System

Abstract

To provide a system according to an embodiment that recommends optimal products on the basis of a consumption desire of a user.SOLUTION: A system according to an embodiment comprises: a reception unit; an analysis unit; a recommendation unit; and a providing unit. The reception unit is configured to input a consumption desire of a user. The analysis unit is configured to analyze information inputted by the reception unit. The recommendation unit is configured to recommend goods on the basis of a result analyzed by the analysis unit. The providing unit is configured to provide the goods recommended by the recommendation unit for the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not been sufficient in recommending products that are optimal for a user's consumption desires, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend optimal products based on the consumption desires of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and a provision unit. The reception unit inputs a user's consumption desires. The analysis unit analyzes the information input by the reception unit. The recommendation unit recommends products based on the results of the analysis by the analysis unit. The provision unit provides the user with the products recommended by the recommendation unit. Effect of the Invention

[0007] The system according to the embodiment can recommend optimal products based on the consumption desires of a user. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A recommendation system according to an embodiment of the present invention is a system that recommends optimal products for a user's high-context consumption desires through a chat-style conversation. In this recommendation system, a user inputs his / her consumption desires, preferences, budget, profile information, etc. in a chat style. Next, a generation AI analyzes the input contents and recommends products that are optimal for the user's needs. This recommendation is made taking into consideration the user's past purchase history and behavior history, as well as market trend data (SNS trend data, sales data, etc.). For example, a user inputs a specific request in chat style, such as "I want casual clothes that match the latest trends." This information is input to the generation AI. Next, the generation AI analyzes the input information. The generation AI understands the user's input contents and recommends optimal products taking into consideration the user's past purchase history, behavior history, and market trends. For example, the generation AI recommends casual clothes based on products the user has purchased or viewed in the past, as well as current market trends. Products recommended by the generation AI are presented to the user in chat style. Users can ask further questions about the presented products or request other options. For example, they can ask questions such as "Do you have this clothing in other colors?" This system allows users to easily find the products that best suit their consumption desires. Users can have a highly satisfying shopping experience because the generative AI deeply understands the user's needs and recommends the most suitable products. This allows the recommendation system to recommend and provide the most suitable products based on the user's consumption desires.

[0029] The recommendation system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and a provision unit. The reception unit inputs a consumer desire of a user. The consumer desire of a user includes, for example, a willingness to purchase, an interest in a specific product, and the like, but is not limited to such examples. The reception unit allows, for example, a user to input his / her consumer desire, preferences, budget, profile information, and the like in a chat format. The analysis unit uses a generation AI to analyze the information input by the reception unit. The analysis is performed, for example, by a method such as data mining or statistical analysis, but is not limited to such examples. For example, the generation AI understands the contents of the user's input, and recommends an optimal product in consideration of past purchase history, behavior history, and even market trends. The recommendation unit uses a generation AI to recommend a product based on the results analyzed by the analysis unit. The recommendation is performed, for example, by a method such as a recommendation algorithm or filtering technology, but is not limited to such examples. For example, the generation AI selects an optimal product based on the user's past purchase history, behavior history, and even market trend data. The providing unit provides the product recommended by the recommendation unit to the user. The provision is performed, for example, by a user interface, a notification method, or the like, but is not limited to such examples. For example, the providing unit presents the recommended product to the user in a chat format. In this way, the recommendation system according to the embodiment can recommend and provide the optimal product based on the consumption desires of the user.

[0030] The reception unit inputs the consumer desires of the user. The consumer desires of the user include, but are not limited to, for example, a willingness to buy and an interest in a specific product. The reception unit allows the user to input his / her consumer desires, preferences, budget, profile information, etc. in a chat format. Specifically, the user uses a smartphone or a PC to access a dedicated application or website and inputs his / her wishes and conditions in a chat box. For example, the user can input specific requests such as "I want a new smartphone," "My budget is within 50,000 yen," and "I want something with good camera performance." Furthermore, the user can provide more detailed information by referring to his / her past purchase history and favorite product list. The reception unit collects this information in real time and stores it in a database. The information input by the user is analyzed using natural language processing technology, and the content of the consumer desire is accurately understood. For example, if the user inputs "I want a suitcase to go on a trip," the reception unit extracts keywords such as "travel" and "suitcase" and collects information on related products. In addition, the user's profile information and past behavior history are also taken into consideration, making it possible to make more personalized recommendations. This enables the reception unit to provide basic data for responding to the diverse needs of users and recommending optimal products.

[0031] The analysis unit uses the generation AI to analyze the information input by the reception unit. The analysis is performed by, for example, data mining, statistical analysis, and other methods, but is not limited to such examples. Specifically, the generation AI understands the user's input content, and recommends optimal products by taking into account past purchase history, behavior history, and even market trends. The generation AI analyzes the user's input content using natural language processing technology and extracts keywords and phrases. For example, if a user inputs "I want new running shoes," the generation AI extracts keywords such as "running shoes" and "new" and collects information on related products. The generation AI also analyzes the user's past purchase history and behavior history to understand the user's preferences and trends. For example, the generation AI identifies products that the user is likely to like by taking into account the category, price range, and review ratings of products purchased in the past. Furthermore, the generation AI utilizes market trend data to incorporate the latest popular products and new product information. For example, if "eco-friendly products" are attracting attention as a recent trend, the generation AI can recommend eco-friendly running shoes based on that information. This enables the analysis unit to analyze the user's input from multiple angles and provide information for recommending optimal products.

[0032] The recommendation unit uses the generation AI to recommend products based on the results of the analysis by the analysis unit. The recommendation is performed by, for example, a recommendation algorithm, a filtering technique, or the like, but is not limited to such examples. Specifically, the generation AI selects the optimal product based on the user's past purchase history, behavior history, and even market trend data. The generation AI identifies the optimal product for the user using recommendation algorithms such as collaborative filtering and content-based filtering. Collaborative filtering recommends products purchased by users with similar preferences based on the behavior data of other users. For example, it is possible to recommend products that have been highly rated by users who have purchased products in the same category. Content-based filtering recommends products with similar characteristics based on the user's past purchase history and behavior history. For example, it selects products with similar characteristics by considering the brand, function, and price range of products purchased in the past. Furthermore, the generation AI utilizes market trend data that is updated in real time to incorporate the latest popular products and new product information. This allows the recommendation unit to respond to the diverse needs of users and recommend optimal products.

[0033] The providing unit provides the user with the product recommended by the recommendation unit. The provision is performed, for example, by a user interface, a notification method, or the like, but is not limited to such examples. Specifically, the providing unit presents the recommended product to the user in a chat format. The user interface is required to have an intuitive and easy-to-use design, and is designed so that the user can easily check product information. For example, an image, price, description, review rating, etc. of the recommended product are displayed in a chat box, and the user can consider purchasing based on the information. In addition, the providing unit utilizes push notification or email notification as a notification method to quickly provide information to the user. For example, when there is a new recommendation, the user can be notified by a push notification on a smartphone. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the recommendation. For example, information such as whether the user purchased the recommended product and the satisfaction level after purchase is collected, and is reflected in the next recommendation. As a result, the providing unit can quickly and reliably provide optimal product information to the user and improve the user's purchasing experience.

[0034] The recommendation system includes a collection unit that collects profile information of a user. The collection unit collects the profile information of the user. The profile information includes, for example, age, gender, hobbies, etc., but is not limited to such examples. For example, the collection unit allows the user to input his / her own profile information in a chat format. The collection unit can also automatically collect the profile information based on the user's past behavior history and social media activities. For example, the collection unit can complement the profile information based on the content the user shared on social media and information on brands the user follows. This allows the collection unit to collect the user's profile information to provide more personalized recommendations. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities to AI and cause the AI ​​to collect the profile information.

[0035] The recommendation system includes a history collection unit that collects past purchase history or behavior history. The history collection unit collects past purchase history or behavior history. The purchase history includes, for example, purchase date and time, purchased product, purchase frequency, etc., but is not limited to such examples. The behavior history includes, for example, website browsing history, click history, etc., but is not limited to such examples. The history collection unit can automatically collect, for example, products purchased or viewed by the user in the past. In addition, the history collection unit can predict consumption desires based on the user's behavior history. For example, the history collection unit can recommend related products based on products that the user frequently viewed in the past. As a result, the history collection unit can make recommendations based on the user's preferences by collecting the past purchase history and behavior history. A part or all of the above-mentioned processing in the history collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the history collection unit can input the user's behavior history into AI and cause the AI ​​to collect the history.

[0036] The recommendation system includes a trend collection unit that collects market trend data. The trend collection unit collects market trend data. The trend data includes, but is not limited to, social media trends and search engine trends. The trend collection unit can automatically collect, for example, SNS trend data and sales data. The trend collection unit can also predict consumer desires based on the user's behavior history and market trends. For example, the trend collection unit can recommend related products based on current market trends. As a result, the trend collection unit can make recommendations based on the latest trends by collecting market trend data. A part or all of the above-mentioned processing in the trend collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the trend collection unit can input SNS trend data to AI and cause AI to collect trend data.

[0037] The analysis unit can analyze the user's needs based on the collected data. The analysis unit uses the generation AI to analyze the user's needs based on the collected data. The needs include, for example, the user's requests, expectations, and wishes, but are not limited to such examples. The analysis unit can analyze the user's needs based on, for example, the user's profile information, past purchase history, behavior history, market trend data, and the like. For example, the generation AI can analyze the user's past purchase history and behavior history, and predict the user's needs based on the current market trend. This allows the analysis unit to analyze the user's needs based on the collected data, thereby enabling more accurate recommendations. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's profile information and behavior history into the generation AI and have the generation AI execute the analysis of the needs.

[0038] The recommendation unit can select an appropriate product based on the analysis result. The recommendation unit uses the generation AI to select an appropriate product based on the analysis result. The appropriate product includes, for example, the user's preferences, past purchase history, etc., but is not limited to such examples. The recommendation unit can select an optimal product based on, for example, the user's past purchase history and behavior history, and further market trend data. For example, the generation AI can analyze the user's past purchase history and behavior history and select an optimal product based on the current market trend. This allows the recommendation unit to recommend a product that meets the user's needs by selecting an optimal product based on the analysis result. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input the analysis result to the generation AI and cause the generation AI to select a product.

[0039] The providing unit can present the recommended product to the user. The providing unit presents the recommended product to the user. The presentation includes, for example, a user interface, a notification method, and the like, but is not limited to such examples. The providing unit can present the recommended product to the user in a chat format, for example. In addition, the providing unit can allow the user to ask further questions about the recommended product or request other options. For example, the providing unit enables the user to ask a question such as "Do you have this outfit in a different color?". This makes it easier for the user to select a product by presenting the recommended product to the user. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the recommended product to AI and cause the AI ​​to execute the presentation of the product.

[0040] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays consumption desires and preferences that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest consumption desires to be used in a specific time period from the user's past input history. In this way, the reception unit can suggest the optimal input method to the user by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to AI and have the AI ​​execute the suggestion of the optimal input method.

[0041] The reception unit can customize the input prompt based on the user's current environment. For example, when the user is using the app at night, the reception unit provides a night mode interface. In addition, when the user is in a specific location (e.g., a shopping mall), the reception unit can also suggest consumption desires related to the location. Furthermore, when the user is using the app during a specific time period (e.g., lunch time), the reception unit can also suggest consumption desires related to the time period. This allows the reception unit to customize the input prompt based on the user's current environment, thereby enabling more appropriate input. Part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's current environmental data to AI and cause AI to customize the input prompt.

[0042] The reception unit can provide an input prompt corresponding to a region-specific consumption desire in consideration of the geographical location information of the user. For example, when the user is in a specific region, the reception unit suggests products and services that are popular in that region. In addition, when the user is traveling, the reception unit can also provide an input prompt corresponding to a consumption desire at the travel destination. Furthermore, when the user is participating in a specific event (e.g., a festival), the reception unit can also suggest a consumption desire related to the event. In this way, the reception unit can provide an input corresponding to a region-specific consumption desire by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to provide an input prompt.

[0043] The reception unit can analyze the user's social media activity and automatically complete the related input contents. For example, the reception unit automatically completes the consumption desires based on the content shared by the user on social media. The reception unit can also suggest related consumption desires based on information on brands and influencers the user follows on social media. Furthermore, the reception unit can automatically complete related consumption desires based on posts that the user has "liked" on social media. This allows the reception unit to automatically complete the related input contents by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI and have the AI ​​perform automatic completion of the input contents.

[0044] The analysis unit can improve the analysis accuracy by combining the user's past purchase history with the current market trend during the analysis. The analysis unit uses the generation AI to improve the analysis accuracy by combining the user's past purchase history with the current market trend during the analysis. The purchase history includes, for example, purchase date and time, purchased product, purchase frequency, etc., but is not limited to such examples. The market trend includes, for example, social media trends, search engine trends, etc., but is not limited to such examples. The analysis unit can analyze a product that matches the current market trend based on the user's past purchase history, for example. The analysis unit can also combine the user's past purchase history with market trend data to analyze an optimal product. Furthermore, the analysis unit can analyze the user's past purchase history and analyze the product based on the current market trend. As a result, the analysis unit improves the analysis accuracy by combining the user's past purchase history with the market trend. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's purchase history and market trend data to the generation AI and cause the generation AI to execute the improvement of the analysis accuracy.

[0045] The analysis unit can personalize the analysis result by considering the profile information of the user at the time of analysis. The analysis unit uses the generation AI to personalize the analysis result by considering the profile information of the user at the time of analysis. The profile information includes, for example, age, gender, hobbies, etc., but is not limited to such examples. The analysis unit can analyze the optimal product by considering, for example, the age and gender of the user. In addition, the analysis unit can analyze related products by considering the hobbies and interests of the user. Furthermore, the analysis unit can analyze the optimal product by considering the budget of the user. As a result, the analysis unit can personalize the analysis result by considering the profile information of the user. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the profile information of the user to the generation AI and cause the generation AI to execute personalization of the analysis result.

[0046] The analysis unit can reflect a trend specific to a region by considering the geographical location information of the user at the time of analysis. The analysis unit uses the generation AI to reflect a trend specific to a region by considering the geographical location information of the user at the time of analysis. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The trend specific to a region includes, for example, a culture of the region, a seasonal event, etc., but is not limited to such examples. For example, when the user is in a specific region, the analysis unit can analyze products that are popular in that region. In addition, when the user is traveling, the analysis unit can also analyze products that reflect trends at the travel destination. Furthermore, when the user is participating in a specific event (for example, a festival), the analysis unit can analyze products related to the event. This enables the analysis unit to reflect a trend specific to a region by considering the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and cause the generation AI to reflect the trend specific to the region.

[0047] The analysis unit can complement the analysis result by referring to the social media activity of the user during the analysis. The analysis unit uses the generation AI to complement the analysis result by referring to the social media activity of the user during the analysis. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The analysis unit can complement the analysis result, for example, based on the content shared by the user on social media. The analysis unit can also complement the analysis result based on information on brands and influencers that the user follows on social media. Furthermore, the analysis unit can complement the analysis result based on posts that the user "liked" on social media. Thereby, the analysis unit can complement the analysis result by referring to the social media activity of the user. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the social media activity of the user to the generation AI and cause the generation AI to execute the complement of the analysis result.

[0048] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the product at the time of recommendation. The recommendation unit adjusts the level of detail of the recommendation based on the importance of the product using the generation AI at the time of recommendation. The importance of the product includes, for example, the user's preferences, past purchase history, etc., but is not limited to such examples. The recommendation unit can provide detailed information for important products, for example. The recommendation unit can also provide concise information for products with low importance. Furthermore, the recommendation unit can dynamically adjust the level of detail of the recommendation according to the importance of the product. As a result, the recommendation unit can provide information that is important to the user preferentially by adjusting the level of detail of the recommendation based on the importance of the product. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input the importance data of the product to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0049] The recommendation unit can apply different recommendation algorithms depending on the category of the product when making a recommendation. The recommendation unit uses the generation AI to apply different recommendation algorithms depending on the category of the product when making a recommendation. The product categories include, for example, electronic devices, clothing, and food, but are not limited to such examples. The recommendation algorithms include, for example, collaborative filtering, content-based filtering, and the like, but are not limited to such examples. For example, the recommendation unit can apply a recommendation algorithm that emphasizes trend data to fashion products. In addition, the recommendation unit can also apply a recommendation algorithm that emphasizes technical specifications to electronic devices. Furthermore, the recommendation unit can apply a recommendation algorithm that emphasizes the user's taste preferences to food. This allows the recommendation unit to make more accurate recommendations by applying the optimal recommendation algorithm depending on the category of the product. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input product category data into the generation AI and have the generation AI apply a recommendation algorithm.

[0050] The recommendation unit can determine the priority order of the recommendation based on the submission time of the product at the time of recommendation. The recommendation unit uses the generation AI to determine the priority order of the recommendation based on the submission time of the product at the time of recommendation. The submission time of the product includes, for example, a release date, a seasonal product, etc., but is not limited to such examples. The recommendation unit can, for example, preferentially recommend new products. In addition, the recommendation unit can also recommend seasonal products according to the season. Furthermore, the recommendation unit can preferentially recommend sale products during a sale period. This allows the recommendation unit to determine the priority order of the recommendation based on the submission time of the product, thereby enabling timely recommendation. Part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input product submission time data to the generation AI and cause the generation AI to execute the determination of the priority order of the recommendation.

[0051] The recommendation unit can adjust the order of recommendations based on the relevance of products when making a recommendation. The recommendation unit adjusts the order of recommendations based on the relevance of products using the generation AI when making a recommendation. The relevance of products includes, for example, the same category, related functions, and the like, but is not limited to such examples. The recommendation unit can, for example, preferentially recommend products that are most relevant to the user's consumption desire. The recommendation unit can also postpone products with low relevance. Furthermore, the recommendation unit can dynamically adjust the order of recommendations according to the relevance of products. Thereby, the recommendation unit can provide the most relevant product to the user with priority by adjusting the order of recommendations based on the relevance of products. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input product relevance data to the generation AI and cause the generation AI to adjust the order of recommendations.

[0052] The providing unit can select the optimal display method by referring to the user's past purchase history at the time of providing. The providing unit selects the optimal display method by referring to the user's past purchase history at the time of providing. The purchase history includes, for example, purchase date and time, purchased product, purchase frequency, etc., but is not limited to such examples. The providing unit can, for example, preferentially display products similar to products purchased by the user in the past. The providing unit can also display related products based on the user's past purchase history. Furthermore, the providing unit can analyze the user's past purchase history and select the optimal display method. Thereby, the providing unit can display related products in the optimal manner by referring to the user's past purchase history. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's purchase history data to AI and cause the AI ​​to select the display method.

[0053] The providing unit can customize the display of the product based on the user's current environment at the time of providing. The providing unit customizes the display of the product based on the user's current environment at the time of providing. The current environment includes, for example, the type of device, the place of use, the time period, etc., but is not limited to such examples. For example, when the user uses the app at night, the providing unit can provide a display method of a night mode. In addition, when the user is in a specific place (e.g., a shopping mall), the providing unit can also display products related to the place. Furthermore, when the user uses the app during a specific time period (e.g., lunch time), the providing unit can also display products related to the time period. Thereby, the providing unit customizes the display of the product based on the user's current environment, thereby enabling more appropriate display. Part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's current environmental data to AI and cause AI to execute customization of the display of the product.

[0054] The providing unit can provide the optimal product by considering the geographical location information of the user at the time of providing. The providing unit can provide the optimal product by considering the geographical location information of the user at the time of providing. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the providing unit can provide a product that is popular in that area. In addition, when the user is traveling, the providing unit can also provide a product that corresponds to the consumption desire at the travel destination. Furthermore, when the user is participating in a specific event (for example, a festival), the providing unit can also provide a product related to the event. In this way, the providing unit can provide the optimal product specific to the area by considering the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to execute the provision of the product.

[0055] The providing unit can provide related products by analyzing the social media activity of the user at the time of providing. The providing unit can provide related products by analyzing the social media activity of the user at the time of providing. Social media activities include, for example, the content of posts, the number of likes, the number of followers, etc., but are not limited to such examples. The providing unit can provide related products based on the content shared by the user on social media, for example. The providing unit can also provide related products based on information on brands and influencers that the user follows on social media. Furthermore, the providing unit can also provide related products based on posts that the user "liked" on social media. Thereby, the providing unit can provide related products by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the social media activity of the user to AI and cause the AI ​​to provide the product.

[0056] The collection unit can select the optimal collection method by referring to the user's past behavioral history at the time of collection. The collection unit selects the optimal collection method by referring to the user's past behavioral history at the time of collection. The behavioral history includes, for example, past input contents, input frequency, etc., but is not limited to such examples. The collection unit can collect related profile information, for example, based on the user's past behavioral history. The collection unit can also analyze the user's past behavioral history and select the optimal collection method. Furthermore, the collection unit can also determine the priority of information to be collected by referring to the user's past behavioral history. Thereby, the collection unit can collect related profile information in an optimal manner by referring to the user's past behavioral history. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral history data to AI and cause the AI ​​to select the collection method.

[0057] The collection unit can collect optimal profile information in consideration of the geographical location information of the user at the time of collection. The collection unit collects optimal profile information in consideration of the geographical location information of the user at the time of collection. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the collection unit can collect profile information related to the area. In addition, when the user is traveling, the collection unit can also collect profile information at the travel destination. Furthermore, when the user is participating in a specific event (e.g., a festival), the collection unit can also collect profile information related to the event. In this way, the collection unit can collect region-specific profile information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and cause the AI ​​to execute collection of profile information.

[0058] The history collection unit can select the optimal collection method by referring to the user's past purchase history at the time of collection. The history collection unit can select the optimal collection method by referring to the user's past purchase history at the time of collection. The purchase history includes, for example, purchase date and time, purchased product, purchase frequency, etc., but is not limited to such examples. The history collection unit can collect related history information based on, for example, the user's past purchase history. In addition, the history collection unit can analyze the user's past purchase history and select the optimal collection method. Furthermore, the history collection unit can determine the priority of information to be collected by referring to the user's past purchase history. Thereby, the history collection unit can collect related history information in an optimal manner by referring to the user's past purchase history. A part or all of the above-mentioned processing in the history collection unit may be performed using AI, or may be performed without using AI. For example, the history collection unit can input the user's purchase history data to AI and cause the AI ​​to select the collection method.

[0059] The history collection unit can collect an optimal history by considering the geographical location information of the user at the time of collection. The history collection unit collects an optimal history by considering the geographical location information of the user at the time of collection. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the history collection unit can collect history information related to the area. In addition, when the user is traveling, the history collection unit can also collect history information at the travel destination. Furthermore, when the user is participating in a specific event (for example, a festival), the history collection unit can also collect history information related to the event. In this way, the history collection unit can collect area-specific history information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the history collection unit may be performed using AI, or may be performed without using AI. For example, the history collection unit can input the geographical location information of the user to AI and cause the AI ​​to execute the collection of history.

[0060] The trend collection unit can select the optimal collection method by referring to past trend data at the time of collection. The trend collection unit selects the optimal collection method by referring to past trend data at the time of collection. Trend data includes, for example, social media trends, search engine trends, and the like, but is not limited to such examples. The trend collection unit can collect current trends, for example, based on past trend data. In addition, the trend collection unit can analyze past trend data and select the optimal collection method. Furthermore, the trend collection unit can determine the priority order of trend data to be collected by referring to past trend data. Thereby, the trend collection unit can collect current trends in an optimal manner by referring to past trend data. A part or all of the above-mentioned processing in the trend collection unit may be performed using AI, or may be performed without using AI. For example, the trend collection unit can input past trend data to AI and cause AI to select a collection method.

[0061] The trend collecting unit can collect optimal trend data in consideration of the geographical location information of the user at the time of collection. The trend collecting unit collects optimal trend data in consideration of the geographical location information of the user at the time of collection. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the trend collecting unit can collect trend data related to the area. In addition, when the user is traveling, the trend collecting unit can also collect trend data at the travel destination. Furthermore, when the user is participating in a specific event (for example, a festival), the trend collecting unit can also collect trend data related to the event. In this way, the trend collecting unit can collect trend data specific to the area by considering the geographical location information of the user. A part or all of the above-mentioned processing in the trend collecting unit may be performed using AI, or may be performed without using AI. For example, the trend collecting unit can input the geographical location information of the user to AI and cause the AI ​​to collect trend data.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The recommendation system may further include a feedback collection unit that collects real-time feedback from users. The feedback collection unit enables users to input ratings and comments on recommended products in real time. For example, the user may provide positive feedback such as "this product matches my taste" for the recommended product. The user may also provide negative feedback such as "this product is over my budget." In this way, the feedback collection unit collects real-time feedback from users, allowing the recommendation system to respond to the needs of users more quickly. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI or may be performed without using AI. For example, the feedback collection unit may input user feedback data to AI and cause AI to adjust the recommendations.

[0064] The collection unit can further collect health data of the user. The health data includes, for example, the number of steps, heart rate, sleep patterns, and the like, but is not limited to such examples. For example, when the user uses a wearable device, the collection unit can automatically collect health data from the device. The collection unit can also grasp the health condition based on data input by the user to a health app. As a result, the collection unit can make recommendations according to the health condition by collecting the user's health data. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's health data to AI and cause the AI ​​to adjust the recommendations.

[0065] The history collection unit can further include a willingness estimation function for predicting the user's willingness to purchase. The willingness estimation function predicts the current willingness to purchase based on the user's past purchase history and behavior history. For example, if the user has frequently purchased products of a specific brand in the past, it can be estimated that the user has a high willingness to purchase products of that brand. In addition, if the user has a tendency to purchase specific products in a specific season, it can also predict the willingness to purchase in that season. This allows the history collection unit to predict the user's willingness to purchase, thereby enabling more accurate recommendations. Some or all of the above-mentioned processing in the willingness estimation function may be performed using AI, or may be performed without using AI. For example, the history collection unit can input the user's behavior history data into AI and have the AI ​​execute the prediction of the willingness to purchase.

[0066] The trend collecting unit can further collect trend data by taking into consideration the user's influence on social media. Influence includes, for example, the number of followers, the engagement rate of posts, and the like, but is not limited to such examples. For example, if the user is an influencer with many followers, the trend collecting unit can collect trend data by focusing on the content of the user's posts. In addition, if the user has high engagement in a specific topic, the trend collecting unit can also collect trend data related to the topic with priority. In this way, the trend collecting unit can collect more relevant trend data by taking into consideration the user's influence. A part or all of the above-mentioned processing in the evaluation of influence may be performed using AI, or may be performed without using AI. For example, the trend collecting unit can input the user's social media data into AI and cause the AI ​​to perform the evaluation of influence.

[0067] The analysis unit can further perform the analysis taking into consideration the user's life events. Life events include, for example, marriage, moving, childbirth, and the like, but are not limited to such examples. For example, if the user is about to get married, the analysis unit can prioritize analyzing products related to the wedding. Also, if the user is planning to move, the analysis unit can analyze products related to the new house. Furthermore, if the user is planning to give birth, the analysis unit can prioritize analyzing baby products. This allows the analysis unit to make more personalized recommendations by taking into consideration the user's life events. Some or all of the above-mentioned processing in grasping the life events may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's life event data into AI and cause the AI ​​to adjust the analysis.

[0068] The process flow of the first embodiment will be briefly described below.

[0069] Step 1: The reception unit inputs the user's consumption desires. The user's consumption desires include willingness to buy, interest in specific products, etc. The reception unit allows the user to input their own consumption desires, preferences, budget, profile information, etc. in a chat format. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis is performed using methods such as data mining and statistical analysis. The generation AI understands the user's input and generates data to recommend optimal products, taking into account past purchase history, behavioral history, and even market trends. Step 3: The recommendation unit uses the generation AI to recommend products based on the results of the analysis by the analysis unit. Recommendations are made using methods such as recommendation algorithms and filtering techniques. The generation AI selects the most suitable products based on the user's past purchase history, behavioral history, and market trend data. Step 4: The providing unit provides the product recommended by the recommendation unit to the user. The product is provided by a user interface, a notification method, or the like. For example, the providing unit presents the recommended product to the user in a chat format.

[0070] (Example 2) A recommendation system according to an embodiment of the present invention is a system that recommends optimal products for a user's high-context consumption desires through a chat-style conversation. In this recommendation system, a user inputs his / her consumption desires, preferences, budget, profile information, etc. in a chat style. Next, a generation AI analyzes the input contents and recommends products that are optimal for the user's needs. This recommendation is made taking into consideration the user's past purchase history and behavior history, as well as market trend data (SNS trend data, sales data, etc.). For example, a user inputs a specific request in chat style, such as "I want casual clothes that match the latest trends." This information is input to the generation AI. Next, the generation AI analyzes the input information. The generation AI understands the user's input contents and recommends optimal products taking into consideration the user's past purchase history, behavior history, and market trends. For example, the generation AI recommends casual clothes based on products the user has purchased or viewed in the past, as well as current market trends. Products recommended by the generation AI are presented to the user in chat style. Users can ask further questions about the presented products or request other options. For example, they can ask questions such as "Do you have this clothing in other colors?" This system allows users to easily find the products that best suit their consumption desires. Users can have a highly satisfying shopping experience because the generative AI deeply understands the user's needs and recommends the most suitable products. This allows the recommendation system to recommend and provide the most suitable products based on the user's consumption desires.

[0071] The recommendation system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and a provision unit. The reception unit inputs a consumer desire of a user. The consumer desire of a user includes, for example, a willingness to purchase, an interest in a specific product, and the like, but is not limited to such examples. The reception unit allows, for example, a user to input his / her consumer desire, preferences, budget, profile information, and the like in a chat format. The analysis unit uses a generation AI to analyze the information input by the reception unit. The analysis is performed, for example, by a method such as data mining or statistical analysis, but is not limited to such examples. For example, the generation AI understands the contents of the user's input, and recommends an optimal product in consideration of past purchase history, behavior history, and even market trends. The recommendation unit uses a generation AI to recommend a product based on the results analyzed by the analysis unit. The recommendation is performed, for example, by a method such as a recommendation algorithm or filtering technology, but is not limited to such examples. For example, the generation AI selects an optimal product based on the user's past purchase history, behavior history, and even market trend data. The providing unit provides the product recommended by the recommendation unit to the user. The provision is performed, for example, by a user interface, a notification method, or the like, but is not limited to such examples. For example, the providing unit presents the recommended product to the user in a chat format. In this way, the recommendation system according to the embodiment can recommend and provide the optimal product based on the consumption desires of the user.

[0072] The reception unit inputs the consumer desires of the user. The consumer desires of the user include, but are not limited to, for example, a willingness to buy and an interest in a specific product. The reception unit allows the user to input his / her consumer desires, preferences, budget, profile information, etc. in a chat format. Specifically, the user uses a smartphone or a PC to access a dedicated application or website and inputs his / her wishes and conditions in a chat box. For example, the user can input specific requests such as "I want a new smartphone," "My budget is within 50,000 yen," and "I want something with good camera performance." Furthermore, the user can provide more detailed information by referring to his / her past purchase history and favorite product list. The reception unit collects this information in real time and stores it in a database. The information input by the user is analyzed using natural language processing technology, and the content of the consumer desire is accurately understood. For example, if the user inputs "I want a suitcase to go on a trip," the reception unit extracts keywords such as "travel" and "suitcase" and collects information on related products. In addition, the user's profile information and past behavior history are also taken into consideration, making it possible to make more personalized recommendations. This enables the reception unit to provide basic data for responding to the diverse needs of users and recommending optimal products.

[0073] The analysis unit uses the generation AI to analyze the information input by the reception unit. The analysis is performed by, for example, data mining, statistical analysis, and other methods, but is not limited to such examples. Specifically, the generation AI understands the user's input content, and recommends optimal products by taking into account past purchase history, behavior history, and even market trends. The generation AI analyzes the user's input content using natural language processing technology and extracts keywords and phrases. For example, if a user inputs "I want new running shoes," the generation AI extracts keywords such as "running shoes" and "new" and collects information on related products. The generation AI also analyzes the user's past purchase history and behavior history to understand the user's preferences and trends. For example, the generation AI identifies products that the user is likely to like by taking into account the category, price range, and review ratings of products purchased in the past. Furthermore, the generation AI utilizes market trend data to incorporate the latest popular products and new product information. For example, if "eco-friendly products" are attracting attention as a recent trend, the generation AI can recommend eco-friendly running shoes based on that information. This enables the analysis unit to analyze the user's input from multiple angles and provide information for recommending optimal products.

[0074] The recommendation unit uses the generation AI to recommend products based on the results of the analysis by the analysis unit. The recommendation is performed by, for example, a recommendation algorithm, a filtering technique, or the like, but is not limited to such examples. Specifically, the generation AI selects the optimal product based on the user's past purchase history, behavior history, and even market trend data. The generation AI identifies the optimal product for the user using recommendation algorithms such as collaborative filtering and content-based filtering. Collaborative filtering recommends products purchased by users with similar preferences based on the behavior data of other users. For example, it is possible to recommend products that have been highly rated by users who have purchased products in the same category. Content-based filtering recommends products with similar characteristics based on the user's past purchase history and behavior history. For example, it selects products with similar characteristics by considering the brand, function, and price range of products purchased in the past. Furthermore, the generation AI utilizes market trend data that is updated in real time to incorporate the latest popular products and new product information. This allows the recommendation unit to respond to the diverse needs of users and recommend optimal products.

[0075] The providing unit provides the user with the product recommended by the recommendation unit. The provision is performed, for example, by a user interface, a notification method, or the like, but is not limited to such examples. Specifically, the providing unit presents the recommended product to the user in a chat format. The user interface is required to have an intuitive and easy-to-use design, and is designed so that the user can easily check product information. For example, an image, price, description, review rating, etc. of the recommended product are displayed in a chat box, and the user can consider purchasing based on the information. In addition, the providing unit utilizes push notification or email notification as a notification method to quickly provide information to the user. For example, when there is a new recommendation, the user can be notified by a push notification on a smartphone. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the recommendation. For example, information such as whether the user purchased the recommended product and the satisfaction level after purchase is collected, and is reflected in the next recommendation. As a result, the providing unit can quickly and reliably provide optimal product information to the user and improve the user's purchasing experience.

[0076] The recommendation system includes a collection unit that collects profile information of a user. The collection unit collects the profile information of the user. The profile information includes, for example, age, gender, hobbies, etc., but is not limited to such examples. For example, the collection unit allows the user to input his / her own profile information in a chat format. The collection unit can also automatically collect the profile information based on the user's past behavior history and social media activities. For example, the collection unit can complement the profile information based on the content the user shared on social media and information on brands the user follows. This allows the collection unit to collect the user's profile information to provide more personalized recommendations. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities to AI and cause the AI ​​to collect the profile information.

[0077] The recommendation system includes a history collection unit that collects past purchase history or behavior history. The history collection unit collects past purchase history or behavior history. The purchase history includes, for example, purchase date and time, purchased product, purchase frequency, etc., but is not limited to such examples. The behavior history includes, for example, website browsing history, click history, etc., but is not limited to such examples. The history collection unit can automatically collect, for example, products purchased or viewed by the user in the past. In addition, the history collection unit can predict consumption desires based on the user's behavior history. For example, the history collection unit can recommend related products based on products that the user frequently viewed in the past. As a result, the history collection unit can make recommendations based on the user's preferences by collecting the past purchase history and behavior history. A part or all of the above-mentioned processing in the history collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the history collection unit can input the user's behavior history into AI and cause the AI ​​to collect the history.

[0078] The recommendation system includes a trend collection unit that collects market trend data. The trend collection unit collects market trend data. The trend data includes, but is not limited to, social media trends and search engine trends. The trend collection unit can automatically collect, for example, SNS trend data and sales data. The trend collection unit can also predict consumer desires based on the user's behavior history and market trends. For example, the trend collection unit can recommend related products based on current market trends. As a result, the trend collection unit can make recommendations based on the latest trends by collecting market trend data. A part or all of the above-mentioned processing in the trend collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the trend collection unit can input SNS trend data to AI and cause AI to collect trend data.

[0079] The analysis unit can analyze the user's needs based on the collected data. The analysis unit uses the generation AI to analyze the user's needs based on the collected data. The needs include, for example, the user's requests, expectations, and wishes, but are not limited to such examples. The analysis unit can analyze the user's needs based on, for example, the user's profile information, past purchase history, behavior history, market trend data, and the like. For example, the generation AI can analyze the user's past purchase history and behavior history, and predict the user's needs based on the current market trend. This allows the analysis unit to analyze the user's needs based on the collected data, thereby enabling more accurate recommendations. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's profile information and behavior history into the generation AI and have the generation AI execute the analysis of the needs.

[0080] The recommendation unit can select an appropriate product based on the analysis result. The recommendation unit uses the generation AI to select an appropriate product based on the analysis result. The appropriate product includes, for example, the user's preferences, past purchase history, etc., but is not limited to such examples. The recommendation unit can select an optimal product based on, for example, the user's past purchase history and behavior history, and further market trend data. For example, the generation AI can analyze the user's past purchase history and behavior history and select an optimal product based on the current market trend. This allows the recommendation unit to recommend a product that meets the user's needs by selecting an optimal product based on the analysis result. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input the analysis result to the generation AI and cause the generation AI to select a product.

[0081] The providing unit can present the recommended product to the user. The providing unit presents the recommended product to the user. The presentation includes, for example, a user interface, a notification method, and the like, but is not limited to such examples. The providing unit can present the recommended product to the user in a chat format, for example. In addition, the providing unit can allow the user to ask further questions about the recommended product or request other options. For example, the providing unit enables the user to ask a question such as "Do you have this outfit in a different color?". This makes it easier for the user to select a product by presenting the recommended product to the user. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the recommended product to AI and cause the AI ​​to execute the presentation of the product.

[0082] The reception unit can estimate the user's emotion and dynamically change the design of the input interface based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple and intuitive interface and minimizes input procedures. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input consumption desires. This improves the user's input experience by changing the design of the input interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute a design change of the input interface.

[0083] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays consumption desires and preferences that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest consumption desires to be used in a specific time period from the user's past input history. In this way, the reception unit can suggest the optimal input method to the user by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to AI and have the AI ​​execute the suggestion of the optimal input method.

[0084] The reception unit can customize the input prompt based on the user's current environment. For example, when the user is using the app at night, the reception unit provides a night mode interface. In addition, when the user is in a specific location (e.g., a shopping mall), the reception unit can also suggest consumption desires related to the location. Furthermore, when the user is using the app during a specific time period (e.g., lunch time), the reception unit can also suggest consumption desires related to the time period. This allows the reception unit to customize the input prompt based on the user's current environment, thereby enabling more appropriate input. Part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's current environmental data to AI and cause AI to customize the input prompt.

[0085] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit preferentially displays important input items and simplifies the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input items and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can preferentially display the most important input items to enable quick input. In this way, the reception unit can prioritize input contents according to the user's emotion, thereby allowing important information to be input preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the input contents.

[0086] The reception unit can provide an input prompt corresponding to a region-specific consumption desire in consideration of the geographical location information of the user. For example, when the user is in a specific region, the reception unit suggests products and services that are popular in that region. In addition, when the user is traveling, the reception unit can also provide an input prompt corresponding to a consumption desire at the travel destination. Furthermore, when the user is participating in a specific event (e.g., a festival), the reception unit can also suggest a consumption desire related to the event. In this way, the reception unit can provide an input corresponding to a region-specific consumption desire by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to provide an input prompt.

[0087] The reception unit can analyze the user's social media activity and automatically complete the related input contents. For example, the reception unit automatically completes the consumption desires based on the content shared by the user on social media. The reception unit can also suggest related consumption desires based on information on brands and influencers the user follows on social media. Furthermore, the reception unit can automatically complete related consumption desires based on posts that the user has "liked" on social media. This allows the reception unit to automatically complete the related input contents by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI and have the AI ​​perform automatic completion of the input contents.

[0088] The analysis unit can estimate the user's emotion and dynamically adjust the analysis algorithm based on the estimated user's emotion. For example, when the user is stressed, the analysis unit uses a simple analysis algorithm to quickly provide a result. In addition, when the user is relaxed, the analysis unit can also use a detailed analysis algorithm to provide a highly accurate result. Furthermore, when the user is in a hurry, the analysis unit can also use a quick analysis algorithm to provide a result in a short time. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the analysis algorithm according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0089] The analysis unit can improve the analysis accuracy by combining the user's past purchase history with the current market trend during the analysis. The analysis unit uses the generation AI to improve the analysis accuracy by combining the user's past purchase history with the current market trend during the analysis. The purchase history includes, for example, purchase date and time, purchased product, purchase frequency, etc., but is not limited to such examples. The market trend includes, for example, social media trends, search engine trends, etc., but is not limited to such examples. The analysis unit can analyze a product that matches the current market trend based on the user's past purchase history, for example. The analysis unit can also combine the user's past purchase history with market trend data to analyze an optimal product. Furthermore, the analysis unit can analyze the user's past purchase history and analyze the product based on the current market trend. As a result, the analysis unit improves the analysis accuracy by combining the user's past purchase history with the market trend. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's purchase history and market trend data to the generation AI and cause the generation AI to execute the improvement of the analysis accuracy.

[0090] The analysis unit can personalize the analysis result by considering the profile information of the user at the time of analysis. The analysis unit uses the generation AI to personalize the analysis result by considering the profile information of the user at the time of analysis. The profile information includes, for example, age, gender, hobbies, etc., but is not limited to such examples. The analysis unit can analyze the optimal product by considering, for example, the age and gender of the user. In addition, the analysis unit can analyze related products by considering the hobbies and interests of the user. Furthermore, the analysis unit can analyze the optimal product by considering the budget of the user. As a result, the analysis unit can personalize the analysis result by considering the profile information of the user. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the profile information of the user to the generation AI and cause the generation AI to execute personalization of the analysis result.

[0091] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is stressed, the analysis unit provides a simple and highly visible display method. In addition, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. As a result, the analysis unit can adjust the display method of the analysis result according to the user's emotion, thereby enabling a display that is easy for the user to see. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0092] The analysis unit can reflect a trend specific to a region by considering the geographical location information of the user at the time of analysis. The analysis unit uses the generation AI to reflect a trend specific to a region by considering the geographical location information of the user at the time of analysis. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. The trend specific to a region includes, for example, a culture of the region, a seasonal event, etc., but is not limited to such examples. For example, when the user is in a specific region, the analysis unit can analyze products that are popular in that region. In addition, when the user is traveling, the analysis unit can also analyze products that reflect trends at the travel destination. Furthermore, when the user is participating in a specific event (for example, a festival), the analysis unit can analyze products related to the event. This enables the analysis unit to reflect a trend specific to a region by considering the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and cause the generation AI to reflect the trend specific to the region.

[0093] The analysis unit can complement the analysis result by referring to the social media activity of the user during the analysis. The analysis unit uses the generation AI to complement the analysis result by referring to the social media activity of the user during the analysis. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The analysis unit can complement the analysis result, for example, based on the content shared by the user on social media. The analysis unit can also complement the analysis result based on information on brands and influencers that the user follows on social media. Furthermore, the analysis unit can complement the analysis result based on posts that the user "liked" on social media. Thereby, the analysis unit can complement the analysis result by referring to the social media activity of the user. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the social media activity of the user to the generation AI and cause the generation AI to execute the complement of the analysis result.

[0094] The recommendation unit can estimate the user's emotion and adjust the way of expressing the recommendation based on the estimated user's emotion. For example, when the user is feeling stressed, the recommendation unit provides a simple and highly visible recommendation. In addition, when the user is relaxed, the recommendation unit can also provide a recommendation including detailed information. Furthermore, when the user is in a hurry, the recommendation unit can also provide a recommendation that covers the main points. As a result, the recommendation unit can adjust the way of expressing the recommendation according to the user's emotion, thereby enabling a recommendation that is easy for the user to see. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way of expressing the recommendation.

[0095] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the product at the time of recommendation. The recommendation unit adjusts the level of detail of the recommendation based on the importance of the product using the generation AI at the time of recommendation. The importance of the product includes, for example, the user's preferences, past purchase history, etc., but is not limited to such examples. The recommendation unit can provide detailed information for important products, for example. The recommendation unit can also provide concise information for products with low importance. Furthermore, the recommendation unit can dynamically adjust the level of detail of the recommendation according to the importance of the product. As a result, the recommendation unit can provide information that is important to the user preferentially by adjusting the level of detail of the recommendation based on the importance of the product. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input the importance data of the product to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0096] The recommendation unit can apply different recommendation algorithms depending on the category of the product when making a recommendation. The recommendation unit uses the generation AI to apply different recommendation algorithms depending on the category of the product when making a recommendation. The product categories include, for example, electronic devices, clothing, and food, but are not limited to such examples. The recommendation algorithms include, for example, collaborative filtering, content-based filtering, and the like, but are not limited to such examples. For example, the recommendation unit can apply a recommendation algorithm that emphasizes trend data to fashion products. In addition, the recommendation unit can also apply a recommendation algorithm that emphasizes technical specifications to electronic devices. Furthermore, the recommendation unit can apply a recommendation algorithm that emphasizes the user's taste preferences to food. This allows the recommendation unit to make more accurate recommendations by applying the optimal recommendation algorithm depending on the category of the product. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input product category data into the generation AI and have the generation AI apply a recommendation algorithm.

[0097] The recommendation unit can estimate the user's emotion and adjust the length of the recommendation based on the estimated user's emotion. For example, when the user is stressed, the recommendation unit provides a short recommendation that is to the point. In addition, when the user is relaxed, the recommendation unit can also provide a longer recommendation that includes detailed information. In addition, when the user is in a hurry, the recommendation unit can also provide a short recommendation that can be quickly understood. In this way, the recommendation unit can provide an appropriate amount of information for the user by adjusting the length of the recommendation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the recommendation.

[0098] The recommendation unit can determine the priority order of the recommendation based on the submission time of the product at the time of recommendation. The recommendation unit uses the generation AI to determine the priority order of the recommendation based on the submission time of the product at the time of recommendation. The submission time of the product includes, for example, a release date, a seasonal product, etc., but is not limited to such examples. The recommendation unit can, for example, preferentially recommend new products. In addition, the recommendation unit can also recommend seasonal products according to the season. Furthermore, the recommendation unit can preferentially recommend sale products during a sale period. This allows the recommendation unit to determine the priority order of the recommendation based on the submission time of the product, thereby enabling timely recommendation. Part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input product submission time data to the generation AI and cause the generation AI to execute the determination of the priority order of the recommendation.

[0099] The recommendation unit can adjust the order of recommendations based on the relevance of products when making a recommendation. The recommendation unit adjusts the order of recommendations based on the relevance of products using the generation AI when making a recommendation. The relevance of products includes, for example, the same category, related functions, and the like, but is not limited to such examples. The recommendation unit can, for example, preferentially recommend products that are most relevant to the user's consumption desire. The recommendation unit can also postpone products with low relevance. Furthermore, the recommendation unit can dynamically adjust the order of recommendations according to the relevance of products. Thereby, the recommendation unit can provide the most relevant product to the user with priority by adjusting the order of recommendations based on the relevance of products. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input product relevance data to the generation AI and cause the generation AI to adjust the order of recommendations.

[0100] The providing unit can estimate the user's emotion and adjust the display method of the product to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. As a result, the providing unit can adjust the display method of the product according to the user's emotion, thereby enabling a display that is easy for the user to see. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the product.

[0101] The providing unit can select the optimal display method by referring to the user's past purchase history at the time of providing. The providing unit selects the optimal display method by referring to the user's past purchase history at the time of providing. The purchase history includes, for example, purchase date and time, purchased product, purchase frequency, etc., but is not limited to such examples. The providing unit can, for example, preferentially display products similar to products purchased by the user in the past. The providing unit can also display related products based on the user's past purchase history. Furthermore, the providing unit can analyze the user's past purchase history and select the optimal display method. Thereby, the providing unit can display related products in the optimal manner by referring to the user's past purchase history. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's purchase history data to AI and cause the AI ​​to select the display method.

[0102] The providing unit can customize the display of the product based on the user's current environment at the time of providing. The providing unit customizes the display of the product based on the user's current environment at the time of providing. The current environment includes, for example, the type of device, the place of use, the time period, etc., but is not limited to such examples. For example, when the user uses the app at night, the providing unit can provide a display method of a night mode. In addition, when the user is in a specific place (e.g., a shopping mall), the providing unit can also display products related to the place. Furthermore, when the user uses the app during a specific time period (e.g., lunch time), the providing unit can also display products related to the time period. Thereby, the providing unit customizes the display of the product based on the user's current environment, thereby enabling more appropriate display. Part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's current environmental data to AI and cause AI to execute customization of the display of the product.

[0103] The providing unit can estimate the user's emotion and determine the priority order of products to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit preferentially displays important products. In addition, when the user is relaxed, the providing unit can also preferentially display products including detailed information. Furthermore, when the user is in a hurry, the providing unit can also preferentially display the most important products. In this way, the providing unit can provide important products preferentially by determining the priority order of products according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order of products.

[0104] The providing unit can provide the optimal product by considering the geographical location information of the user at the time of providing. The providing unit can provide the optimal product by considering the geographical location information of the user at the time of providing. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the providing unit can provide a product that is popular in that area. In addition, when the user is traveling, the providing unit can also provide a product that corresponds to the consumption desire at the travel destination. Furthermore, when the user is participating in a specific event (for example, a festival), the providing unit can also provide a product related to the event. In this way, the providing unit can provide the optimal product specific to the area by considering the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to execute the provision of the product.

[0105] The providing unit can provide related products by analyzing the social media activity of the user at the time of providing. The providing unit can provide related products by analyzing the social media activity of the user at the time of providing. Social media activities include, for example, the content of posts, the number of likes, the number of followers, etc., but are not limited to such examples. The providing unit can provide related products based on the content shared by the user on social media, for example. The providing unit can also provide related products based on information on brands and influencers that the user follows on social media. Furthermore, the providing unit can also provide related products based on posts that the user "liked" on social media. Thereby, the providing unit can provide related products by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the social media activity of the user to AI and cause the AI ​​to provide the product.

[0106] The collection unit can estimate the user's emotion and adjust the collection method of the profile information based on the estimated emotion of the user. For example, when the user is feeling stressed, the collection unit provides a simple and intuitive collection method. In addition, the collection unit can also provide a method for collecting detailed information when the user is relaxed. Furthermore, the collection unit can also provide a method for quickly collecting profile information when the user is in a hurry. This allows the collection unit to adjust the collection method of the profile information according to the user's emotion, thereby enabling more appropriate information collection. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection method.

[0107] The collection unit can select the optimal collection method by referring to the user's past behavioral history at the time of collection. The collection unit selects the optimal collection method by referring to the user's past behavioral history at the time of collection. The behavioral history includes, for example, past input contents, input frequency, etc., but is not limited to such examples. The collection unit can collect related profile information, for example, based on the user's past behavioral history. The collection unit can also analyze the user's past behavioral history and select the optimal collection method. Furthermore, the collection unit can also determine the priority of information to be collected by referring to the user's past behavioral history. Thereby, the collection unit can collect related profile information in an optimal manner by referring to the user's past behavioral history. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral history data to AI and cause the AI ​​to select the collection method.

[0108] The collection unit can estimate the user's emotion and determine the priority of the profile information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects important profile information. In addition, the collection unit can also collect detailed profile information when the user is relaxed. Furthermore, when the user is in a hurry, the collection unit can preferentially collect the most important profile information. In this way, the collection unit can preferentially collect important information by determining the priority of the profile information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using an AI or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the profile information.

[0109] The collection unit can collect optimal profile information in consideration of the geographical location information of the user at the time of collection. The collection unit collects optimal profile information in consideration of the geographical location information of the user at the time of collection. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the collection unit can collect profile information related to the area. In addition, when the user is traveling, the collection unit can also collect profile information at the travel destination. Furthermore, when the user is participating in a specific event (e.g., a festival), the collection unit can also collect profile information related to the event. In this way, the collection unit can collect region-specific profile information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and cause the AI ​​to execute collection of profile information.

[0110] The history collection unit can estimate the user's emotion and adjust the history collection method based on the estimated user's emotion. For example, when the user is stressed, the history collection unit provides a simple and intuitive collection method. In addition, when the user is relaxed, the history collection unit can also provide a method for collecting detailed history information. Furthermore, when the user is in a hurry, the history collection unit can also provide a method for quickly collecting history information. This allows the history collection unit to adjust the history collection method according to the user's emotion, thereby enabling more appropriate information collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the history collection unit may be performed using an AI, or may be performed without using an AI. For example, the history collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection method.

[0111] The history collection unit can select the optimal collection method by referring to the user's past purchase history at the time of collection. The history collection unit can select the optimal collection method by referring to the user's past purchase history at the time of collection. The purchase history includes, for example, purchase date and time, purchased product, purchase frequency, etc., but is not limited to such examples. The history collection unit can collect related history information based on, for example, the user's past purchase history. In addition, the history collection unit can analyze the user's past purchase history and select the optimal collection method. Furthermore, the history collection unit can determine the priority of information to be collected by referring to the user's past purchase history. Thereby, the history collection unit can collect related history information in an optimal manner by referring to the user's past purchase history. A part or all of the above-mentioned processing in the history collection unit may be performed using AI, or may be performed without using AI. For example, the history collection unit can input the user's purchase history data to AI and cause the AI ​​to select the collection method.

[0112] The history collection unit can estimate the user's emotion and determine the priority of the history to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the history collection unit preferentially collects important history information. In addition, when the user is relaxed, the history collection unit can also collect detailed history information. Furthermore, when the user is in a hurry, the history collection unit can preferentially collect the most important history information. In this way, the history collection unit can preferentially collect important information by determining the priority of the history according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the history collection unit may be performed using an AI, or may be performed without using an AI. For example, the history collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the history.

[0113] The history collection unit can collect an optimal history by considering the geographical location information of the user at the time of collection. The history collection unit collects an optimal history by considering the geographical location information of the user at the time of collection. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the history collection unit can collect history information related to the area. In addition, when the user is traveling, the history collection unit can also collect history information at the travel destination. Furthermore, when the user is participating in a specific event (for example, a festival), the history collection unit can also collect history information related to the event. In this way, the history collection unit can collect area-specific history information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the history collection unit may be performed using AI, or may be performed without using AI. For example, the history collection unit can input the geographical location information of the user to AI and cause the AI ​​to execute the collection of history.

[0114] The trend collection unit can estimate the user's emotion and adjust the trend data collection method based on the estimated user's emotion. For example, when the user is stressed, the trend collection unit provides a simple and intuitive collection method. In addition, when the user is relaxed, the trend collection unit can also provide a method for collecting detailed trend data. Furthermore, when the user is in a hurry, the trend collection unit can also provide a method for quickly collecting trend data. This allows the trend collection unit to adjust the trend data collection method according to the user's emotion, thereby enabling more appropriate information collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the trend collection unit may be performed using an AI, or may be performed without using an AI. For example, the trend collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection method.

[0115] The trend collection unit can select the optimal collection method by referring to past trend data at the time of collection. The trend collection unit selects the optimal collection method by referring to past trend data at the time of collection. Trend data includes, for example, social media trends, search engine trends, and the like, but is not limited to such examples. The trend collection unit can collect current trends, for example, based on past trend data. In addition, the trend collection unit can analyze past trend data and select the optimal collection method. Furthermore, the trend collection unit can determine the priority order of trend data to be collected by referring to past trend data. Thereby, the trend collection unit can collect current trends in an optimal manner by referring to past trend data. A part or all of the above-mentioned processing in the trend collection unit may be performed using AI, or may be performed without using AI. For example, the trend collection unit can input past trend data to AI and cause AI to select a collection method.

[0116] The trend collection unit can estimate the user's emotion and determine the priority order of trend data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the trend collection unit preferentially collects important trend data. In addition, the trend collection unit can also collect detailed trend data when the user is relaxed. Furthermore, when the user is in a hurry, the trend collection unit can preferentially collect the most important trend data. In this way, the trend collection unit can preferentially collect important information by determining the priority order of trend data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the trend collection unit may be performed using an AI or may be performed without using an AI. For example, the trend collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order of trend data.

[0117] The trend collecting unit can collect optimal trend data in consideration of the geographical location information of the user at the time of collection. The trend collecting unit collects optimal trend data in consideration of the geographical location information of the user at the time of collection. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the trend collecting unit can collect trend data related to the area. In addition, when the user is traveling, the trend collecting unit can also collect trend data at the travel destination. Furthermore, when the user is participating in a specific event (for example, a festival), the trend collecting unit can also collect trend data related to the event. In this way, the trend collecting unit can collect trend data specific to the area by considering the geographical location information of the user. A part or all of the above-mentioned processing in the trend collecting unit may be performed using AI, or may be performed without using AI. For example, the trend collecting unit can input the geographical location information of the user to AI and cause the AI ​​to collect trend data.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The recommendation system may further include a feedback collection unit that collects real-time feedback from users. The feedback collection unit enables users to input ratings and comments on recommended products in real time. For example, the user may provide positive feedback such as "this product matches my taste" for the recommended product. The user may also provide negative feedback such as "this product is over my budget." In this way, the feedback collection unit collects real-time feedback from users, allowing the recommendation system to respond to the needs of users more quickly. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI or may be performed without using AI. For example, the feedback collection unit may input user feedback data to AI and cause AI to adjust the recommendations.

[0120] The collection unit can further collect health data of the user. The health data includes, for example, the number of steps, heart rate, sleep patterns, and the like, but is not limited to such examples. For example, when the user uses a wearable device, the collection unit can automatically collect health data from the device. The collection unit can also grasp the health condition based on data input by the user to a health app. As a result, the collection unit can make recommendations according to the health condition by collecting the user's health data. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the user's health data to AI and cause the AI ​​to adjust the recommendations.

[0121] The history collection unit can further include a willingness estimation function for predicting the user's willingness to purchase. The willingness estimation function predicts the current willingness to purchase based on the user's past purchase history and behavior history. For example, if the user has frequently purchased products of a specific brand in the past, it can be estimated that the user has a high willingness to purchase products of that brand. In addition, if the user has a tendency to purchase specific products in a specific season, it can also predict the willingness to purchase in that season. This allows the history collection unit to predict the user's willingness to purchase, thereby enabling more accurate recommendations. Some or all of the above-mentioned processing in the willingness estimation function may be performed using AI, or may be performed without using AI. For example, the history collection unit can input the user's behavior history data into AI and have the AI ​​execute the prediction of the willingness to purchase.

[0122] The trend collecting unit can further collect trend data by taking into consideration the user's influence on social media. Influence includes, for example, the number of followers, the engagement rate of posts, and the like, but is not limited to such examples. For example, if the user is an influencer with many followers, the trend collecting unit can collect trend data by focusing on the content of the user's posts. In addition, if the user has high engagement in a specific topic, the trend collecting unit can also collect trend data related to the topic with priority. In this way, the trend collecting unit can collect more relevant trend data by taking into consideration the user's influence. A part or all of the above-mentioned processing in the evaluation of influence may be performed using AI, or may be performed without using AI. For example, the trend collecting unit can input the user's social media data into AI and cause the AI ​​to perform the evaluation of influence.

[0123] The analysis unit can further perform the analysis taking into consideration the user's life events. Life events include, for example, marriage, moving, childbirth, and the like, but are not limited to such examples. For example, if the user is about to get married, the analysis unit can prioritize analyzing products related to the wedding. Also, if the user is planning to move, the analysis unit can analyze products related to the new house. Furthermore, if the user is planning to give birth, the analysis unit can prioritize analyzing baby products. This allows the analysis unit to make more personalized recommendations by taking into consideration the user's life events. Some or all of the above-mentioned processing in grasping the life events may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the user's life event data into AI and cause the AI ​​to adjust the analysis.

[0124] The recommendation unit can further estimate the user's emotion and adjust the timing of the recommendation based on the estimated user's emotion. For example, if the user is feeling stressed, the recommendation is made during a time period when the user can relax. Also, if the user is relaxed, the recommendation can be made on the spot. Furthermore, if the user is in a hurry, the recommendation can be made quickly. This allows the recommendation unit to adjust the timing of the recommendation according to the user's emotion, thereby enabling more effective recommendation. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of the recommendation.

[0125] The providing unit can further estimate the user's emotion and adjust the method of explaining the product to be provided based on the estimated user's emotion. For example, if the user is stressed, a concise and easy-to-understand explanation is provided. Also, if the user is relaxed, a detailed explanation can be provided. Furthermore, if the user is in a hurry, an explanation that focuses on the main points can be provided. As a result, the providing unit can adjust the method of explaining the product according to the user's emotion, thereby enabling an explanation that is easy for the user to understand. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the method of explaining the product.

[0126] The reception unit can further estimate the user's emotion and adjust the confirmation method of the input contents based on the estimated user's emotion. For example, if the user is stressed, a concise confirmation message can be displayed. Also, if the user is relaxed, a detailed confirmation message can be displayed. Furthermore, if the user is in a hurry, a message that can be confirmed quickly can be displayed. In this way, the reception unit can adjust the confirmation method of the input contents according to the user's emotion, thereby enabling a message that is easy for the user to confirm. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the confirmation method.

[0127] The analysis unit can further estimate the user's emotion and adjust the feedback method of the analysis result based on the estimated user's emotion. For example, if the user is stressed, concise and positive feedback is provided. Also, if the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, feedback that can be quickly understood can be provided. As a result, the analysis unit can adjust the feedback method of the analysis result according to the user's emotion, thereby enabling feedback that is easy for the user to accept. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the feedback method.

[0128] The providing unit can further estimate the user's emotion and adjust the selection criteria for the product to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, products with a relaxing effect are preferentially provided. Also, when the user is relaxed, products that attract the user's interest can be provided. Furthermore, when the user is in a hurry, products that can be used quickly can be provided. In this way, the providing unit can provide the optimal product for the user by adjusting the product selection criteria according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the product selection criteria.

[0129] The process flow of the second embodiment will be briefly described below.

[0130] Step 1: The reception unit inputs the user's consumption desires. The user's consumption desires include willingness to buy, interest in specific products, etc. The reception unit allows the user to input their own consumption desires, preferences, budget, profile information, etc. in a chat format. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis is performed using methods such as data mining and statistical analysis. The generation AI understands the user's input and generates data to recommend optimal products, taking into account past purchase history, behavioral history, and even market trends. Step 3: The recommendation unit uses the generation AI to recommend products based on the results of the analysis by the analysis unit. Recommendations are made using methods such as recommendation algorithms and filtering techniques. The generation AI selects the most suitable products based on the user's past purchase history, behavioral history, and market trend data. Step 4: The providing unit provides the product recommended by the recommendation unit to the user. The product is provided by a user interface, a notification method, or the like. For example, the providing unit presents the recommended product to the user in a chat format.

[0131] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0133] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, provision unit, collection unit, history collection unit, and trend collection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and inputs the user's consumption desire. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends a product based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the recommended product to the user. The collection unit is realized, for example, by the control unit 46A of the smart device 14, and collects user profile information. The history collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects past purchase history or behavior history. The trend collecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects market trend data. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, provision unit, collection unit, history collection unit, and trend collection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and inputs the user's consumption desire. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends a product based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the recommended product to the user. The collection unit is realized, for example, by the control unit 46A of the smart glasses 214, and collects user profile information. The history collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects past purchase history or behavior history. The trend collecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects market trend data. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0153] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, provision unit, collection unit, history collection unit, and trend collection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and inputs the user's consumption desire. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends a product based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the recommended product to the user. The collection unit is realized, for example, by the control unit 46A of the headset type terminal 314, and collects user profile information. The history collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects past purchase history or behavior history. The trend collecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects market trend data. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a 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.

[0169] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0174] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0175] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0178] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0180] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0182] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, provision unit, collection unit, history collection unit, and trend collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and inputs the user's consumption desire. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends a product based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the recommended product to the user. The collection unit is realized, for example, by the control unit 46A of the robot 414, and collects user profile information. The history collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects past purchase history or behavior history. The trend collecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects market trend data. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0184] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0185] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0187] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0189] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0192] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0193] In addition, 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 upon request from the data processing device 12.

[0194] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0195] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0196] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0197] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0198] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0200] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0202] (Appendix 1) A reception unit for inputting a user's consumption desires; an analysis unit that analyzes the information input by the reception unit; a recommendation unit that recommends products based on the results of the analysis by the analysis unit; A providing unit that provides the product recommended by the recommendation unit to a user. A system characterized by: (Appendix 2) A collection unit for collecting user profile information 2. The system of claim 1, (Appendix 3) Equipped with a history collection unit that collects past purchase history or behavioral history 2. The system of claim 1, (Appendix 4) Equipping a trend collection department to collect market trend data 2. The system of claim 1, (Appendix 5) The analysis unit is Analyze user needs based on collected data 2. The system of claim 1, (Appendix 6) The recommendation unit is Select the appropriate product based on the analysis results 2. The system of claim 1, (Appendix 7) The providing unit is Present recommended products to the user 2. The system of claim 1, (Appendix 8) The reception unit is Estimate user emotions and dynamically change the design of the input interface based on the estimated user emotions. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1, (Appendix 10) The reception unit is Customize input prompts based on the user's current environment 2. The system of claim 1, (Appendix 11) The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The reception unit is Provide users with input prompts based on their geographic location to meet their local consumption needs 2. The system of claim 1, (Appendix 13) The reception unit is Analyze your social media activity and auto-complete relevant text 2. The system of claim 1, (Appendix 14) The analysis unit is Inferring user emotions and dynamically adjusting analysis algorithms based on the estimated user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is When analyzing, combine users' past purchase history with current market trends to improve analysis accuracy. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, take into account user profile information to personalize analysis results 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, we take into account your geographic location to reflect local trends 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, we complement the analysis results by looking at the user's social media activity. 2. The system of claim 1, (Appendix 20) The recommendation unit is Infer user emotions and adjust the way recommendations are presented based on the inferred user emotions 2. The system of claim 1, (Appendix 21) The recommendation unit is When making recommendations, adjust the level of detail of the recommendations based on the importance of the products 2. The system of claim 1, (Appendix 22) The recommendation unit is When making recommendations, apply different recommendation algorithms depending on the product category. 2. The system of claim 1, (Appendix 23) The recommendation unit is Infer user sentiment and adjust the length of recommendations based on the estimated user sentiment 2. The system of claim 1, (Appendix 24) The recommendation unit is When making recommendations, prioritize recommendations based on when the products were submitted 2. The system of claim 1, (Appendix 25) The recommendation unit is When making recommendations, adjust the order of recommendations based on product relevance 2. The system of claim 1, (Appendix 26) The providing unit is Infer user emotions and adjust the display method of offered products based on the estimated user emotions 2. The system of claim 1, (Appendix 27) The providing unit is When providing the information, the optimal display method is selected by referring to the user's past purchase history. 2. The system of claim 1, (Appendix 28) The providing unit is At delivery time, customize product display based on the user's current environment 2. The system of claim 1, (Appendix 29) The providing unit is Estimate user emotions and prioritize products to provide based on the estimated user emotions 2. The system of claim 1, (Appendix 30) The providing unit is When providing products, the most suitable products are provided taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 31) The providing unit is When offered, analyze your social media activity to provide you with relevant products 2. The system of claim 1, (Appendix 32) The collecting unit includes: To estimate a user's emotions and adjust a method of collecting profile information based on the estimated user's emotions 3. The system of claim 2, (Appendix 33) The collecting unit includes: When collecting data, the optimal collection method is selected by referring to the user's past behavioral history. 3. The system of claim 2, (Appendix 34) The collecting unit includes: To estimate a user's emotion and determine a priority of profile information to be collected based on the estimated user's emotion. 3. The system of claim 2, (Appendix 35) The collecting unit includes: At the time of collection, take into account the user's geographic location to collect optimal profile information 3. The system of claim 2, (Appendix 36) The history collection unit is Infer user emotions and adjust history collection methods based on the inferred user emotions 4. The system of claim 3. (Appendix 37) The history collection unit is When collecting data, the optimal collection method is selected by referring to the user's past purchase history. 4. The system of claim 3. (Appendix 38) The history collection unit is A user's emotion is estimated, and a priority order of history to be collected is determined based on the estimated user's emotion. 4. The system of claim 3. (Appendix 39) The history collection unit is When collecting data, the geographic location of the user is taken into consideration to collect the most appropriate history. 4. The system of claim 3. (Appendix 40) The trend collecting unit, To estimate a user's sentiment and adjust a trend data collection method based on the estimated user's sentiment 5. The system of claim 4. (Appendix 41) The trend collecting unit, When collecting data, select the optimal collection method by referring to past trend data. 5. The system of claim 4. (Appendix 42) The trend collecting unit, To estimate user sentiment and determine a priority order of trend data to be collected based on the estimated user sentiment. 5. The system of claim 4. (Appendix 43) The trend collecting unit, At the time of collection, the geographic location information of the user is taken into consideration to collect optimal trend data. 5. The system of claim 4. [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting a user's consumption desires; an analysis unit that analyzes the information input by the reception unit; a recommendation unit that recommends products based on the results of the analysis by the analysis unit; A providing unit that provides the product recommended by the recommendation unit to a user. A system characterized by:

2. A collection unit for collecting user profile information 2. The system of claim 1 .

3. Equipped with a history collection unit that collects past purchase history or behavioral history 2. The system of claim 1 .

4. Equipping a trend collection department to collect market trend data 2. The system of claim 1 .

5. The analysis unit is Analyze user needs based on collected data 2. The system of claim 1 .

6. The recommendation unit is Select the appropriate product based on the analysis results 2. The system of claim 1 .

7. The providing unit is Present recommended products to the user 2. The system of claim 1 .

8. The reception unit is Estimate user emotions and dynamically change the design of the input interface based on the estimated user emotions.

2. The system of claim 1 .

Images