system

Abstract

The system according to the embodiment aims to enable users to easily search for products and carry out purchasing procedures. [Solution] A system according to an embodiment includes a reception unit, a proposal unit, and a purchase unit. The reception unit accepts product purchase requests from users. The proposal unit analyzes product information and proposes products based on the information accepted by the reception unit. The purchase unit searches for the products proposed by the proposal unit on an e-commerce site and completes the purchase procedure.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology requires two steps to purchase a product: information search and purchase procedure, which is time-consuming.

[0005] The system according to the embodiment aims to enable users to easily search for products and carry out purchasing procedures. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and a purchase unit. The reception unit accepts a product purchase request from a user. The proposal unit analyzes product information and proposes products based on the information accepted by the reception unit. The purchase unit searches for the products proposed by the proposal unit on an e-commerce site and completes the purchase procedure. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily search for products and carry out purchasing procedures. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A product purchase support system according to an embodiment of the present invention significantly reduces the effort required for users to purchase products. This product purchase support system allows users to seamlessly search for and purchase products simply by entering the desired product into a messaging app. Specifically, when a user enters the desired product into a messaging app, a generation AI analyzes the input information and suggests optimal products. The suggested product information is provided to the user via the messaging app, and the user selects the desired product from the suggested products. The system then searches for the product selected by the user on an e-commerce site, automatically completing the purchase process. This allows users to seamlessly search for and purchase products simply by entering the product name into a messaging app. This system is extremely convenient for busy businesspeople, elderly people, and other users who don't want to spend time and effort. Furthermore, because the generation AI collects the latest information and suggests optimal products, users can always access the latest product information. This significantly reduces the effort required for users to purchase products.

[0029] A product purchase support system according to an embodiment includes a reception unit, a suggestion unit, and a purchase unit. The reception unit accepts a product purchase request from a user. The user's purchase request can be accepted, for example, in the form of text input or voice input. The suggestion unit analyzes product information and suggests products based on the information accepted by the reception unit. The product information can be analyzed using technologies such as natural language processing and image analysis. The suggestion unit uses a generation AI to suggest optimal products based on the user's needs. For example, the generation AI can analyze the user's input information and suggest related products using a ranking display or recommendation algorithm. The purchase unit searches for the products suggested by the suggestion unit on an e-commerce site and completes the purchase process. The purchase process includes steps such as adding the product to a cart and payment processing. The purchase unit automatically completes the purchase process for the product selected by the user, thereby reducing the user's effort. As a result, the product purchase support system according to an embodiment can perform a comprehensive process from product information search to purchase by simply having the user enter the product name into a messaging app. For example, the reception unit accepts the product name entered by the user into the messaging app. The proposal unit uses generative AI to set product selection criteria based on the user's needs and propose the most suitable products. The purchase unit searches for the product selected by the user on an e-commerce site and automatically completes the purchase procedure. This significantly reduces the effort required for users to purchase products.

[0030] The suggestion unit collects product information from the web and social networking sites and identifies products based on the user's needs. The suggestion unit collects product information from the web and social networking sites using, for example, web scraping or APIs. For example, the suggestion unit can automatically collect product information from specific websites using web scraping technology. The suggestion unit can also collect product information of user interest using social networking site APIs. The suggestion unit analyzes the collected product information and identifies optimal products based on the user's needs. For example, the suggestion unit can identify the user's needs based on the user's survey results and past purchase history. As a result, the suggestion unit can identify products that meet the user's needs and suggest optimal products. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI or without a generation AI. For example, the suggestion unit inputs the collected product information into a generation AI, which can then identify optimal products based on the user's needs. As a result, the suggestion unit can identify products that meet the user's needs and suggest optimal products.

[0031] The suggestion unit allows the user to select a product they wish to purchase from the suggested products. The suggestion unit provides methods such as a click operation or a voice command to allow the user to select a product they wish to purchase from the suggested products. For example, the suggestion unit allows the user to select a product they wish to purchase from the suggested products by a click operation. The suggestion unit can also select a product they wish to purchase using a voice command. This allows the suggestion unit to select a product they wish to purchase from the suggested products. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit inputs the user's selection information into the generation AI, which can then suggest an optimal product based on the user's selection. This allows the suggestion unit to select a product the user wishes to purchase from the suggested products.

[0032] The purchasing unit searches for products selected by a user on an e-commerce site and automatically completes the purchase process. The purchasing unit provides methods such as API integration or bot operation to search for products selected by a user on an e-commerce site and automatically complete the purchase process. For example, the purchasing unit can use the API of the e-commerce site to search for products selected by a user and automatically complete the purchase process. The purchasing unit can also use a bot to automatically complete the purchase process on an e-commerce site. This allows the purchasing unit to automatically complete the purchase process for products selected by a user, thereby reducing the user's effort. Some or all of the above-described processing in the purchasing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the purchasing unit inputs product information selected by a user into a generation AI, which then automatically completes the purchase process on an e-commerce site. This allows the purchasing unit to automatically complete the purchase process for products selected by a user, thereby reducing the user's effort.

[0033] The reception unit can accept a product name entered by a user into a messaging app. For example, the reception unit uses a messaging app such as LINE (registered trademark) or WhatsApp (registered trademark) to accept a product name entered by a user into a messaging app. For example, the reception unit can accept a product name entered by a user into LINE. The reception unit can also accept a product name entered by a user into WhatsApp. This allows the reception unit to accept a product name entered by a user into a messaging app, thereby enabling easy product purchase. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input a product name entered by a user into a messaging app into the generation AI, which can analyze the product information. This allows the reception unit to accept a product name entered by a user into a messaging app, thereby enabling easy product purchase.

[0034] The suggestion unit can set product selection criteria based on the user's needs. The suggestion unit uses criteria such as price, ratings, and popularity to set product selection criteria based on the user's needs. For example, the suggestion unit can set price selection criteria based on the user's budget. The suggestion unit can also set product evaluation criteria based on the user's ratings. Furthermore, the suggestion unit can set selection criteria based on the popularity of the products. This allows the suggestion unit to suggest more appropriate products by setting product selection criteria based on the user's needs. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI or without using a generation AI. For example, the suggestion unit can input user needs information into the generation AI, which can set product selection criteria. This allows the suggestion unit to suggest more appropriate products by setting product selection criteria based on the user's needs.

[0035] The reception unit can analyze the user's past purchase history and select the optimal reception method. The reception unit, for example, uses techniques such as data mining and machine learning to analyze the user's past purchase history and select the optimal reception method. For example, the reception unit can prioritize reception of product categories that the user has frequently purchased in the past. The reception unit can also analyze the user's past purchase history to determine whether they tend to purchase during specific time periods and accept purchases during those time periods. Furthermore, the reception unit can prioritize reception of purchases from specific brands or stores based on the user's past purchase history. This allows the reception unit to select the optimal reception method by analyzing the user's past purchase history. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past purchase history data into the generation AI, which can select the optimal reception method. This allows the reception unit to select the optimal reception method by analyzing the user's past purchase history.

[0036] When accepting a product purchase request, the reception unit can perform filtering based on the user's current living situation and areas of interest. For example, when accepting a product purchase request, the reception unit uses information such as a questionnaire or behavioral history to perform filtering based on the user's current living situation and areas of interest. For example, if the user is interested in health, the reception unit can prioritize health-related products. Furthermore, if the user is traveling, the reception unit can prioritize products that can be used at the travel destination. Furthermore, if the user has started a new hobby, the reception unit can prioritize products related to that hobby. In this way, the reception unit can receive more appropriate products by filtering based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's living situation and areas of interest into the generation AI, which can then perform filtering. In this way, the reception unit can receive more appropriate products by filtering based on the user's living situation and areas of interest.

[0037] When accepting a product purchase request, the reception unit can prioritize accepting highly relevant products by taking into account the user's geographical location information. For example, when accepting a product purchase request, the reception unit uses information such as GPS data and an IP address to prioritize accepting highly relevant products by taking into account the user's geographical location information. For example, when accepting a product purchase request, the reception unit can prioritize accepting products that are popular in that area when the user is in a specific region. Furthermore, when the user is traveling, the reception unit can prioritize accepting products that can be used at the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize accepting products that can be purchased at a nearby store. In this way, the reception unit can prioritize accepting highly relevant products by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's geographical location information into the generation AI, which can then prioritize accepting highly relevant products. In this way, the reception unit can prioritize accepting highly relevant products by taking into account the user's geographical location information.

[0038] When accepting a product purchase request, the reception unit can analyze the user's social media activity and accept related products. For example, when accepting a product purchase request, the reception unit analyzes the user's social media activity and uses information such as the content of posts and the number of followers to accept related products. For example, the reception unit can prioritize accepting products that the user is talking about on social media. The reception unit can also prioritize accepting products that are introduced by influencers the user follows. Furthermore, the reception unit can prioritize accepting products that are popular in communities in which the user participates. In this way, the reception unit can prioritize accepting related products by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can accept related products. In this way, the reception unit can prioritize accepting related products by analyzing the user's social media activity.

[0039] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit uses information such as sales data and user ratings to adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit can make a suggestion including detailed descriptions and reviews for expensive products. The suggestion unit can also make a suggestion with a concise description for products used daily. Furthermore, the suggestion unit can also make a suggestion by emphasizing special features for new products or limited-edition products. In this way, the suggestion unit can make a more appropriate suggestion by adjusting the level of detail of the suggestion based on the importance of the product. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input product importance data into the generation AI, which can adjust the level of detail of the suggestion. In this way, the suggestion unit can make a more appropriate suggestion by adjusting the level of detail of the suggestion based on the importance of the product.

[0040] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit uses technologies such as collaborative filtering and content-based filtering to apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit can make suggestions based on trend information for fashion products. For electronic devices, the suggestion unit can make suggestions based on specification comparisons. For food products, the suggestion unit can make suggestions based on nutritional information and recipes. This allows the suggestion unit to make more appropriate suggestions by applying different suggestion algorithms depending on the product category. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input product category data into the generation AI, and the generation AI can apply different suggestion algorithms. This allows the suggestion unit to make more appropriate suggestions by applying different suggestion algorithms depending on the product category.

[0041] The suggestion unit can determine the priority of suggestions based on the time of product submission when making suggestions. For example, the suggestion unit uses information such as release dates and campaign periods to determine the priority of suggestions based on the time of product submission when making suggestions. For example, the suggestion unit can prioritize new products or limited-edition products. The suggestion unit can also prioritize products available during sales periods. Furthermore, the suggestion unit can prioritize seasonal products or event-related products. This allows the suggestion unit to determine the priority of suggestions based on the time of product submission, thereby enabling more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input product submission time data into the generation AI, which can then determine the priority of suggestions. This allows the suggestion unit to determine the priority of suggestions based on the time of product submission, thereby enabling more appropriate suggestions.

[0042] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit uses information such as common attributes and user interests to adjust the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant products based on the user's past purchase history. The suggestion unit can also prioritize suggesting highly relevant products based on the user's current areas of interest. Furthermore, the suggestion unit can prioritize suggesting highly relevant products based on the user's social media activity. This allows the suggestion unit to adjust the order of suggestions based on the relevance of products, thereby enabling more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input product relevance data into the generation AI, which can then adjust the order of suggestions. This allows the suggestion unit to adjust the order of suggestions based on the relevance of products, thereby enabling more appropriate suggestions.

[0043] During the purchase process, the purchasing unit can analyze the user's past purchasing behavior and select the optimal purchase process method. For example, during the purchase process, the purchasing unit uses information such as purchase frequency and purchase amount to analyze the user's past purchasing behavior and select the optimal purchase process method. For example, the purchasing unit can prioritize purchase processes that the user has used in the past. The purchasing unit can also prioritize specific payment methods based on the user's past purchasing behavior. Furthermore, the purchasing unit can prioritize specific delivery methods based on the user's past purchasing behavior. In this way, the purchasing unit can select the optimal purchase process method by analyzing the user's past purchasing behavior. Some or all of the above-described processing in the purchasing unit may be performed using or without the generation AI. For example, the purchasing unit can input the user's past purchasing behavior data into the generation AI, which can select the optimal purchase process method. In this way, the purchasing unit can select the optimal purchase process method by analyzing the user's past purchasing behavior.

[0044] The purchasing unit can customize the purchasing procedure based on the user's current living situation during the purchasing process. For example, the purchasing unit uses information such as a questionnaire or behavioral history to customize the purchasing procedure based on the user's current living situation during the purchasing process. For example, if the user is traveling, the purchasing unit can provide a method for picking up the item at the user's destination. If the user is busy, the purchasing unit can also provide a fast delivery method. Furthermore, if the user is at home, the purchasing unit can also provide a standard delivery method. This allows the purchasing unit to provide the optimal purchasing procedure based on the user's living situation. Some or all of the above-described processing in the purchasing unit may be performed using or without the generation AI. For example, the purchasing unit can input the user's living situation data into the generation AI, which can then customize the purchasing procedure. This allows the purchasing unit to provide the optimal purchasing procedure based on the user's living situation.

[0045] The purchasing unit can select the optimal purchasing method by taking into account the user's geographical location information during the purchase process. For example, the purchasing unit uses information such as GPS data and an IP address to select the optimal purchasing method by taking into account the user's geographical location information during the purchase process. For example, if the user is in a specific area, the purchasing unit can provide delivery methods available in that area. Also, if the user is traveling, the purchasing unit can provide a method for receiving the item at the user's destination. Furthermore, if the user is at home, the purchasing unit can provide a standard delivery method. In this way, the purchasing unit can provide the optimal purchasing method by taking into account the user's geographical location information. Some or all of the above-described processing in the purchasing unit may be performed using or without the generation AI. For example, the purchasing unit can input the user's geographical location information into the generation AI, which can select the optimal purchasing method. In this way, the purchasing unit can provide the optimal purchasing method by taking into account the user's geographical location information.

[0046] During the purchase process, the purchasing unit can analyze the user's social media activity and suggest a method for the purchase process. For example, during the purchase process, the purchasing unit uses information such as the content of posts and the number of followers to analyze the user's social media activity and suggest a method for the purchase process. For example, the purchasing unit can prioritize products that the user is talking about on social media during the purchase process. The purchasing unit can also prioritize products that the user is following during the purchase process. Furthermore, the purchasing unit can prioritize products that are popular in the communities the user participates in during the purchase process. In this way, the purchasing unit can suggest a more appropriate method for the purchase process by analyzing the user's social media activity. Some or all of the above-described processing in the purchasing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the purchasing unit can input the user's social media activity data into a generation AI, which can then suggest a method for the purchase process. In this way, the purchasing unit can suggest a more appropriate method for the purchase process by analyzing the user's social media activity.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can analyze the user's past purchase history and preferentially suggest product categories that the user frequently purchases. For example, if the user has purchased many electronic devices in the past, the reception unit can preferentially suggest products related to electronic devices. Also, if the user has a preference for a particular brand, the reception unit can preferentially suggest products from that brand. Furthermore, if the user tends to purchase specific products in a particular season, the reception unit can suggest products that are suited to that season. In this way, the reception unit can utilize the user's past purchase history to suggest more appropriate products.

[0049] The suggestion unit can suggest products taking into consideration the user's current living situation. For example, if the user is planning to move, the suggestion unit can suggest furniture and home appliances necessary for the move. Also, if the user has started a new hobby, the suggestion unit can suggest products related to that hobby. Furthermore, if the user is interested in health, the suggestion unit can suggest health foods and fitness-related products. In this way, the suggestion unit can suggest products according to the user's living situation.

[0050] The purchasing unit can propose the optimal delivery method taking into account the user's geographical location information. For example, if the user lives in an urban area, same-day delivery or next-day delivery can be proposed. If the user lives in a rural area, standard delivery or a specific delivery company can be proposed. Furthermore, if the user is traveling, a method for receiving the product at the user's destination can be proposed. This allows the purchasing unit to propose the optimal delivery method based on the user's geographical location information.

[0051] The reception unit can analyze the user's social media activity and prioritize suggesting products that the user is interested in. For example, if the user frequently mentions a particular product on social media, the reception unit can prioritize suggesting that product. The reception unit can also suggest products introduced by influencers the user follows. Furthermore, the reception unit can suggest products that are popular in communities in which the user participates. This allows the reception unit to make product suggestions based on the user's social media activity.

[0052] The purchasing unit can analyze the user's past purchasing behavior and suggest the optimal payment method. For example, if the user has frequently used credit cards in the past, it can suggest payment by credit card. Also, if the user prefers to use electronic money, it can suggest payment by electronic money. Furthermore, if the user uses a specific point program, it can suggest a payment method that uses those points. This allows the purchasing unit to suggest the optimal payment method based on the user's past purchasing behavior.

[0053] The purchasing unit can propose the optimal purchasing procedure method taking into consideration the user's current living situation. For example, if the user is busy, a quick purchasing procedure can be proposed. If the user is traveling, a method for receiving the product at the user's destination can be proposed. Furthermore, if the user is at home, a regular delivery method can be proposed. In this way, the purchasing unit can propose the optimal purchasing procedure method according to the user's living situation.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit receives a product purchase request from a user. The purchase request from the user can be received in the form of, for example, text input or voice input. Step 2: The suggestion unit analyzes the product information based on the information received by the reception unit and suggests products. Technologies such as natural language processing and image analysis are used to analyze the product information. The suggestion unit uses a generation AI to suggest optimal products based on the user's needs. For example, the generation AI can analyze the information input by the user and suggest related products using a ranking display or recommendation algorithm. Step 3: The purchasing unit searches for the products suggested by the suggestion unit on the e-commerce site and completes the purchase process. The purchase process includes steps such as adding the product to a cart and processing the payment. The purchasing unit can save the user time and effort by automatically completing the purchase process for the products selected by the user.

[0056] (Example 2) A product purchase support system according to an embodiment of the present invention significantly reduces the effort required for users to purchase products. This product purchase support system allows users to seamlessly search for and purchase products simply by entering the desired product into a messaging app. Specifically, when a user enters the desired product into a messaging app, a generation AI analyzes the input information and suggests optimal products. The suggested product information is provided to the user via the messaging app, and the user selects the desired product from the suggested products. The system then searches for the product selected by the user on an e-commerce site, automatically completing the purchase process. This allows users to seamlessly search for and purchase products simply by entering the product name into a messaging app. This system is extremely convenient for busy businesspeople, elderly people, and other users who don't want to spend time and effort. Furthermore, because the generation AI collects the latest information and suggests optimal products, users can always access the latest product information. This significantly reduces the effort required for users to purchase products.

[0057] A product purchase support system according to an embodiment includes a reception unit, a suggestion unit, and a purchase unit. The reception unit accepts a product purchase request from a user. The user's purchase request can be accepted, for example, in the form of text input or voice input. The suggestion unit analyzes product information and suggests products based on the information accepted by the reception unit. The product information can be analyzed using technologies such as natural language processing and image analysis. The suggestion unit uses a generation AI to suggest optimal products based on the user's needs. For example, the generation AI can analyze the user's input information and suggest related products using a ranking display or recommendation algorithm. The purchase unit searches for the products suggested by the suggestion unit on an e-commerce site and completes the purchase process. The purchase process includes steps such as adding the product to a cart and payment processing. The purchase unit automatically completes the purchase process for the product selected by the user, thereby reducing the user's effort. As a result, the product purchase support system according to an embodiment can perform a comprehensive process from product information search to purchase by simply having the user enter the product name into a messaging app. For example, the reception unit accepts the product name entered by the user into the messaging app. The proposal unit uses generative AI to set product selection criteria based on the user's needs and propose the most suitable products. The purchase unit searches for the product selected by the user on an e-commerce site and automatically completes the purchase procedure. This significantly reduces the effort required for users to purchase products.

[0058] The suggestion unit collects product information from the web and social networking sites and identifies products based on the user's needs. The suggestion unit collects product information from the web and social networking sites using, for example, web scraping or APIs. For example, the suggestion unit can automatically collect product information from specific websites using web scraping technology. The suggestion unit can also collect product information of user interest using social networking site APIs. The suggestion unit analyzes the collected product information and identifies optimal products based on the user's needs. For example, the suggestion unit can identify the user's needs based on the user's survey results and past purchase history. As a result, the suggestion unit can identify products that meet the user's needs and suggest optimal products. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI or without a generation AI. For example, the suggestion unit inputs the collected product information into a generation AI, which can then identify optimal products based on the user's needs. As a result, the suggestion unit can identify products that meet the user's needs and suggest optimal products.

[0059] The suggestion unit allows the user to select a product they wish to purchase from the suggested products. The suggestion unit provides methods such as a click operation or a voice command to allow the user to select a product they wish to purchase from the suggested products. For example, the suggestion unit allows the user to select a product they wish to purchase from the suggested products by a click operation. The suggestion unit can also select a product they wish to purchase using a voice command. This allows the suggestion unit to select a product they wish to purchase from the suggested products. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit inputs the user's selection information into the generation AI, which can then suggest an optimal product based on the user's selection. This allows the suggestion unit to select a product the user wishes to purchase from the suggested products.

[0060] The purchasing unit searches for products selected by a user on an e-commerce site and automatically completes the purchase process. The purchasing unit provides methods such as API integration or bot operation to search for products selected by a user on an e-commerce site and automatically complete the purchase process. For example, the purchasing unit can use the API of the e-commerce site to search for products selected by a user and automatically complete the purchase process. The purchasing unit can also use a bot to automatically complete the purchase process on an e-commerce site. This allows the purchasing unit to automatically complete the purchase process for products selected by a user, thereby reducing the user's effort. Some or all of the above-described processing in the purchasing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the purchasing unit inputs product information selected by a user into a generation AI, which then automatically completes the purchase process on an e-commerce site. This allows the purchasing unit to automatically complete the purchase process for products selected by a user, thereby reducing the user's effort.

[0061] The reception unit can accept a product name entered by a user into a messaging app. For example, the reception unit uses a messaging app such as LINE or WhatsApp to accept a product name entered by a user into a messaging app. For example, the reception unit can accept a product name entered by a user into LINE. The reception unit can also accept a product name entered by a user into WhatsApp. This allows the reception unit to accept a product name entered by a user into a messaging app, thereby enabling easy product purchases. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input a product name entered by a user into a messaging app into the generation AI, which can analyze the product information. This allows the reception unit to accept a product name entered by a user into a messaging app, thereby enabling easy product purchases.

[0062] The suggestion unit can set product selection criteria based on the user's needs. The suggestion unit uses criteria such as price, ratings, and popularity to set product selection criteria based on the user's needs. For example, the suggestion unit can set price selection criteria based on the user's budget. The suggestion unit can also set product evaluation criteria based on the user's ratings. Furthermore, the suggestion unit can set selection criteria based on the popularity of the products. This allows the suggestion unit to suggest more appropriate products by setting product selection criteria based on the user's needs. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI or without using a generation AI. For example, the suggestion unit can input user needs information into the generation AI, which can set product selection criteria. This allows the suggestion unit to suggest more appropriate products by setting product selection criteria based on the user's needs.

[0063] The reception unit can estimate a user's emotions and adjust the timing of accepting a product purchase request based on the estimated user emotions. The reception unit, for example, uses technologies such as facial expression recognition and voice analysis to estimate a user's emotions and adjust the timing of accepting a product purchase request based on the estimated user emotions. For example, the reception unit can capture a user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. This allows the reception unit to adjust the timing of accepting a purchase request based on the user's emotions. For example, if the user is feeling stressed, the reception unit can accept the product purchase request during a time when the user can relax. Furthermore, if the user is excited, the reception unit can accept the product purchase request immediately. Furthermore, if the user is tired, the reception unit can accept the product purchase request after the user has rested. This allows the reception unit to adjust the timing of accepting a purchase request based on the user's emotions, thereby accepting the purchase request at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and adjust the timing at which the generation AI accepts a purchase request. This allows the reception unit to adjust the timing at which the generation AI accepts a purchase request according to the user's emotion, thereby accepting the purchase request at a more appropriate time.

[0064] The reception unit can analyze the user's past purchase history and select the optimal reception method. The reception unit, for example, uses techniques such as data mining and machine learning to analyze the user's past purchase history and select the optimal reception method. For example, the reception unit can prioritize reception of product categories that the user has frequently purchased in the past. The reception unit can also analyze the user's past purchase history to determine whether they tend to purchase during specific time periods and accept purchases during those time periods. Furthermore, the reception unit can prioritize reception of purchases from specific brands or stores based on the user's past purchase history. This allows the reception unit to select the optimal reception method by analyzing the user's past purchase history. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past purchase history data into the generation AI, which can select the optimal reception method. This allows the reception unit to select the optimal reception method by analyzing the user's past purchase history.

[0065] When accepting a product purchase request, the reception unit can perform filtering based on the user's current living situation and areas of interest. For example, when accepting a product purchase request, the reception unit uses information such as a questionnaire or behavioral history to perform filtering based on the user's current living situation and areas of interest. For example, if the user is interested in health, the reception unit can prioritize health-related products. Furthermore, if the user is traveling, the reception unit can prioritize products that can be used at the travel destination. Furthermore, if the user has started a new hobby, the reception unit can prioritize products related to that hobby. In this way, the reception unit can receive more appropriate products by filtering based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's living situation and areas of interest into the generation AI, which can then perform filtering. In this way, the reception unit can receive more appropriate products by filtering based on the user's living situation and areas of interest.

[0066] The reception unit can estimate the user's emotions and prioritize products to be accepted based on the estimated user emotions. The reception unit, for example, uses technologies such as facial expression recognition and voice analysis to estimate the user's emotions and prioritize products to be accepted based on the estimated user emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. This allows the reception unit to prioritize products based on the user's emotions. For example, if the user is feeling stressed, the reception unit can prioritize purchase requests for products with a relaxing effect. If the user is excited, the reception unit can prioritize purchase requests for entertainment-related products. If the user is tired, the reception unit can prioritize purchase requests for health foods and supplements. This allows the reception unit to prioritize products based on the user's emotions and prioritize more appropriate products. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI, which may then determine the priority of products. This allows the reception unit to prioritize more appropriate products by determining the priority of products according to the user's emotions.

[0067] When accepting a product purchase request, the reception unit can prioritize accepting highly relevant products by taking into account the user's geographical location information. For example, when accepting a product purchase request, the reception unit uses information such as GPS data and an IP address to prioritize accepting highly relevant products by taking into account the user's geographical location information. For example, when accepting a product purchase request, the reception unit can prioritize accepting products that are popular in that area when the user is in a specific region. Furthermore, when the user is traveling, the reception unit can prioritize accepting products that can be used at the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize accepting products that can be purchased at a nearby store. In this way, the reception unit can prioritize accepting highly relevant products by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's geographical location information into the generation AI, which can then prioritize accepting highly relevant products. In this way, the reception unit can prioritize accepting highly relevant products by taking into account the user's geographical location information.

[0068] When accepting a product purchase request, the reception unit can analyze the user's social media activity and accept related products. For example, when accepting a product purchase request, the reception unit analyzes the user's social media activity and uses information such as the content of posts and the number of followers to accept related products. For example, the reception unit can prioritize accepting products that the user is talking about on social media. The reception unit can also prioritize accepting products that are introduced by influencers the user follows. Furthermore, the reception unit can prioritize accepting products that are popular in communities in which the user participates. In this way, the reception unit can prioritize accepting related products by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can accept related products. In this way, the reception unit can prioritize accepting related products by analyzing the user's social media activity.

[0069] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. The suggestion unit uses technologies such as facial expression recognition and voice analysis to estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. This allows the suggestion unit to adjust the way suggestions are expressed based on the user's emotions. For example, if the user is relaxed, the suggestion unit can suggest products using calm expressions. On the other hand, if the user is excited, the suggestion unit can suggest products using energetic expressions. Furthermore, if the user is stressed, the suggestion unit can suggest products using simple and easy-to-understand expressions. This allows the suggestion unit to make more appropriate suggestions by adjusting the way suggestions are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input user emotion data into the generation AI, which may then adjust the way the suggestion is expressed. This allows the suggestion unit to adjust the way the suggestion is expressed in accordance with the user's emotion, thereby making more appropriate suggestions.

[0070] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit uses information such as sales data and user ratings to adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit can make a suggestion including detailed descriptions and reviews for expensive products. The suggestion unit can also make a suggestion with a concise description for products used daily. Furthermore, the suggestion unit can also make a suggestion by emphasizing special features for new products or limited-edition products. In this way, the suggestion unit can make a more appropriate suggestion by adjusting the level of detail of the suggestion based on the importance of the product. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input product importance data into the generation AI, which can adjust the level of detail of the suggestion. In this way, the suggestion unit can make a more appropriate suggestion by adjusting the level of detail of the suggestion based on the importance of the product.

[0071] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit uses technologies such as collaborative filtering and content-based filtering to apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit can make suggestions based on trend information for fashion products. For electronic devices, the suggestion unit can make suggestions based on specification comparisons. For food products, the suggestion unit can make suggestions based on nutritional information and recipes. This allows the suggestion unit to make more appropriate suggestions by applying different suggestion algorithms depending on the product category. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input product category data into the generation AI, and the generation AI can apply different suggestion algorithms. This allows the suggestion unit to make more appropriate suggestions by applying different suggestion algorithms depending on the product category.

[0072] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. The suggestion unit, for example, uses technologies such as facial expression recognition and voice analysis to estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. This allows the suggestion unit to adjust the length of the suggestions based on the user's emotions. For example, if the user is in a hurry, the suggestion unit can make short, to-the-point suggestions. On the other hand, if the user is relaxed, the suggestion unit can make longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can make suggestions with visually stimulating effects. This allows the suggestion unit to adjust the length of the suggestions based on the user's emotions, thereby making more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input user emotion data into the generation AI, which may then adjust the length of the suggestion. This allows the suggestion unit to adjust the length of the suggestion according to the user's emotion, thereby making more appropriate suggestions.

[0073] The suggestion unit can determine the priority of suggestions based on the time of product submission when making suggestions. For example, the suggestion unit uses information such as release dates and campaign periods to determine the priority of suggestions based on the time of product submission when making suggestions. For example, the suggestion unit can prioritize new products or limited-edition products. The suggestion unit can also prioritize products available during sales periods. Furthermore, the suggestion unit can prioritize seasonal products or event-related products. This allows the suggestion unit to determine the priority of suggestions based on the time of product submission, thereby enabling more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input product submission time data into the generation AI, which can then determine the priority of suggestions. This allows the suggestion unit to determine the priority of suggestions based on the time of product submission, thereby enabling more appropriate suggestions.

[0074] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit uses information such as common attributes and user interests to adjust the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant products based on the user's past purchase history. The suggestion unit can also prioritize suggesting highly relevant products based on the user's current areas of interest. Furthermore, the suggestion unit can prioritize suggesting highly relevant products based on the user's social media activity. This allows the suggestion unit to adjust the order of suggestions based on the relevance of products, thereby enabling more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input product relevance data into the generation AI, which can then adjust the order of suggestions. This allows the suggestion unit to adjust the order of suggestions based on the relevance of products, thereby enabling more appropriate suggestions.

[0075] The purchasing unit can estimate a user's emotions and adjust the purchasing process based on the estimated user emotions. The purchasing unit, for example, uses technologies such as facial expression recognition and voice analysis to estimate a user's emotions and adjust the purchasing process based on the estimated user emotions. For example, the purchasing unit can capture a user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The purchasing unit can also record the user's voice and estimate the emotion using voice analysis technology. This allows the purchasing unit to adjust the purchasing process based on the user's emotions. For example, if the user is relaxed, the purchasing unit can provide a purchasing process that includes a detailed confirmation procedure. If the user is in a hurry, the purchasing unit can provide a quick purchasing process. Furthermore, if the user is stressed, the purchasing unit can provide a simple and easy-to-understand purchasing process. This allows the purchasing unit to provide a more appropriate purchasing process by adjusting the purchasing process based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the purchasing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the purchasing unit may input user emotion data into the generation AI, which may then adjust the purchasing procedure. This allows the purchasing unit to provide a more appropriate purchasing procedure by adjusting the purchasing procedure according to the user's emotion.

[0076] During the purchase process, the purchasing unit can analyze the user's past purchasing behavior and select the optimal purchase process method. For example, during the purchase process, the purchasing unit uses information such as purchase frequency and purchase amount to analyze the user's past purchasing behavior and select the optimal purchase process method. For example, the purchasing unit can prioritize purchase processes that the user has used in the past. The purchasing unit can also prioritize specific payment methods based on the user's past purchasing behavior. Furthermore, the purchasing unit can prioritize specific delivery methods based on the user's past purchasing behavior. In this way, the purchasing unit can select the optimal purchase process method by analyzing the user's past purchasing behavior. Some or all of the above-described processing in the purchasing unit may be performed using or without the generation AI. For example, the purchasing unit can input the user's past purchasing behavior data into the generation AI, which can select the optimal purchase process method. In this way, the purchasing unit can select the optimal purchase process method by analyzing the user's past purchasing behavior.

[0077] The purchasing unit can customize the purchasing procedure based on the user's current living situation during the purchasing process. For example, the purchasing unit uses information such as a questionnaire or behavioral history to customize the purchasing procedure based on the user's current living situation during the purchasing process. For example, if the user is traveling, the purchasing unit can provide a method for picking up the item at the user's destination. If the user is busy, the purchasing unit can also provide a fast delivery method. Furthermore, if the user is at home, the purchasing unit can also provide a standard delivery method. This allows the purchasing unit to provide the optimal purchasing procedure based on the user's living situation. Some or all of the above-described processing in the purchasing unit may be performed using or without the generation AI. For example, the purchasing unit can input the user's living situation data into the generation AI, which can then customize the purchasing procedure. This allows the purchasing unit to provide the optimal purchasing procedure based on the user's living situation.

[0078] The purchasing unit can estimate a user's emotions and prioritize the purchase process based on the estimated user emotions. The purchasing unit, for example, uses technologies such as facial expression recognition and voice analysis to estimate a user's emotions and prioritize the purchase process based on the estimated user emotions. For example, the purchasing unit can capture a user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The purchasing unit can also record the user's voice and estimate the emotion using voice analysis technology. This allows the purchasing unit to prioritize the purchase process based on the user's emotions. For example, if the user is in a hurry, the purchasing unit can prioritize a quick purchase process. If the user is relaxed, the purchasing unit can prioritize a purchase method that includes a detailed confirmation procedure. Furthermore, if the user is stressed, the purchasing unit can prioritize a simple and easy-to-understand purchase process. This allows the purchasing unit to provide a more appropriate purchase process by prioritizing the purchase process based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the purchasing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the purchasing unit may input user emotion data into the generation AI, which may then determine the priority of the purchasing procedure. This allows the purchasing unit to provide a more appropriate purchasing procedure by determining the priority of the purchasing procedure according to the user's emotion.

[0079] The purchasing unit can select the optimal purchasing method by taking into account the user's geographical location information during the purchase process. For example, the purchasing unit uses information such as GPS data and an IP address to select the optimal purchasing method by taking into account the user's geographical location information during the purchase process. For example, if the user is in a specific area, the purchasing unit can provide delivery methods available in that area. Also, if the user is traveling, the purchasing unit can provide a method for receiving the item at the user's destination. Furthermore, if the user is at home, the purchasing unit can provide a standard delivery method. In this way, the purchasing unit can provide the optimal purchasing method by taking into account the user's geographical location information. Some or all of the above-described processing in the purchasing unit may be performed using or without the generation AI. For example, the purchasing unit can input the user's geographical location information into the generation AI, which can select the optimal purchasing method. In this way, the purchasing unit can provide the optimal purchasing method by taking into account the user's geographical location information.

[0080] During the purchase process, the purchasing unit can analyze the user's social media activity and suggest a method for the purchase process. For example, during the purchase process, the purchasing unit uses information such as the content of posts and the number of followers to analyze the user's social media activity and suggest a method for the purchase process. For example, the purchasing unit can prioritize products that the user is talking about on social media during the purchase process. The purchasing unit can also prioritize products that the user is following during the purchase process. Furthermore, the purchasing unit can prioritize products that are popular in the communities the user participates in during the purchase process. In this way, the purchasing unit can suggest a more appropriate method for the purchase process by analyzing the user's social media activity. Some or all of the above-described processing in the purchasing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the purchasing unit can input the user's social media activity data into a generation AI, which can then suggest a method for the purchase process. In this way, the purchasing unit can suggest a more appropriate method for the purchase process by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, and purchase 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 accepts product names entered by a user in a messaging app. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to suggest optimal products based on the user's needs. The purchase unit is realized by the specific processing unit 290 of the data processing device 12 and searches for products selected by the user on an e-commerce site and automatically completes the purchase procedure. The suggestion unit collects product information from the web and social networking sites using methods such as web scraping and API usage, and identifies products based on the user's needs. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, suggestion unit, and purchase unit, described above, is realized by, for example, 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 accepts a product name entered by a user in a messaging app. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to suggest optimal products based on the user's needs. The purchase unit is realized by the specific processing unit 290 of the data processing device 12 and searches for products selected by the user on an e-commerce site and automatically completes the purchase procedure. The suggestion unit collects product information from the web or SNS using methods such as web scraping or API usage, and identifies products based on the user's needs. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, and purchase 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 accepts product names entered by a user in a messaging app. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to suggest optimal products based on the user's needs. The purchase unit is realized by the specific processing unit 290 of the data processing device 12 and searches for products selected by the user on an e-commerce site and automatically completes the purchase procedure. The suggestion unit collects product information from the web and social networking sites by methods such as web scraping and API usage, and identifies products based on the user's needs. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, and purchase 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 accepts product names entered by a user in a messaging app. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to suggest optimal products based on the user's needs. The purchase unit is realized by the specific processing unit 290 of the data processing device 12 and searches for products selected by the user on an e-commerce site and automatically completes the purchase procedure. The suggestion unit collects product information from the web and social networking sites by methods such as web scraping and API usage, and identifies products based on the user's needs.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The reception unit can analyze the user's past purchase history and preferentially suggest product categories that the user frequently purchases. For example, if the user has purchased many electronic devices in the past, the reception unit can preferentially suggest products related to electronic devices. Also, if the user has a preference for a particular brand, the reception unit can preferentially suggest products from that brand. Furthermore, if the user tends to purchase specific products in a particular season, the reception unit can suggest products that are suited to that season. In this way, the reception unit can utilize the user's past purchase history to suggest more appropriate products.

[0083] The suggestion unit can suggest products taking into consideration the user's current living situation. For example, if the user is planning to move, the suggestion unit can suggest furniture and home appliances necessary for the move. Also, if the user has started a new hobby, the suggestion unit can suggest products related to that hobby. Furthermore, if the user is interested in health, the suggestion unit can suggest health foods and fitness-related products. In this way, the suggestion unit can suggest products according to the user's living situation.

[0084] The suggestion unit can estimate the user's emotions and adjust the types of products to be suggested based on the estimated emotions. For example, if the user is feeling stressed, it can suggest products with a relaxing effect. If the user is excited, it can suggest entertainment-related products. Furthermore, if the user is tired, it can suggest health foods and supplements. In this way, the suggestion unit can suggest products according to the user's emotions.

[0085] The purchasing unit can propose the optimal delivery method taking into account the user's geographical location information. For example, if the user lives in an urban area, same-day delivery or next-day delivery can be proposed. If the user lives in a rural area, standard delivery or a specific delivery company can be proposed. Furthermore, if the user is traveling, a method for receiving the product at the user's destination can be proposed. This allows the purchasing unit to propose the optimal delivery method based on the user's geographical location information.

[0086] The reception unit can analyze the user's social media activity and prioritize suggesting products that the user is interested in. For example, if the user frequently mentions a particular product on social media, the reception unit can prioritize suggesting that product. The reception unit can also suggest products introduced by influencers the user follows. Furthermore, the reception unit can suggest products that are popular in communities in which the user participates. This allows the reception unit to make product suggestions based on the user's social media activity.

[0087] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is relaxed, products can be suggested using calm expressions. If the user is excited, products can be suggested using energetic expressions. Furthermore, if the user is stressed, products can be suggested using simple and easy-to-understand expressions. This allows the suggestion unit to adjust the way suggestions are expressed based on the user's emotions.

[0088] The purchasing unit can analyze the user's past purchasing behavior and suggest the optimal payment method. For example, if the user has frequently used credit cards in the past, it can suggest payment by credit card. Also, if the user prefers to use electronic money, it can suggest payment by electronic money. Furthermore, if the user uses a specific point program, it can suggest a payment method that uses those points. This allows the purchasing unit to suggest the optimal payment method based on the user's past purchasing behavior.

[0089] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can suggest products immediately. If the user is busy, the suggestion unit can suggest products later. Furthermore, if the user is feeling stressed, the suggestion unit can suggest products during a time when the user is able to relax. This allows the suggestion unit to suggest products at the optimal timing according to the user's emotions.

[0090] The purchasing unit can propose the optimal purchasing procedure method taking into consideration the user's current living situation. For example, if the user is busy, a quick purchasing procedure can be proposed. If the user is traveling, a method for receiving the product at the user's destination can be proposed. Furthermore, if the user is at home, a regular delivery method can be proposed. In this way, the purchasing unit can propose the optimal purchasing procedure method according to the user's living situation.

[0091] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. In this way, the suggestion unit can adjust the length of the suggestions according to the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit receives a product purchase request from a user. The purchase request from the user can be received in the form of, for example, text input or voice input. Step 2: The suggestion unit analyzes the product information based on the information received by the reception unit and suggests products. Technologies such as natural language processing and image analysis are used to analyze the product information. The suggestion unit uses a generation AI to suggest optimal products based on the user's needs. For example, the generation AI can analyze the information input by the user and suggest related products using a ranking display or recommendation algorithm. Step 3: The purchasing unit searches for the products suggested by the suggestion unit on the e-commerce site and completes the purchase process. The purchase process includes steps such as adding the product to a cart and processing the payment. The purchasing unit can save the user time and effort by automatically completing the purchase process for the products selected by the user.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0095] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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, RAM 30, and 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.

[0101] 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.

[0102] 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 audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] 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 carried out in a secure state.

[0105] Fig. 4 shows an example of the 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.

[0106] 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.

[0107] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0111] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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, RAM 30, and 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.

[0117] 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.

[0118] 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 audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] 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 carried out in a secure state.

[0121] 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.

[0122] 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.

[0123] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0125] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0127] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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, RAM 30, and 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.

[0133] 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.

[0134] 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 audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] 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 carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0138] 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.

[0139] 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.

[0140] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0142] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] 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 voice indicating a user input regarding 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.

[0144] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] 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.

[0155] 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 USB (Universal Serial Bus) 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.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] 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.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0160] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 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 that receives a product purchase request from a user; a suggestion unit that analyzes product information and suggests products based on the information received by the reception unit; a purchasing unit that searches for the products suggested by the suggesting unit on an electronic commerce site and carries out a purchasing procedure. A system characterized by:

2. The proposal unit Collect product information from the web and social media, and identify products based on user needs 2. The system of claim 1.

3. The proposal unit Let the user select the product they want to purchase from the suggested products 2. The system of claim 1.

4. The purchasing department Search for the product selected by the user on an e-commerce site and automatically complete the purchase process 2. The system of claim 1.

5. The reception unit Accept product names entered by users in messaging apps 2. The system of claim 1.

6. The proposal unit Set product selection criteria based on user needs 2. The system of claim 1.

7. The reception unit To estimate a user's emotions and adjust the timing of accepting a product purchase request based on the estimated user's emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past purchase history and select the reception method 2. The system of claim 1.

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