodiment of the present invention is a system that collects user information, analyzes product trends, and recommends the most suitable product to the user. The product selection support system collects user information (interests, purchase intentions, etc.) and product trends in the market, and a generating AI analyzes this data to recommend the most suitable product to the user. This mechanism allows users to easily find products based on their interests and purchase intentions. For example, the product selection support system collects information such as the user's attributes, life events, purchase intentions, purchase experience, and interests. This information is inferred based on the user's behavior history using a specific data base. For example, data such as keywords the user has searched for in the past and advertisements they have clicked are collected. Next, the product selection support system uses a specific trend map to predict products that are currently trending or likely to become popular from search data and visualizes them by category. This makes it possible to grasp current trending products. Based on the collected user information and product trends, the generating AI performs analysis. The generating AI recommends the most suitable product based on the user's interests and purchase intentions. For example, if the user is a "sweets lover," the generating AI recommends trending products such as "maritozzo." In this process, the generating AI selects the most suitable product by considering the user's past behavior history and current trend data. Finally, the AI ​​presents the recommended product to the user. For example, the chat AI might send a message like, "Have you heard of Maritozzo? It's a trendy dessert whose search volume has skyrocketed since the end of last year. I think it's perfect for you, someone with a sweet tooth and a curious mind!" This allows users to easily find products based on their interests and purchasing intentions. This system allows users to enjoy a new shopping experience and bring a "!" to their daily lives. In this way, the product selection support system makes it easy for users to find products based on their interests and purchasing intentions.

[0064] The product selection support system according to this embodiment comprises a collection unit, a trend collection unit, an analysis unit, and a provision unit. The collection unit collects user information. For example, the collection unit collects information such as user attributes, life events, purchase intentions, purchase experiences, and interests. The collection unit uses a specific data base to collect information that can be inferred based on the user's behavior history. For example, the collection unit collects data such as keywords the user has searched for in the past and advertisements they have clicked. The trend collection unit collects product trends. For example, the trend collection unit uses a specific trend map to predict products that are currently trending or likely to become popular based on search data, and visualizes them by category. The trend collection unit can grasp current trending products. The analysis unit analyzes the information collected by the collection unit and the trend collection unit and recommends the most suitable products to the user. The analysis unit uses generative AI to recommend the most suitable products based on the user's interests and purchase intentions. For example, if the user is a "sweets lover," the analysis unit recommends trending products such as "maritozzo." The analysis unit selects the most suitable product by considering the user's past behavior history and current trend data. The supply unit presents the product recommended by the analysis unit to the user. The supply unit sends a message recommending the most suitable product to the user, for example, using a chat AI. For example, the supply unit sends a message such as, "Have you heard of Maritozzo? It's a trendy dessert whose search volume has skyrocketed since the end of last year. I think it's perfect for you, who loves sweets and is very curious!" As a result, the product selection support system according to the embodiment can easily find products based on the user's interests and purchase intentions.

[0065] The data collection unit collects user information. For example, it collects information such as user attributes, life events, purchase intentions, purchase experiences, and interests. Specifically, it collects basic attribute information such as the user's age, gender, occupation, and place of residence. It also collects information on life events such as marriage, childbirth, and relocation to understand the user's living situation. Furthermore, it collects information on purchase intentions and purchase experiences, such as what products the user is interested in, what products they have purchased in the past, and what products they are currently considering purchasing. Regarding interests, it analyzes the user's search history, browsing history, and social media activity to understand what areas the user is interested in based on their hobbies and preferences. The data collection unit uses a specific data infrastructure to collect information that can be inferred based on the user's behavioral history. For example, the data collection unit collects data such as keywords the user has searched for in the past and advertisements they have clicked. This allows it to infer what products the user is interested in and collect more accurate information. The data collection unit centrally manages this information and makes it accessible to the analysis and provisioning units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to efficiently and effectively collect user information and improve the overall system performance.

[0066] The Trend Collection Department collects product trends. For example, it uses specific trend maps to predict which products are currently trending or likely to become trending based on search data, and visualizes this information by category. Specifically, the Trend Collection Department analyzes data collected from internet search engines, social media, and online shopping sites to understand current trending products. For instance, it analyzes search query data from search engines to identify rapidly rising keywords and phrases. It also analyzes social media posts, comments, and hashtag usage to identify products and topics of high user interest. Furthermore, it analyzes sales data, review counts, and ratings from online shopping sites to understand trends in popular and new products. Based on this data, the Trend Collection Department predicts which products are currently trending or likely to become trending and visualizes this information by category. For example, it lists current trending products for categories such as food, fashion, and home appliances, and displays them visually using graphs and charts. This allows the Trend Collection Department to provide users with the latest trend information and build a foundation for recommending products that will interest them.

[0067] The analysis unit analyzes information collected by the data collection unit and the trend collection unit to recommend the most suitable products to the user. The analysis unit uses generative AI to recommend the most suitable products based on the user's interests and purchase intentions. Specifically, the generative AI receives user attribute information, behavioral history, and trend data as input, and uses this data to predict the user's preferences and needs. For example, if the user is a "sweets lover," the generative AI will recommend trending products such as "maritozzo" based on past search and purchase history. The generative AI uses natural language processing technology to analyze text data such as the user's search queries, reviews, and comments to understand the user's interests. It also uses machine learning algorithms to analyze the user's behavioral patterns and purchase history to select the most suitable products. Furthermore, the generative AI considers the latest trend data provided by the trend collection unit to recommend the products that will be most interesting to the user. As a result, the analysis unit can select the most suitable products by considering the user's past behavioral history and current trend data, and provide personalized product recommendations to the user.

[0068] The service provider presents users with products recommended by the analysis department. For example, the service provider uses chat AI to send messages recommending the most suitable products to users. Specifically, the service provider generates personalized messages based on the user's interests and purchase intentions, and recommends products to the user. For example, the service provider might send a message such as, "Have you heard of Maritozzo? It's a trendy dessert that's seen a surge in searches since the end of last year. It's perfect for you, someone with a sweet tooth and a curious personality!" The chat AI uses natural language processing technology to recommend products through dialogue with the user and collects user reactions and feedback. Based on this feedback, the service provider can continuously improve the accuracy and effectiveness of its recommendations. For example, if a user shows interest in a recommended product, the service provider provides related products and additional information. If a user shows no interest in a recommended product, the service provider recommends a different product. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the sales department to quickly and reliably recommend products to users, thereby increasing their purchase intent.

[0069] The data collection unit can collect information such as user attributes, life events, purchase intentions, purchase experiences, and interests. For example, the data collection unit can collect attribute information such as the user's age, gender, and occupation. The data collection unit can also collect information on the user's life events, such as marriage, childbirth, and moving. The data collection unit can also collect information on the user's purchase intentions, such as the products the user plans to buy and when they are likely to be purchased. The data collection unit can also collect information on the user's purchase experience, such as products the user has purchased in the past and how often they have purchased them. The data collection unit can also collect information on the user's interests, such as their hobbies and categories of interest. By collecting detailed information about the user, it becomes possible to make more accurate product recommendations. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user behavior history data into AI, which can then estimate the user's attributes and interests.

[0070] The trend collection unit can use a specific trend map to predict which products are currently trending or likely to trend based on search data, and visualize them by category. For example, the trend collection unit can collect data such as sudden increases in sales or searches based on a specific trend map to predict which products are trending. The trend collection unit can use methods such as graph display or list display to visualize by category. This allows for an understanding of current trending products. Some or all of the above processing in the trend collection unit may be performed using AI, or it may be performed without AI. For example, the trend collection unit can input search data into AI, which can then predict trending products and visualize them by category.

[0071] The analysis unit can recommend the most suitable products based on the user's interests and purchase intentions. For example, if the user is a "sweets lover," the analysis unit will recommend trendy products such as "maritozzo." The analysis unit selects the most suitable products by considering the user's past behavior history and current trend data. This allows the system to recommend the most suitable products based on the user's interests and purchase intentions. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the user's behavior history data and trend data into the generation AI, which then recommends the most suitable products.

[0072] The service provider can use chat AI to send messages recommending the most suitable products to users. For example, the service provider could send a message such as, "Do you know Maritozzo? It's a trendy dessert that has seen a surge in searches since the end of last year. It's perfect for you, someone with a sweet tooth and a curious mind!" This allows the service provider to send messages recommending the most suitable products to users using chat AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider inputs product information recommended by the generation AI into the chat AI, and the chat AI sends a message to the user.

[0073] The data collection unit can estimate the user's emotions and adjust the type of information it collects based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information on products that promote relaxation. If the user is excited, the data collection unit may also collect information on products related to active hobbies. If the user is tired, the data collection unit may also collect information on products that promote refreshment. By adjusting the type of information collected based on the user's emotions, more appropriate information can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's facial expression data into a generative AI, which estimates the user's emotions and adjusts the type of information to collect.

[0074] The data collection unit can analyze a user's past behavior history and select the optimal information collection method. For example, the data collection unit can collect relevant product information based on keywords that the user has frequently searched for in the past. The data collection unit can also analyze advertisements that the user has clicked on in the past and collect product information that the user might be interested in. The data collection unit can also collect relevant product information by referring to the user's past purchase history. In this way, the optimal information collection method can be selected by analyzing the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's behavior history data into the AI, and the AI ​​selects the optimal information collection method.

[0075] The data collection unit can filter information based on the user's current life stage and areas of interest. For example, if the user is newly married, the data collection unit can collect information on products needed for their new life. If the user is raising children, the data collection unit can also collect information on childcare-related products. If the user is retired, the data collection unit can also collect information on products related to hobbies and travel. By filtering information based on the user's current life stage and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs data on the user's life stage and areas of interest into the AI, and the AI ​​filters the information.

[0076] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting information on products that promote relaxation. If the user is excited, the data collection unit may also prioritize collecting information on products related to active hobbies. If the user is tired, the data collection unit may also prioritize collecting information on products that promote refreshment. By prioritizing the information to collect based on the user's emotions, more appropriate information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's facial expression data into the generative AI, which estimates the user's emotions and determines the priority of information to collect.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can collect information on local specialties and events in the area where the user is currently located. If the user is traveling, the data collection unit can also collect information on tourist attractions and restaurants in the travel destination. If the user is considering moving, the data collection unit can also collect information on living in the new area. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into the AI, which will then prioritize the collection of highly relevant information.

[0078] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect product information related to posts that the user has "liked" on social media. The data collection unit can also collect product information introduced by influencers that the user follows. The data collection unit can also collect product information related to topics in groups and communities that the user participates in. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into AI, and the AI ​​can collect relevant information.

[0079] The trend collection unit can estimate the user's emotions and adjust the trend collection criteria based on the estimated emotions. For example, if the user is relaxed, the trend collection unit will prioritize collecting relaxing product trends. If the user is excited, the trend collection unit can also prioritize collecting active product trends. If the user is tired, the trend collection unit can also prioritize collecting refreshing product trends. This allows for the collection of more appropriate trend information by adjusting the trend collection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the trend collection unit may be performed using AI or not. For example, the trend collection unit inputs user facial expression data into the generative AI, which estimates the user's emotions and adjusts the trend collection criteria.

[0080] The trend collection unit can predict current trends by referring to past trend data during trend collection. For example, the trend collection unit can predict seasonal trend products based on past trend data. The trend collection unit can also predict trend products related to specific events or campaigns based on past trend data. The trend collection unit can also predict trend products popular with specific user groups based on past trend data. In this way, current trends can be predicted by referring to past trend data. Some or all of the above processing in the trend collection unit may be performed using AI or not. For example, the trend collection unit inputs past trend data into AI, and the AI ​​predicts current trends.

[0081] The trend collection unit can apply different trend collection methods to each product category when collecting trends. For example, in the fashion category, the trend collection unit can collect information on the latest fashion shows and magazines. In the technology category, the trend collection unit can also collect information on new product announcements and review sites. In the food category, the trend collection unit can also collect information on popular recipe sites and gourmet blogs. By applying different trend collection methods to each product category, more accurate trend information can be collected. Some or all of the above processing in the trend collection unit may be performed using AI or not. For example, the trend collection unit inputs data for each category into the AI, and the AI ​​applies the most suitable trend collection method for each category.

[0082] The trend collection unit can estimate the user's emotions and adjust the way trends are displayed based on the estimated emotions. For example, if the user is relaxed, the trend collection unit can display relaxing product trends with a visually calming design. If the user is excited, the trend collection unit can also display active product trends with a visually stimulating design. If the user is tired, the trend collection unit can also display refreshing product trends with a visually simple design. This allows for the display of more appropriate trend information by adjusting the way trends are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the trend collection unit may be performed using AI or not. For example, the trend collection unit inputs user facial expression data into the generative AI, which estimates the user's emotions and adjusts the way trends are displayed.

[0083] The trend collection unit can collect trends while considering geographical distribution. For example, the trend collection unit can collect product trends that are popular in a specific region. The trend collection unit can also collect product trends related to seasonal events in each region. The trend collection unit can also collect product trends related to culture and customs in each region. This allows for the collection of regional trend information by considering geographical distribution. Some or all of the above processing in the trend collection unit may be performed using AI or not. For example, the trend collection unit inputs regional data into the AI, and the AI ​​collects trends while considering geographical distribution.

[0084] The trend collection unit can improve the accuracy of trend collection by referring to relevant literature during the trend collection process. For example, the trend collection unit can collect trends by referring to the latest research papers and market research reports. The trend collection unit can also collect trends by referring to expert opinions and interview articles. The trend collection unit can also collect trends by referring to industry news and press releases. In this way, the accuracy of trend collection can be improved by referring to relevant literature. Some or all of the above processes in the trend collection unit may be performed using AI or not. For example, the trend collection unit may input data from relevant literature into AI, and the AI ​​may improve the accuracy of trend collection.

[0085] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the generation AI can adjust the algorithm to prioritize recommending relaxing products. If the user is excited, the generation AI can adjust the algorithm to prioritize recommending active products. If the user is tired, the generation AI can adjust the algorithm to prioritize recommending refreshing products. By adjusting the analysis algorithm based on the user's emotions, more appropriate products can be recommended. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs user facial expression data into the generation AI, which estimates the user's emotions and adjusts the analysis algorithm.

[0086] The analysis unit can recommend the most suitable products by referring to the user's past purchase history during analysis. For example, the analysis unit can recommend products similar to those the user has previously purchased. The analysis unit can also recommend related products based on the user's past purchase history. The analysis unit can also analyze the user's past purchase history and recommend the most suitable product. In this way, the optimal product can be recommended by referring to the user's past purchase history. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs the user's purchase history data into the generating AI, and the generating AI recommends the most suitable product.

[0087] The analysis unit can customize its product recommendation method based on the user's current life stage during analysis. For example, if the user is newly married, the analysis unit will recommend products necessary for starting a new life. If the user is raising children, the analysis unit can also recommend childcare-related products. If the user is retired, the analysis unit can also recommend products related to hobbies or travel. By customizing the product recommendation method based on the user's current life stage, it can recommend more appropriate products. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the user's life stage data into the generative AI, and the generative AI customizes the product recommendation method.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display relaxing products with a visually calming design. If the user is excited, the analysis unit can also display active products with a visually stimulating design. If the user is tired, the analysis unit can also display refreshing products with a visually simple design. In this way, by adjusting the display method of the analysis results based on the user's emotions, more appropriate products can be displayed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs the user's facial expression data into the generative AI, the generative AI estimates the user's emotions, and adjusts the display method of the analysis results.

[0089] The analysis unit can recommend products while considering geographical distribution during analysis. For example, the analysis unit can recommend products based on local specialties and event information in the area where the user is currently located. If the user is traveling, the analysis unit can also recommend products based on tourist attractions and restaurant information in the travel destination. If the user is considering moving, the analysis unit can also recommend products based on lifestyle information in the new area. In this way, by considering geographical distribution, the optimal products for each region can be recommended. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the user's geographical distribution data into the generative AI, and the generative AI recommends the optimal products.

[0090] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit performs analysis by referring to the latest research papers and market research reports. The analysis unit can also perform analysis by referring to expert opinions and interview articles. The analysis unit can also perform analysis by referring to industry news and press releases. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processes in the analysis unit are performed using a generating AI. For example, the analysis unit inputs data from relevant literature into the generating AI, and the generating AI improves the accuracy of the analysis.

[0091] The delivery unit can estimate the user's emotions and adjust the way the message is expressed based on the estimated emotions. For example, if the user is relaxed, the delivery unit can deliver the message in a calm tone. If the user is excited, the delivery unit can deliver the message in a lively tone. If the user is tired, the delivery unit can deliver the message in a simple and easy-to-understand tone. In this way, by adjusting the way the message is expressed based on the user's emotions, a more appropriate message can be delivered. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit inputs user facial expression data into a generative AI, the generative AI estimates the user's emotions, and adjusts the way the message is expressed.

[0092] The delivery unit can select the most suitable message by referring to the user's past response history at the time of delivery. For example, the delivery unit can provide similar messages based on patterns of messages to which the user has previously responded favorably. The delivery unit can also provide new messages while avoiding patterns of messages to which the user has previously not responded. The delivery unit can also analyze the user's past response history and select the most effective message. In this way, the optimal message can be selected by referring to the user's past response history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit inputs the user's response history data into AI, and the AI ​​selects the optimal message.

[0093] The delivery unit can customize the content of messages based on the user's current life stage at the time of delivery. For example, if the user is newly married, the delivery unit can provide a message introducing products related to starting a new life. If the user is raising children, the delivery unit can also provide a message introducing products related to childcare. If the user is retired, the delivery unit can also provide a message introducing products related to hobbies or travel. By customizing the content of messages based on the user's current life stage, more appropriate messages can be provided. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's life stage data into the AI, and the AI ​​can customize the content of the messages.

[0094] The service provider can estimate the user's emotions and determine the priority of messages to deliver based on the estimated emotions. For example, if the user is relaxed, the service provider can prioritize messages recommending relaxing products. If the user is excited, the service provider can also prioritize messages recommending active products. If the user is tired, the service provider can also prioritize messages recommending refreshing products. By prioritizing messages based on the user's emotions, more appropriate messages can be delivered. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider inputs user facial expression data into a generative AI, which estimates the user's emotions and determines the priority of messages.

[0095] The service provider can provide the most suitable message by considering the user's geographical location at the time of delivery. For example, the service provider can provide a message introducing local specialties and event information in the area where the user is currently located. If the user is traveling, the service provider can also provide a message introducing tourist attractions and restaurants in the travel destination. If the user is considering moving, the service provider can also provide a message introducing information about living in the new area. In this way, the service provider can provide the most suitable message by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into the AI, and the AI ​​can provide the most suitable message.

[0096] The service provider can analyze a user's social media activity and provide relevant messages at the time of delivery. For example, the service provider can provide messages introducing products related to posts the user has "liked" on social media. The service provider can also provide messages introducing products introduced by influencers the user follows. The service provider can also provide messages introducing products related to topics in groups or communities the user participates in. In this way, relevant messages can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can provide relevant messages.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The product selection support system may further include a health monitoring unit that monitors the user's health status and recommends products based on that status. For example, if the user is using a fitness tracker, the health monitoring unit collects data such as the user's exercise level and heart rate and analyzes their health status. If the user is feeling stressed, it can recommend products that help them relax. If the user is not getting enough exercise, it can also recommend fitness-related products. This allows for the recommendation of more appropriate products based on the user's health status. Some or all of the above processing in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit inputs the user's health data into the AI, which analyzes the health status and recommends the most suitable products.

[0099] The product selection support system may further include a purchase history analysis unit that analyzes the user's purchase history and recommends products based on that history. For example, it can recommend products similar to those the user has purchased in the past. If the user prefers a particular brand, it can also recommend new products from that brand. If the expiration date of a product the user has purchased in the past is approaching, it can also recommend products that encourage repurchase. This allows for the recommendation of more appropriate products based on the user's purchase history. Some or all of the above processing in the purchase history analysis unit may be performed using AI or not. For example, the purchase history analysis unit inputs the user's purchase history data into the AI, which analyzes the purchase history and recommends the most suitable products.

[0100] The product selection support system may further include a social media analysis unit that analyzes the user's social media activity and recommends products based on that activity. For example, it can recommend products related to posts that the user has "liked" on social media. It can also recommend products introduced by influencers that the user follows. It can also recommend products related to topics in groups or communities that the user participates in. This allows for the recommendation of more appropriate products based on the user's social media activity. Some or all of the above processing in the social media analysis unit may be performed using AI or not. For example, the social media analysis unit inputs the user's social media activity data into the AI, which analyzes the social media activity and recommends the most suitable products.

[0101] The product selection support system may also include a geographic information analysis unit that recommends products while considering the user's geographic location. For example, it can recommend products based on local specialties and event information in the area where the user is currently located. If the user is traveling, it can also recommend products based on tourist attractions and restaurant information in their destination. If the user is considering moving, it can also recommend products based on lifestyle information in the new area. This allows for the recommendation of more appropriate products by considering the user's geographic location. Some or all of the above processing in the geographic information analysis unit may be performed using AI or not. For example, the geographic information analysis unit inputs the user's geographic location information into the AI, which analyzes the geographic information and recommends the most suitable products.

[0102] The product selection support system may further include an emotion analysis unit that estimates the user's emotions and recommends products based on those estimated emotions. For example, if the user is stressed, it can recommend products that promote relaxation. If the user is excited, it can recommend active products. If the user is tired, it can recommend products that promote refreshment. This allows for the recommendation of more appropriate products based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion analysis unit may be performed using AI or not. For example, the emotion analysis unit inputs the user's facial expression data into the generative AI, which estimates the user's emotions and recommends the most suitable product.

[0103] The product selection support system may further include a life stage analysis unit that recommends products based on the user's life stage. For example, if the user is newly married, it can recommend products necessary for starting a new life. If the user is raising children, it can recommend childcare-related products. If the user is retired, it can recommend products related to hobbies or travel. This allows for the recommendation of more appropriate products based on the user's life stage. Some or all of the above processing in the life stage analysis unit may be performed using AI or not. For example, the life stage analysis unit inputs the user's life stage data into the AI, which analyzes the life stage and recommends the most suitable products.

[0104] The product selection support system may further include an emotion algorithm adjustment unit that estimates the user's emotions and adjusts the product recommendation algorithm based on the estimated user emotions. For example, if the user is relaxed, the generating AI can adjust the algorithm to prioritize recommending relaxing products. If the user is excited, the generating AI can also adjust the algorithm to prioritize recommending active products. If the user is tired, the generating AI can also adjust the algorithm to prioritize recommending refreshing products. In this way, by adjusting the product recommendation algorithm based on the user's emotions, more appropriate products can be recommended. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion algorithm adjustment unit is performed using the generating AI. For example, the emotion algorithm adjustment unit inputs the user's facial expression data into the generating AI, the generating AI estimates the user's emotions, and adjusts the product recommendation algorithm.

[0105] The product selection support system may further include a reaction history analysis unit that analyzes the user's past reaction history and recommends products based on that history. For example, it can recommend products similar to those the user has shown a positive reaction to in the past. It can also recommend new products while avoiding those the user has shown no reaction to in the past. It can also analyze the user's past reaction history and recommend the most effective products. This allows for the recommendation of more appropriate products based on the user's past reaction history. Some or all of the above processing in the reaction history analysis unit may be performed using AI or not. For example, the reaction history analysis unit inputs the user's reaction history data into the AI, which analyzes the reaction history and recommends the optimal product.

[0106] The product selection support system may further include an emotion display adjustment unit that estimates the user's emotions and adjusts the display method of product recommendations based on the estimated user emotions. For example, if the user is relaxed, relaxing products may be displayed with a visually calming design. If the user is excited, active products may be displayed with a visually stimulating design. If the user is tired, refreshing products may be displayed with a visually simple design. In this way, by adjusting the display method of product recommendations based on the user's emotions, more appropriate products can be displayed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion display adjustment unit is performed using the generative AI. For example, the emotion display adjustment unit inputs the user's facial expression data into the generative AI, which estimates the user's emotions and adjusts the display method of product recommendations.

[0107] The product selection support system may further include a life stage customization unit that customizes the product recommendation method based on the user's current life stage. For example, if the user is newly married, it may recommend products necessary for starting a new life. If the user is raising children, it may also recommend childcare-related products. If the user is retired, it may also recommend products related to hobbies or travel. By customizing the product recommendation method based on the user's current life stage, it is possible to recommend more appropriate products. Some or all of the above processing in the life stage customization unit may be performed using AI or not. For example, the life stage customization unit inputs the user's life stage data into the AI, and the AI ​​customizes the product recommendation method.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects user information. For example, the data collection unit collects information such as user attributes, life events, purchase intent, purchase experience, and interests. The data collection unit uses a specific data infrastructure to collect information that can be inferred from the user's behavioral history. For example, the data collection unit collects data such as keywords the user has searched for in the past and advertisements they have clicked. Step 2: The trend collection unit collects product trends. For example, the trend collection unit uses a specific trend map to predict which products are currently trending or likely to become popular based on search data, and visualizes them by category. The trend collection unit can then understand which products are currently trending. Step 3: The analysis unit analyzes the information collected by the data collection unit and the trend collection unit and recommends the most suitable products to the user. The analysis unit uses a generation AI to recommend the most suitable products based on the user's interests and purchase intentions. For example, if the user is a "sweets lover," the analysis unit will recommend trending products such as "maritozzo." The analysis unit selects the most suitable products by considering the user's past behavior history and current trend data. Step 4: The sales team presents the products recommended by the analysis team to the user. The sales team sends a message recommending the most suitable product to the user, for example, using a chat AI. For example, the sales team might send a message like, "Have you heard of Maritozzo? It's a trendy dessert that has seen a surge in searches since the end of last year. It's perfect for you, someone with a sweet tooth and a curious personality!"

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the collection unit, trend collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects the user's attributes and behavioral history. The trend collection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and collects product trends using a trend map and visualizes them by category. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and recommends the most suitable product to the user using a generation AI. The provision unit is implemented in the specific processing unit 46A of the smart device 14, for example, and sends a message recommending the most suitable product to the user using a chat AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, trend collection unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects the user's attributes and behavioral history. The trend collection unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and collects product trends using a trend map and visualizes them by category. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and recommends the most suitable product to the user using a generation AI. The provision unit is implemented in the control unit 46A of the smart glasses 214, for example, and sends a message recommending the most suitable product to the user using a chat AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, trend collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects the user's attributes and behavioral history. The trend collection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and collects product trends using a trend map and visualizes them by category. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and recommends the most suitable product to the user using a generation AI. The provision unit is implemented in the control unit 46A of the headset terminal 314, for example, and sends a message recommending the most suitable product to the user using a chat AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, trend collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects the user's attributes and behavioral history. The trend collection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and collects product trends using a trend map and visualizes them by category. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and recommends the most suitable product to the user using a generation AI. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and sends a message recommending the most suitable product to the user using a chat AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A collection unit that collects user information, A trend collection unit collects product trends based on user information collected by the aforementioned collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit and the aforementioned trend collection unit and recommends the most suitable product to the user. The system includes a provisioning unit that presents the products recommended by the analysis unit to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect information such as user attributes, life events, purchase intentions, purchase experiences, and interests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned trend collection unit, Using specific trend maps, predict which products are currently trending or likely to become so based on search data, and visualize them by category. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We recommend the most suitable products based on the user's interests and purchasing intentions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Use chat AI to send messages recommending the best products to users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current life stage and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned trend collection unit, We estimate user sentiment and adjust the trend collection criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned trend collection unit, When collecting trends, we refer to past trend data to predict current trends. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned trend collection unit, When collecting trends, apply different trend collection methods to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned trend collection unit, It estimates user sentiment and adjusts how trends are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned trend collection unit, When collecting trends, consider their geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned trend collection unit, When collecting trends, refer to relevant literature to improve the accuracy of trend collection. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the system recommends the most suitable products by referencing the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the product recommendation method is customized based on the user's current life stage. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, products are recommended while considering their geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way messages are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When delivering a message, the system selects the most appropriate message by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When delivering the message, customize the content based on the user's current life stage. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of messages to deliver based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When delivering the service, the most appropriate message will be provided, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and deliver relevant messages. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user information, A trend collection unit collects product trends based on user information collected by the aforementioned collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit and the aforementioned trend collection unit and recommends the most suitable product to the user. The system includes a provisioning unit that presents the products recommended by the analysis unit to the user. A system characterized by the following features.

2. The aforementioned collection unit is We collect information such as user attributes, life events, purchase intentions, purchase experiences, and interests. The system according to feature 1.

3. The aforementioned trend collection unit, Using specific trend maps, predict which products are currently trending or likely to become so based on search data, and visualize them by category. The system according to feature 1.

4. The aforementioned analysis unit, We recommend the most suitable products based on the user's interests and purchasing intentions. The system according to feature 1.

5. The aforementioned supply unit is, Use chat AI to send messages recommending the most suitable products to users. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system according to feature 1.

8. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current life stage and areas of interest. The system according to feature 1.

Images