System

Abstract

An object of a system according to an embodiment is to propose an optimal hometown tax payment based on a taste of food and a family structure of a customer.SOLUTION: A system according to an embodiment includes a customer information collection unit, a proposal generation unit, and a conversation processing unit. The customer information collection unit collects information such as food preferences and family members of the customer. The suggestion generation unit suggests a temporary hometown tax payment on the basis of the information collected by the customer information collection unit. The conversation processing unit improves the accuracy of the suggestion by allowing the generation AI and the customer to have a conversation on the basis of the suggestion generated by the suggestion generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 technologies do not adequately propose optimal hometown tax donations based on customers' food preferences and family composition, and there is room for improvement.

[0005] The system of the embodiment aims to make optimal hometown tax donation suggestions based on the customer's food preferences and family composition. [Means for solving the problem]

[0006] The system according to the embodiment includes a customer information collection unit, a proposal generation unit, and a conversation processing unit. The customer information collection unit collects information such as the customer's food preferences and family structure. The proposal generation unit makes a preliminary hometown tax donation proposal based on the information collected by the customer information collection unit. The conversation processing unit improves the accuracy of the proposal by having the customer and the generation AI have a conversation based on the proposal generated by the proposal generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal hometown tax donations based on the customer's food preferences and family composition. [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) The proposal system according to an embodiment of the present invention is a system that proposes the optimal combination of hometown tax donations and application timing (delivery timing) based on information such as the customer's food preferences and family structure. This allows the proposal system to propose hometown tax donations that best suit the customer's needs.

[0029] The proposal system according to the embodiment includes a customer information collection unit, a proposal generation unit, and a conversation processing unit. The customer information collection unit collects information such as a customer's food preferences and family structure. For example, the customer information collection unit asks the customer questions such as, "What is your favorite food?" and "How many people are in your family?" and collects the responses. The customer information collection unit can also collect the customer's past hometown tax donation history and reviews. For example, the customer information collection unit can collect the customer's past hometown tax donation history and analyze which regions and products the customer gave high ratings to. The customer information collection unit can also collect information about the customer's lifestyle and health status. For example, the customer information collection unit can collect information such as whether the customer exercises or has specific dietary restrictions, and based on that information, make healthy hometown tax donation proposals. The proposal generation unit makes initial hometown tax donation proposals based on the collected information. For example, the proposal generation unit can suggest combinations of hometown tax donations that match the customer's preferences. The proposal generation unit generates proposals using a generation AI. The generation AI analyzes customer information and makes optimal suggestions using text generation AI (e.g., LLM) or multimodal generation AI. For example, the generation AI suggests "beef from City A and fruit from Town B" based on the customer's preferences. The conversation processing unit improves the accuracy of the suggestions by having the generation AI and the customer converse based on the suggestions generated by the proposal generation unit. For example, the conversation processing unit uses the generation AI to ask the customer additional questions such as "Which cut of beef do you like?" or "Do you want to change the fruit with the seasons?" to understand the customer's detailed preferences. The generation AI improves the accuracy of the suggestions through conversation with the customer using text generation AI (e.g., LLM) or multimodal generation AI. As a result, the recommendation system according to the embodiment can make hometown tax donation suggestions that best meet the customer's needs. For example, the system can suggest optimal hometown tax donation combinations based on the customer's preferred ingredients and family composition, thereby improving customer satisfaction.

[0030] The customer information collection unit collects the customer's food preferences and family composition, as well as past hometown tax donation history and reviews, to understand the customer's preferences in more detail. The customer information collection unit, for example, collects the customer's history of hometown tax donations made in the past and analyzes which regions and products the customer has given high ratings to. For example, the customer's preferences are understood in detail based on the reviews and ratings of products for which tax has been paid in the past. The customer information collection unit also collects the customer's history of hometown tax donations made in the past and analyzes which regions and products the customer has given high ratings to. For example, the customer's preferences are understood in detail based on the reviews and ratings of products for which tax has been paid in the past. The customer information collection unit also collects the customer's history of hometown tax donations made in the past and analyzes which regions and products the customer has given high ratings to. For example, the customer's preferences are understood in detail based on the reviews and ratings of products for which tax has been paid in the past. This allows the customer's preferences to be understood in detail, making it possible to make more personalized suggestions.

[0031] The customer information collection unit also collects information about the customer's lifestyle and health condition, and can make health-conscious suggestions. The customer information collection unit, for example, collects information about the customer's lifestyle and makes health-conscious suggestions. For example, it collects information such as whether the customer exercises or has specific dietary restrictions, and makes healthy hometown tax donation suggestions based on that information. The customer information collection unit also collects information about the customer's health condition and makes health-conscious suggestions. For example, it collects information such as whether the customer has allergies or specific diseases, and makes safe hometown tax donation suggestions based on that information. The customer information collection unit also collects information about the customer's lifestyle and health condition and makes health-conscious suggestions. For example, if the customer provides the results of a health check, it makes health-conscious hometown tax donation suggestions. This makes it possible to make health-conscious suggestions based on the customer's lifestyle and health condition.

[0032] The customer information collection unit collects information using voice input or image recognition, and can provide a more intuitive interface. The customer information collection unit, for example, enables a customer to provide information using voice input. For example, a customer inputs their favorite foods or family composition by voice, and the information is automatically analyzed and stored in a database. The customer information collection unit also enables a customer to provide information using image recognition. For example, a customer uploads photos of their family, and the family composition is automatically analyzed using image recognition technology. The customer information collection unit also enables a customer to provide information using voice input or image recognition. For example, a customer inputs their favorite foods or family composition by voice, and the information is automatically analyzed and stored in a database. In this way, a more intuitive interface can be provided by using voice input or image recognition.

[0033] The customer information collection unit can link with a customer's social media account and automatically extract food preferences and family composition from the posted content. The customer information collection unit, for example, links with a customer's social media account and automatically extracts food preferences and family composition from the posted content. For example, it analyzes food photos and comments posted by the customer on social media to understand the food preferences. The customer information collection unit can also link with a customer's social media account and automatically extract food preferences and family composition from the posted content. For example, it analyzes food photos and comments posted by the customer on social media to understand the food preferences. The customer information collection unit can also link with a customer's social media account and automatically extract food preferences and family composition from the posted content. For example, it analyzes food photos and comments posted by the customer on social media to understand the food preferences. In this way, by linking with a social media account, the customer's food preferences and family composition can be automatically extracted.

[0034] The proposal generation unit can make proposals that promote ethical consumption by taking into consideration producer information and production processes of local specialty products. For example, when the generation AI makes a proposal, the proposal generation unit makes proposals that promote ethical consumption by taking into consideration producer information and production processes of local specialty products. For example, it presents a photo of the producer's face and a video of the production process. Also, when the generation AI makes a proposal, the proposal generation unit makes proposals that promote ethical consumption by taking into consideration producer information and production processes of local specialty products. For example, it presents a photo of the producer's face and a video of the production process. Also, when the generation AI makes a proposal, the proposal generation unit makes proposals that promote ethical consumption by taking into consideration producer information and production processes of local specialty products. For example, it presents a photo of the producer's face and a video of the production process. In this way, by making proposals that promote ethical consumption, it is possible to promote sustainable consumption.

[0035] The proposal generation unit can visually present the proposal content using not only text but also images and videos. For example, when the generation AI makes a proposal, the proposal generation unit visually presents it using not only text but also images and videos. For example, it displays photos and an introduction video of the proposed product. Furthermore, when the generation AI makes a proposal, the proposal generation unit visually presents it using not only text but also images and videos. For example, it displays photos and an introduction video of the proposed product. Furthermore, when the generation AI makes a proposal, the proposal generation unit visually presents it using not only text but also images and videos. For example, it displays photos and an introduction video of the proposed product. In this way, by visually presenting the proposal content, it becomes possible to make proposals that are easy for customers to understand.

[0036] The proposal generation unit can present the proposal content together with reviews and ratings from other customers, thereby increasing its credibility. For example, when the generation AI makes a proposal, the proposal generation unit presents the proposal content together with reviews and ratings from other customers, thereby increasing its credibility. For example, it displays reviews and ratings of the proposed product. Furthermore, when the generation AI makes a proposal, the proposal generation unit presents the proposal content together with reviews and ratings from other customers, thereby increasing its credibility. For example, it displays reviews and ratings of the proposed product. Furthermore, when the generation AI makes a proposal, the proposal generation unit presents the proposal content together with reviews and ratings from other customers, thereby increasing its credibility. For example, it displays reviews and ratings of the proposed product. In this way, by presenting the proposal content together with reviews and ratings from other customers, it is possible to increase its credibility.

[0037] The conversation processing unit can collect information about the customer's life events and make special suggestions. For example, the conversation processing unit collects information about life events such as birthdays and anniversaries during a conversation with the customer, and makes special suggestions based on that information. For example, it may suggest local specialties that coincide with birthdays. In addition, the conversation processing unit collects information about life events such as birthdays and anniversaries during a conversation with the customer, and makes special suggestions based on that information. For example, it may suggest local specialties that coincide with birthdays. In addition, the conversation processing unit collects information about life events such as birthdays and anniversaries during a conversation with the customer, and makes special suggestions based on that information. For example, it may suggest local specialties that coincide with birthdays. In this way, by making special suggestions based on the customer's life events, customer satisfaction can be improved.

[0038] The conversation processing unit can analyze the conversation history, track changes in the customer's preferences, and update the proposal content. For example, the generation AI analyzes the conversation history with the customer, tracks changes in the customer's preferences, and updates the proposal content. For example, it makes new proposals based on products that were preferred in past conversations. The conversation processing unit also analyzes the conversation history with the customer, tracks changes in the customer's preferences, and updates the proposal content. For example, it makes new proposals based on products that were preferred in past conversations. The conversation processing unit also analyzes the conversation history with the customer, tracks changes in the customer's preferences, and updates the proposal content. For example, it makes new proposals based on products that were preferred in past conversations. In this way, by tracking changes in the customer's preferences and updating the proposal content, more appropriate proposals can be made.

[0039] The conversation processing unit can suggest other services that the customer may be interested in. For example, the generation AI may suggest other services (travel, entertainment, etc.) that the customer may be interested in during a conversation with the customer. For example, it may suggest a travel plan related to hometown tax donations. The conversation processing unit can also suggest other services (travel, entertainment, etc.) that the customer may be interested in during a conversation with the customer. For example, it may suggest a travel plan related to hometown tax donations. The conversation processing unit can also suggest other services (travel, entertainment, etc.) that the customer may be interested in during a conversation with the customer. For example, it may suggest a travel plan related to hometown tax donations. In this way, customer satisfaction can be improved by suggesting other services that the customer may be interested in.

[0040] The conversation processing unit can re-suggest products or services in which the customer has shown interest in the past. For example, the conversation processing unit re-suggests products or services in which the customer has shown interest in the past during a conversation between the generation AI and the customer. For example, it re-suggests products that the customer has liked in the past. The conversation processing unit can also re-suggest products or services in which the customer has shown interest in the past during a conversation between the generation AI and the customer. For example, it re-suggests products that the customer has liked in the past. The conversation processing unit can also re-suggest products or services in which the customer has shown interest in the past during a conversation between the generation AI and the customer. For example, it re-suggests products that the customer has liked in the past. In this way, by re-suggesting products or services in which the customer has shown interest in the past, customer satisfaction can be improved.

[0041] The proposal generation unit takes into consideration each taxpayer's lifestyle and health condition in addition to their food preferences and family composition, and is able to make proposals that will satisfy everyone. The proposal generation unit, for example, takes into consideration each taxpayer's lifestyle and health condition in addition to their food preferences and family composition, and makes proposals that will satisfy everyone. For example, it may suggest low-calorie specialty foods to a health-conscious family. The proposal generation unit also takes into consideration each taxpayer's lifestyle and health condition in addition to their food preferences and family composition, and makes proposals that will satisfy everyone. For example, it may suggest low-calorie specialty foods to a health-conscious family. The proposal generation unit also takes into consideration each taxpayer's lifestyle and health condition in addition to their food preferences and family composition, and makes proposals that will satisfy everyone. For example, it may suggest low-calorie specialty foods to a health-conscious family. In this way, by taking into consideration each taxpayer's lifestyle and health condition, it is possible to make proposals that will satisfy everyone.

[0042] The proposal generation unit can analyze each taxpayer's past hometown tax donation history and reviews and propose the optimal combination. The proposal generation unit, for example, analyzes each taxpayer's past hometown tax donation history and reviews and proposes the optimal combination. For example, it prioritizes proposing products that have been highly rated in the past. The proposal generation unit also analyzes each taxpayer's past hometown tax donation history and reviews and proposes the optimal combination. For example, it prioritizes proposing products that have been highly rated in the past. The proposal generation unit also analyzes each taxpayer's past hometown tax donation history and reviews and proposes the optimal combination. For example, it prioritizes proposing products that have been highly rated in the past. In this way, it is possible to propose the optimal combination by analyzing each taxpayer's past hometown tax donation history and reviews.

[0043] The proposal generation unit can also suggest events and activities that the whole family can enjoy, based on the information of each taxpayer. The proposal generation unit, for example, suggests events and activities that the whole family can enjoy, based on the information of each taxpayer. For example, it suggests local events that the whole family can participate in. The proposal generation unit can also suggest events and activities that the whole family can enjoy, based on the information of each taxpayer. For example, it suggests local events that the whole family can participate in. The proposal generation unit can also suggest events and activities that the whole family can enjoy, based on the information of each taxpayer. For example, it suggests local events that the whole family can participate in. In this way, by suggesting events and activities that the whole family can enjoy, customer satisfaction can be improved.

[0044] The proposal generation unit can propose a travel plan that will satisfy the whole family based on the information of each taxpayer. The proposal generation unit, for example, proposes a travel plan that will satisfy the whole family based on the information of each taxpayer. For example, it proposes tourist spots and accommodations that the whole family can enjoy. Also, the proposal generation unit proposes a travel plan that will satisfy the whole family based on the information of each taxpayer. For example, it proposes tourist spots and accommodations that the whole family can enjoy. Also, the proposal generation unit proposes a travel plan that will satisfy the whole family based on the information of each taxpayer. For example, it proposes tourist spots and accommodations that the whole family can enjoy. In this way, by proposing a travel plan that will satisfy the whole family, customer satisfaction can be improved.

[0045] The proposal generation unit is able to propose the optimal application time by taking into consideration the quality and supply status of seasonal specialty products. The proposal generation unit, for example, proposes the optimal application time by taking into consideration the quality and supply status of seasonal specialty products. For example, since fruit is delicious in summer, it may suggest that it would be good to apply in July. The proposal generation unit also proposes the optimal application time by taking into consideration the quality and supply status of seasonal specialty products. For example, since fruit is delicious in summer, it may suggest that it would be good to apply in July. The proposal generation unit also proposes the optimal application time by taking into consideration the quality and supply status of seasonal specialty products. For example, since fruit is delicious in summer, it may suggest that it would be good to apply in July. In this way, it is possible to propose the optimal application time by taking into consideration the quality and supply status of seasonal specialty products.

[0046] The proposal generation unit can propose a delivery time that matches a customer's life event. The proposal generation unit, for example, proposes a delivery time that matches a customer's life event (birthday, anniversary, etc.). For example, delivers a local specialty product to coincide with a birthday. The proposal generation unit also proposes a delivery time that matches a customer's life event (birthday, anniversary, etc.). For example, delivers a local specialty product to coincide with a birthday. The proposal generation unit also proposes a delivery time that matches a customer's life event (birthday, anniversary, etc.). For example, delivers a local specialty product to coincide with a birthday. In this way, by proposing a delivery time that matches a customer's life event, customer satisfaction can be improved.

[0047] The proposal generation unit can also provide information about seasonal events and festivals in accordance with the time of application. The proposal generation unit can also provide information about seasonal events and festivals in accordance with the time of application, for example, providing information about summer festivals and fireworks displays. The proposal generation unit can also provide information about seasonal events and festivals in accordance with the time of application, for example, providing information about summer festivals and fireworks displays. The proposal generation unit can also provide information about seasonal events and festivals in accordance with the time of application, for example, providing information about summer festivals and fireworks displays. In this way, by providing information about seasonal events and festivals in accordance with the time of application, customer satisfaction can be improved.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The proposed system can further include a tourist information provider that provides regional tourist information. For example, it can provide information on tourist spots and events in the area where the customer is making a hometown tax donation, motivating the customer to visit the area when making the donation. The tourist information provider can also provide seasonal tourist information. For example, it can provide information on famous cherry blossom viewing spots in spring, swimming beaches in summer, autumn foliage spots in autumn, and ski resorts in winter. This allows customers to learn more about the attractions of the region through hometown tax donations, thereby contributing to regional revitalization.

[0050] The proposed system can further include a recipe providing unit that provides recipe information for local specialty products. For example, it can provide recipes for dishes using the specialty products that customers have received through hometown tax donations, allowing them to enjoy the specialty products even more. The recipe providing unit can also provide recipe information for each season. For example, it can provide recipes for cold dishes in the summer and hot dishes in the winter. This allows customers to make the most of the specialty products and improves their satisfaction with hometown tax donations.

[0051] The proposed system can further include a cultural information provider that provides information on the culture and history of the region. For example, it can provide information on traditional events and historical background of the region where the customer is making the hometown tax donation, helping the customer understand the culture of the region when making the tax donation. The cultural information provider can also provide information on the manufacturing process of traditional crafts and specialty products of the region. This allows customers to come into contact with the culture and history of the region through hometown tax donations, deepening their attachment to the region.

[0052] The proposed system can further include a storage information provider that provides information on storage methods and expiration dates for local specialty products. For example, the system can provide advice on optimal storage methods and expiration dates for specialty products received by customers through hometown tax donations, and on how to enjoy the products for as long as possible. The storage information provider can also provide information on freezing and processing methods for specialty products. This allows customers to use the specialty products without waste, improving satisfaction with hometown tax donations.

[0053] The proposal system can further include a nutrition information provider that provides nutritional information on local specialty products. For example, the system can provide information on the nutritional components and health benefits of the specialty products that customers receive through hometown tax donations, thereby promoting the appeal of the specialty products to health-conscious customers. The nutrition information provider can also provide information on healthy recipes and meal plans using the specialty products. This allows customers to enjoy the specialty products in a healthy way, improving satisfaction with hometown tax donations.

[0054] The proposed system can also be equipped with an exchange promotion section that encourages interaction with producers of local specialty products. For example, online exchange events could be held with producers in the area where customers make hometown tax donations, providing an opportunity to hear directly about the appeal of the specialty products and the producers' thoughts. The exchange promotion section could also broadcast interview videos of producers and live production sites. This allows customers to learn the stories behind the specialty products and deepen their understanding and empathy for hometown tax donations.

[0055] The recommendation system can further include a gift suggestion unit that makes gift suggestions for local specialty products. For example, the system can make suggestions for giving the specialty products received through hometown tax donations as gifts, helping to spread the appeal of the specialty products. The gift suggestion unit can also provide information on gift sets using the specialty products and wrapping methods. This allows customers to use the specialty products as gifts, improving satisfaction with hometown tax donations.

[0056] The proposed system can further include an allergy information provider that provides allergy information for local specialty products. For example, it can provide information about allergens contained in specialty products that customers receive through hometown tax donations, helping customers with allergies make safe choices. The allergy information provider can also provide information about the ingredient labeling of specialty products and allergy-friendly recipes. This allows customers to enjoy specialty products with peace of mind, improving satisfaction with hometown tax donations.

[0057] The proposed system can further include an eco-information section that highlights the ecological aspects of local specialties. For example, it can provide information on how the specialties received through hometown tax donations are produced with consideration for the environment, promoting ecological consumption. The eco-information section can also provide information on the renewable energy and organic farming methods used in the production process of the specialties. This allows customers to make environmentally friendly choices and improves their satisfaction with hometown tax donations.

[0058] The proposed system can further include a storage information provider that provides information on storage methods and expiration dates for local specialty products. For example, the system can provide advice on optimal storage methods and expiration dates for specialty products received by customers through hometown tax donations, and on how to enjoy the products for as long as possible. The storage information provider can also provide information on freezing and processing methods for specialty products. This allows customers to use the specialty products without waste, improving satisfaction with hometown tax donations.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The customer information collection department collects information such as the customer's food preferences and family structure. For example, it asks the customer questions such as "What is your favorite food?" and "How many people are in your family?" and collects their answers. It also collects information on the customer's past hometown tax donation history and reviews, as well as information on their lifestyle and health. For example, it collects the customer's hometown tax donation history and analyzes which regions and products they gave high ratings to. It also collects information such as whether the customer exercises or has specific dietary restrictions, and uses this information to make healthy hometown tax donation suggestions. Step 2: The proposal generation unit makes a primary hometown tax donation proposal based on the collected information. For example, it proposes a combination of hometown tax donations that suits the customer's preferences. The proposal generation unit uses generation AI to generate proposals, and uses text generation AI (e.g., LLM) or multimodal generation AI to analyze customer information and make the optimal proposal. For example, the generation AI might suggest "beef from city A and fruit from town B" based on the customer's preferences. Step 3: The conversation processing unit improves the accuracy of the proposals by having the generation AI and the customer converse based on the proposals generated by the proposal generation unit. For example, the generation AI may ask the customer additional questions such as "Which cut of beef do you like?" or "Do you like to change the fruit with the seasons?" to understand the customer's detailed preferences. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to improve the accuracy of the proposals through conversation with the customer.

[0061] (Example 2) The proposal system according to an embodiment of the present invention is a system that proposes the optimal combination of hometown tax donations and application timing (delivery timing) based on information such as the customer's food preferences and family structure. This allows the proposal system to propose hometown tax donations that best suit the customer's needs.

[0062] The proposal system according to the embodiment includes a customer information collection unit, a proposal generation unit, and a conversation processing unit. The customer information collection unit collects information such as a customer's food preferences and family structure. For example, the customer information collection unit asks the customer questions such as, "What is your favorite food?" and "How many people are in your family?" and collects the responses. The customer information collection unit can also collect the customer's past hometown tax donation history and reviews. For example, the customer information collection unit can collect the customer's past hometown tax donation history and analyze which regions and products the customer gave high ratings to. The customer information collection unit can also collect information about the customer's lifestyle and health status. For example, the customer information collection unit can collect information such as whether the customer exercises or has specific dietary restrictions, and based on that information, make healthy hometown tax donation proposals. The proposal generation unit makes initial hometown tax donation proposals based on the collected information. For example, the proposal generation unit can suggest combinations of hometown tax donations that match the customer's preferences. The proposal generation unit generates proposals using a generation AI. The generation AI analyzes customer information and makes optimal suggestions using text generation AI (e.g., LLM) or multimodal generation AI. For example, the generation AI suggests "beef from City A and fruit from Town B" based on the customer's preferences. The conversation processing unit improves the accuracy of the suggestions by having the generation AI and the customer converse based on the suggestions generated by the proposal generation unit. For example, the conversation processing unit uses the generation AI to ask the customer additional questions such as "Which cut of beef do you like?" or "Do you want to change the fruit with the seasons?" to understand the customer's detailed preferences. The generation AI improves the accuracy of the suggestions through conversation with the customer using text generation AI (e.g., LLM) or multimodal generation AI. As a result, the recommendation system according to the embodiment can make hometown tax donation suggestions that best meet the customer's needs. For example, the system can suggest optimal hometown tax donation combinations based on the customer's preferred ingredients and family composition, thereby improving customer satisfaction.

[0063] The customer information collection unit collects the customer's food preferences and family composition, as well as past hometown tax donation history and reviews, to understand the customer's preferences in more detail. The customer information collection unit, for example, collects the customer's history of hometown tax donations made in the past and analyzes which regions and products the customer has given high ratings to. For example, the customer's preferences are understood in detail based on the reviews and ratings of products for which tax has been paid in the past. The customer information collection unit also collects the customer's history of hometown tax donations made in the past and analyzes which regions and products the customer has given high ratings to. For example, the customer's preferences are understood in detail based on the reviews and ratings of products for which tax has been paid in the past. The customer information collection unit also collects the customer's history of hometown tax donations made in the past and analyzes which regions and products the customer has given high ratings to. For example, the customer's preferences are understood in detail based on the reviews and ratings of products for which tax has been paid in the past. This allows the customer's preferences to be understood in detail, making it possible to make more personalized suggestions.

[0064] The customer information collection unit also collects information about the customer's lifestyle and health condition, and can make health-conscious suggestions. The customer information collection unit, for example, collects information about the customer's lifestyle and makes health-conscious suggestions. For example, it collects information such as whether the customer exercises or has specific dietary restrictions, and makes healthy hometown tax donation suggestions based on that information. The customer information collection unit also collects information about the customer's health condition and makes health-conscious suggestions. For example, it collects information such as whether the customer has allergies or specific diseases, and makes safe hometown tax donation suggestions based on that information. The customer information collection unit also collects information about the customer's lifestyle and health condition and makes health-conscious suggestions. For example, if the customer provides the results of a health check, it makes health-conscious hometown tax donation suggestions. This makes it possible to make health-conscious suggestions based on the customer's lifestyle and health condition.

[0065] The customer information collection unit uses the emotion estimation function to analyze the emotion of a customer when answering a question and generate questions that elicit positive emotions. The customer information collection unit, for example, analyzes the facial expression and voice of a customer when answering a question to estimate the emotion. For example, if a customer answers with a smile, the emotion is determined to be positive and a question that elicits even more positive emotions is generated. The customer information collection unit also analyzes the facial expression and voice of a customer when answering a question to estimate the emotion. For example, if a customer answers with a smile, the emotion is determined to be positive and a question that elicits even more positive emotions is generated. The customer information collection unit also analyzes the facial expression and voice of a customer when answering a question to estimate the emotion. For example, if a customer answers with a smile, the emotion is determined to be positive and a question that elicits even more positive emotions is generated. In this way, by analyzing the customer's emotion and generating questions that elicit positive emotions, customer satisfaction can be improved.

[0066] The customer information collection unit collects information using voice input or image recognition, and can provide a more intuitive interface. The customer information collection unit, for example, enables a customer to provide information using voice input. For example, a customer inputs their favorite foods or family composition by voice, and the information is automatically analyzed and stored in a database. The customer information collection unit also enables a customer to provide information using image recognition. For example, a customer uploads photos of their family, and the family composition is automatically analyzed using image recognition technology. The customer information collection unit also enables a customer to provide information using voice input or image recognition. For example, a customer inputs their favorite foods or family composition by voice, and the information is automatically analyzed and stored in a database. In this way, a more intuitive interface can be provided by using voice input or image recognition.

[0067] The customer information collection unit can link with a customer's social media account and automatically extract food preferences and family composition from the posted content. The customer information collection unit, for example, links with a customer's social media account and automatically extracts food preferences and family composition from the posted content. For example, it analyzes food photos and comments posted by the customer on social media to understand the food preferences. The customer information collection unit can also link with a customer's social media account and automatically extract food preferences and family composition from the posted content. For example, it analyzes food photos and comments posted by the customer on social media to understand the food preferences. The customer information collection unit can also link with a customer's social media account and automatically extract food preferences and family composition from the posted content. For example, it analyzes food photos and comments posted by the customer on social media to understand the food preferences. In this way, by linking with a social media account, the customer's food preferences and family composition can be automatically extracted.

[0068] The customer information collection unit uses the emotion estimation function to monitor the emotions of customers when they answer questions in real time, thereby improving the accuracy of answers. The customer information collection unit, for example, analyzes the facial expressions and voice of customers when they answer questions in real time to estimate their emotions. For example, if a customer answers with a smile, the emotion is determined to be positive, and a question that elicits a more positive emotion is generated. The customer information collection unit also analyzes the facial expressions and voice of customers when they answer questions in real time to estimate their emotions. For example, if a customer answers with a smile, the emotion is determined to be positive, and a question that elicits a more positive emotion is generated. The customer information collection unit also analyzes the facial expressions and voice of customers when they answer questions in real time to estimate their emotions. For example, if a customer answers with a smile, the emotion is determined to be positive, and a question that elicits a more positive emotion is generated. In this way, by monitoring customer emotions in real time, the accuracy of answers can be improved.

[0069] The proposal generation unit can make proposals that promote ethical consumption by taking into consideration producer information and production processes of local specialty products. For example, when the generation AI makes a proposal, the proposal generation unit makes proposals that promote ethical consumption by taking into consideration producer information and production processes of local specialty products. For example, it presents a photo of the producer's face and a video of the production process. Also, when the generation AI makes a proposal, the proposal generation unit makes proposals that promote ethical consumption by taking into consideration producer information and production processes of local specialty products. For example, it presents a photo of the producer's face and a video of the production process. Also, when the generation AI makes a proposal, the proposal generation unit makes proposals that promote ethical consumption by taking into consideration producer information and production processes of local specialty products. For example, it presents a photo of the producer's face and a video of the production process. In this way, by making proposals that promote ethical consumption, it is possible to promote sustainable consumption.

[0070] The proposal generation unit can use the emotion estimation function to analyze the customer's emotional response to the proposal content and make a proposal that elicits a positive response. For example, when the generation AI makes a proposal, the proposal generation unit uses the emotion estimation function to analyze the customer's emotional response to the proposal content and make a proposal that elicits a positive response. For example, it prioritizes proposing products that customers will react to with a smile. Furthermore, when the generation AI makes a proposal, the proposal generation unit uses the emotion estimation function to analyze the customer's emotional response to the proposal content and make a proposal that elicits a positive response. For example, it prioritizes proposing products that customers will react to with a smile. Furthermore, when the generation AI makes a proposal, the proposal generation unit uses the emotion estimation function to analyze the customer's emotional response to the proposal content and make a proposal that elicits a positive response. For example, it prioritizes proposing products that customers will react to with a smile. In this way, by analyzing the customer's emotional response and making a proposal that elicits a positive response, customer satisfaction can be improved.

[0071] The proposal generation unit can visually present the proposal content using not only text but also images and videos. For example, when the generation AI makes a proposal, the proposal generation unit visually presents it using not only text but also images and videos. For example, it displays photos and an introduction video of the proposed product. Furthermore, when the generation AI makes a proposal, the proposal generation unit visually presents it using not only text but also images and videos. For example, it displays photos and an introduction video of the proposed product. Furthermore, when the generation AI makes a proposal, the proposal generation unit visually presents it using not only text but also images and videos. For example, it displays photos and an introduction video of the proposed product. In this way, by visually presenting the proposal content, it becomes possible to make proposals that are easy for customers to understand.

[0072] The proposal generation unit can present the proposal content together with reviews and ratings from other customers, thereby increasing its credibility. For example, when the generation AI makes a proposal, the proposal generation unit presents the proposal content together with reviews and ratings from other customers, thereby increasing its credibility. For example, it displays reviews and ratings of the proposed product. Furthermore, when the generation AI makes a proposal, the proposal generation unit presents the proposal content together with reviews and ratings from other customers, thereby increasing its credibility. For example, it displays reviews and ratings of the proposed product. Furthermore, when the generation AI makes a proposal, the proposal generation unit presents the proposal content together with reviews and ratings from other customers, thereby increasing its credibility. For example, it displays reviews and ratings of the proposed product. In this way, by presenting the proposal content together with reviews and ratings from other customers, it is possible to increase its credibility.

[0073] The proposal generation unit uses the emotion estimation function to monitor the customer's emotional response to the proposal content in real time, thereby improving the accuracy of the proposal. For example, when the generation AI makes a proposal, the proposal generation unit uses the emotion estimation function to monitor the customer's emotional response to the proposal content in real time, thereby improving the accuracy of the proposal. For example, it prioritizes proposing products that customers react to with a smile. Furthermore, when the generation AI makes a proposal, the proposal generation unit uses the emotion estimation function to monitor the customer's emotional response to the proposal content in real time, thereby improving the accuracy of the proposal. For example, it prioritizes proposing products that customers react to with a smile. Furthermore, when the generation AI makes a proposal, the proposal generation unit uses the emotion estimation function to monitor the customer's emotional response to the proposal content in real time, thereby improving the accuracy of the proposal. For example, it prioritizes proposing products that customers react to with a smile. In this way, by monitoring the customer's emotional response in real time, the accuracy of the proposal can be improved.

[0074] The conversation processing unit can collect information about the customer's life events and make special suggestions. For example, the conversation processing unit collects information about life events such as birthdays and anniversaries during a conversation with the customer, and makes special suggestions based on that information. For example, it may suggest local specialties that coincide with birthdays. In addition, the conversation processing unit collects information about life events such as birthdays and anniversaries during a conversation with the customer, and makes special suggestions based on that information. For example, it may suggest local specialties that coincide with birthdays. In addition, the conversation processing unit collects information about life events such as birthdays and anniversaries during a conversation with the customer, and makes special suggestions based on that information. For example, it may suggest local specialties that coincide with birthdays. In this way, by making special suggestions based on the customer's life events, customer satisfaction can be improved.

[0075] The conversation processing unit can analyze the conversation history, track changes in the customer's preferences, and update the proposal content. For example, the generation AI analyzes the conversation history with the customer, tracks changes in the customer's preferences, and updates the proposal content. For example, it makes new proposals based on products that were preferred in past conversations. The conversation processing unit also analyzes the conversation history with the customer, tracks changes in the customer's preferences, and updates the proposal content. For example, it makes new proposals based on products that were preferred in past conversations. The conversation processing unit also analyzes the conversation history with the customer, tracks changes in the customer's preferences, and updates the proposal content. For example, it makes new proposals based on products that were preferred in past conversations. In this way, by tracking changes in the customer's preferences and updating the proposal content, more appropriate proposals can be made.

[0076] The conversation processing unit can use the emotion estimation function to analyze the emotions of a customer during a conversation and generate questions that elicit positive emotions. In the conversation processing unit, for example, the generation AI analyzes the customer's facial expressions and voice during a conversation and estimates their emotions. For example, if a customer is smiling while speaking, it determines that their emotions are positive and generates questions that elicit even more positive emotions. In addition, the conversation processing unit uses the generation AI to analyze the customer's facial expressions and voice during a conversation and estimate their emotions. For example, if a customer is smiling while speaking, it determines that their emotions are positive and generates questions that elicit even more positive emotions. In addition, the conversation processing unit uses the generation AI to analyze the customer's facial expressions and voice during a conversation and estimate their emotions. For example, if a customer is smiling while speaking, it determines that their emotions are positive and generates questions that elicit even more positive emotions. In this way, by analyzing the customer's emotions and generating questions that elicit positive emotions, customer satisfaction can be improved.

[0077] The conversation processing unit can suggest other services that the customer may be interested in. For example, the generation AI may suggest other services (travel, entertainment, etc.) that the customer may be interested in during a conversation with the customer. For example, it may suggest a travel plan related to hometown tax donations. The conversation processing unit can also suggest other services (travel, entertainment, etc.) that the customer may be interested in during a conversation with the customer. For example, it may suggest a travel plan related to hometown tax donations. The conversation processing unit can also suggest other services (travel, entertainment, etc.) that the customer may be interested in during a conversation with the customer. For example, it may suggest a travel plan related to hometown tax donations. In this way, customer satisfaction can be improved by suggesting other services that the customer may be interested in.

[0078] The conversation processing unit can re-suggest products or services in which the customer has shown interest in the past. For example, the conversation processing unit re-suggests products or services in which the customer has shown interest in the past during a conversation between the generation AI and the customer. For example, it re-suggests products that the customer has liked in the past. The conversation processing unit can also re-suggest products or services in which the customer has shown interest in the past during a conversation between the generation AI and the customer. For example, it re-suggests products that the customer has liked in the past. The conversation processing unit can also re-suggest products or services in which the customer has shown interest in the past during a conversation between the generation AI and the customer. For example, it re-suggests products that the customer has liked in the past. In this way, by re-suggesting products or services in which the customer has shown interest in the past, customer satisfaction can be improved.

[0079] The proposal generation unit takes into consideration each taxpayer's lifestyle and health condition in addition to their food preferences and family composition, and is able to make proposals that will satisfy everyone. The proposal generation unit, for example, takes into consideration each taxpayer's lifestyle and health condition in addition to their food preferences and family composition, and makes proposals that will satisfy everyone. For example, it may suggest low-calorie specialty foods to a health-conscious family. The proposal generation unit also takes into consideration each taxpayer's lifestyle and health condition in addition to their food preferences and family composition, and makes proposals that will satisfy everyone. For example, it may suggest low-calorie specialty foods to a health-conscious family. The proposal generation unit also takes into consideration each taxpayer's lifestyle and health condition in addition to their food preferences and family composition, and makes proposals that will satisfy everyone. For example, it may suggest low-calorie specialty foods to a health-conscious family. In this way, by taking into consideration each taxpayer's lifestyle and health condition, it is possible to make proposals that will satisfy everyone.

[0080] The proposal generation unit can analyze each taxpayer's past hometown tax donation history and reviews and propose the optimal combination. The proposal generation unit, for example, analyzes each taxpayer's past hometown tax donation history and reviews and proposes the optimal combination. For example, it prioritizes proposing products that have been highly rated in the past. The proposal generation unit also analyzes each taxpayer's past hometown tax donation history and reviews and proposes the optimal combination. For example, it prioritizes proposing products that have been highly rated in the past. The proposal generation unit also analyzes each taxpayer's past hometown tax donation history and reviews and proposes the optimal combination. For example, it prioritizes proposing products that have been highly rated in the past. In this way, it is possible to propose the optimal combination by analyzing each taxpayer's past hometown tax donation history and reviews.

[0081] The proposal generation unit can use the emotion estimation function to analyze the emotions of each taxpayer and make proposals that will make everyone feel positive. The proposal generation unit, for example, analyzes the emotions of each taxpayer and makes proposals that will make everyone feel positive. For example, it prioritizes proposing products that will make all family members react with a smile. The proposal generation unit also analyzes the emotions of each taxpayer and makes proposals that will make everyone feel positive. For example, it prioritizes proposing products that will make all family members react with a smile. The proposal generation unit also analyzes the emotions of each taxpayer and makes proposals that will make everyone feel positive. For example, it prioritizes proposing products that will make all family members react with a smile. In this way, by analyzing the emotions of each taxpayer and making proposals that will make everyone feel positive, it is possible to improve customer satisfaction.

[0082] The proposal generation unit can also suggest events and activities that the whole family can enjoy, based on the information of each taxpayer. The proposal generation unit, for example, suggests events and activities that the whole family can enjoy, based on the information of each taxpayer. For example, it suggests local events that the whole family can participate in. The proposal generation unit can also suggest events and activities that the whole family can enjoy, based on the information of each taxpayer. For example, it suggests local events that the whole family can participate in. The proposal generation unit can also suggest events and activities that the whole family can enjoy, based on the information of each taxpayer. For example, it suggests local events that the whole family can participate in. In this way, by suggesting events and activities that the whole family can enjoy, customer satisfaction can be improved.

[0083] The proposal generation unit can propose a travel plan that will satisfy the whole family based on the information of each taxpayer. The proposal generation unit, for example, proposes a travel plan that will satisfy the whole family based on the information of each taxpayer. For example, it proposes tourist spots and accommodations that the whole family can enjoy. Also, the proposal generation unit proposes a travel plan that will satisfy the whole family based on the information of each taxpayer. For example, it proposes tourist spots and accommodations that the whole family can enjoy. Also, the proposal generation unit proposes a travel plan that will satisfy the whole family based on the information of each taxpayer. For example, it proposes tourist spots and accommodations that the whole family can enjoy. In this way, by proposing a travel plan that will satisfy the whole family, customer satisfaction can be improved.

[0084] The proposal generation unit can use the emotion estimation function to monitor the emotions of each taxpayer in real time and make optimal proposals. The proposal generation unit, for example, monitors the emotions of each taxpayer in real time and makes optimal proposals. For example, it prioritizes proposing products that all family members will react to with a smile. The proposal generation unit can also monitor the emotions of each taxpayer in real time and make optimal proposals. For example, it prioritizes proposing products that all family members will react to with a smile. The proposal generation unit can also monitor the emotions of each taxpayer in real time and make optimal proposals. For example, it prioritizes proposing products that all family members will react to with a smile. In this way, it is possible to make optimal proposals by monitoring the emotions of each taxpayer in real time.

[0085] The proposal generation unit is able to propose the optimal application time by taking into consideration the quality and supply status of seasonal specialty products. The proposal generation unit, for example, proposes the optimal application time by taking into consideration the quality and supply status of seasonal specialty products. For example, since fruit is delicious in summer, it may suggest that it would be good to apply in July. The proposal generation unit also proposes the optimal application time by taking into consideration the quality and supply status of seasonal specialty products. For example, since fruit is delicious in summer, it may suggest that it would be good to apply in July. The proposal generation unit also proposes the optimal application time by taking into consideration the quality and supply status of seasonal specialty products. For example, since fruit is delicious in summer, it may suggest that it would be good to apply in July. In this way, it is possible to propose the optimal application time by taking into consideration the quality and supply status of seasonal specialty products.

[0086] The proposal generation unit can propose a delivery time that matches a customer's life event. The proposal generation unit, for example, proposes a delivery time that matches a customer's life event (birthday, anniversary, etc.). For example, delivers a local specialty product to coincide with a birthday. The proposal generation unit also proposes a delivery time that matches a customer's life event (birthday, anniversary, etc.). For example, delivers a local specialty product to coincide with a birthday. The proposal generation unit also proposes a delivery time that matches a customer's life event (birthday, anniversary, etc.). For example, delivers a local specialty product to coincide with a birthday. In this way, by proposing a delivery time that matches a customer's life event, customer satisfaction can be improved.

[0087] The proposal generation unit can use the emotion estimation function to identify the time when the customer feels the most positive emotions and make proposals tailored to that time. The proposal generation unit, for example, uses the emotion estimation function to identify the time when the customer feels the most positive emotions and make proposals tailored to that time. For example, delivers local specialties when the customer feels the happiest. The proposal generation unit can also use the emotion estimation function to identify the time when the customer feels the most positive emotions and make proposals tailored to that time. For example, delivers local specialties when the customer feels the happiest. The proposal generation unit can also use the emotion estimation function to identify the time when the customer feels the most positive emotions and make proposals tailored to that time. For example, delivers local specialties when the customer feels the happiest. In this way, by identifying the time when the customer feels the most positive emotions and making proposals tailored to that time, customer satisfaction can be improved.

[0088] The proposal generation unit can also provide information about seasonal events and festivals in accordance with the time of application. The proposal generation unit can also provide information about seasonal events and festivals in accordance with the time of application, for example, providing information about summer festivals and fireworks displays. The proposal generation unit can also provide information about seasonal events and festivals in accordance with the time of application, for example, providing information about summer festivals and fireworks displays. The proposal generation unit can also provide information about seasonal events and festivals in accordance with the time of application, for example, providing information about summer festivals and fireworks displays. In this way, by providing information about seasonal events and festivals in accordance with the time of application, customer satisfaction can be improved.

[0089] The proposal generation unit uses the emotion estimation function to monitor in real time when the customer is feeling the most positive emotions and make optimal proposals. The proposal generation unit, for example, uses the emotion estimation function to monitor in real time when the customer is feeling the most positive emotions and make optimal proposals. For example, it delivers local specialties when the customer is feeling the most happy. The proposal generation unit also uses the emotion estimation function to monitor in real time when the customer is feeling the most positive emotions and make optimal proposals. For example, it delivers local specialties when the customer is feeling the most happy. The proposal generation unit also uses the emotion estimation function to monitor in real time when the customer is feeling the most positive emotions and make optimal proposals. For example, it delivers local specialties when the customer is feeling the most happy. In this way, by monitoring in real time when the customer is feeling the most positive emotions and making optimal proposals, customer satisfaction can be improved.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The proposed system can further include a tourist information provider that provides regional tourist information. For example, it can provide information on tourist spots and events in the area where the customer is making a hometown tax donation, motivating the customer to visit the area when making the donation. The tourist information provider can also provide seasonal tourist information. For example, it can provide information on famous cherry blossom viewing spots in spring, swimming beaches in summer, autumn foliage spots in autumn, and ski resorts in winter. This allows customers to learn more about the attractions of the region through hometown tax donations, thereby contributing to regional revitalization.

[0092] The proposed system can further include a recipe providing unit that provides recipe information for local specialty products. For example, it can provide recipes for dishes using the specialty products that customers have received through hometown tax donations, allowing them to enjoy the specialty products even more. The recipe providing unit can also provide recipe information for each season. For example, it can provide recipes for cold dishes in the summer and hot dishes in the winter. This allows customers to make the most of the specialty products and improves their satisfaction with hometown tax donations.

[0093] The proposed system can further include a cultural information provider that provides information on the culture and history of the region. For example, it can provide information on traditional events and historical background of the region where the customer is making the hometown tax donation, helping the customer understand the culture of the region when making the tax donation. The cultural information provider can also provide information on the manufacturing process of traditional crafts and specialty products of the region. This allows customers to come into contact with the culture and history of the region through hometown tax donations, deepening their attachment to the region.

[0094] The proposed system can further include a storage information provider that provides information on storage methods and expiration dates for local specialty products. For example, the system can provide advice on optimal storage methods and expiration dates for specialty products received by customers through hometown tax donations, and on how to enjoy the products for as long as possible. The storage information provider can also provide information on freezing and processing methods for specialty products. This allows customers to use the specialty products without waste, improving satisfaction with hometown tax donations.

[0095] The proposal system can further include a nutrition information provider that provides nutritional information on local specialty products. For example, the system can provide information on the nutritional components and health benefits of the specialty products that customers receive through hometown tax donations, thereby promoting the appeal of the specialty products to health-conscious customers. The nutrition information provider can also provide information on healthy recipes and meal plans using the specialty products. This allows customers to enjoy the specialty products in a healthy way, improving satisfaction with hometown tax donations.

[0096] The proposed system can also be equipped with an exchange promotion section that encourages interaction with producers of local specialty products. For example, online exchange events could be held with producers in the area where customers make hometown tax donations, providing an opportunity to hear directly about the appeal of the specialty products and the producers' thoughts. The exchange promotion section could also broadcast interview videos of producers and live production sites. This allows customers to learn the stories behind the specialty products and deepen their understanding and empathy for hometown tax donations.

[0097] The recommendation system can further include a gift suggestion unit that makes gift suggestions for local specialty products. For example, the system can make suggestions for giving the specialty products received through hometown tax donations as gifts, helping to spread the appeal of the specialty products. The gift suggestion unit can also provide information on gift sets using the specialty products and wrapping methods. This allows customers to use the specialty products as gifts, improving satisfaction with hometown tax donations.

[0098] The proposed system can further include an allergy information provider that provides allergy information for local specialty products. For example, it can provide information about allergens contained in specialty products that customers receive through hometown tax donations, helping customers with allergies make safe choices. The allergy information provider can also provide information about the ingredient labeling of specialty products and allergy-friendly recipes. This allows customers to enjoy specialty products with peace of mind, improving satisfaction with hometown tax donations.

[0099] The proposed system can further include an eco-information section that highlights the ecological aspects of local specialties. For example, it can provide information on how the specialties received through hometown tax donations are produced with consideration for the environment, promoting ecological consumption. The eco-information section can also provide information on the renewable energy and organic farming methods used in the production process of the specialties. This allows customers to make environmentally friendly choices and improves their satisfaction with hometown tax donations.

[0100] The proposed system can further include a storage information provider that provides information on storage methods and expiration dates for local specialty products. For example, the system can provide advice on optimal storage methods and expiration dates for specialty products received by customers through hometown tax donations, and on how to enjoy the products for as long as possible. The storage information provider can also provide information on freezing and processing methods for specialty products. This allows customers to use the specialty products without waste, improving satisfaction with hometown tax donations.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The customer information collection department collects information such as the customer's food preferences and family structure. For example, it asks the customer questions such as "What is your favorite food?" and "How many people are in your family?" and collects their answers. It also collects information on the customer's past hometown tax donation history and reviews, as well as information on their lifestyle and health. For example, it collects the customer's hometown tax donation history and analyzes which regions and products they gave high ratings to. It also collects information such as whether the customer exercises or has specific dietary restrictions, and uses this information to make healthy hometown tax donation suggestions. Step 2: The proposal generation unit makes a primary hometown tax donation proposal based on the collected information. For example, it proposes a combination of hometown tax donations that suits the customer's preferences. The proposal generation unit uses generation AI to generate proposals, and uses text generation AI (e.g., LLM) or multimodal generation AI to analyze customer information and make the optimal proposal. For example, the generation AI might suggest "beef from city A and fruit from town B" based on the customer's preferences. Step 3: The conversation processing unit improves the accuracy of the proposals by having the generation AI and the customer converse based on the proposals generated by the proposal generation unit. For example, the generation AI may ask the customer additional questions such as "Which cut of beef do you like?" or "Do you like to change the fruit with the seasons?" to understand the customer's detailed preferences. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to improve the accuracy of the proposals through conversation with the customer.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 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 customer information collection department that collects information such as customers' food preferences and family structure; a proposal generation unit that makes a primary hometown tax donation proposal based on the information collected by the customer information collection unit; and a conversation processing unit that enhances the accuracy of the proposal by having the generation AI and the customer have a conversation based on the proposal generated by the proposal generation unit. A system characterized by:

2. The customer information collection unit In addition to the customer's food preferences and family structure, past hometown tax donation history and reviews are collected to understand more detailed preferences.

2. The system of claim 1.

3. The customer information collection unit It also collects information about the customer's lifestyle and health status and makes health-conscious suggestions.

2. The system of claim 1.

4. The customer information collection unit Analyze the emotions of the customer when answering questions and generate questions that elicit positive emotions 2. The system of claim 1.

5. The customer information collection unit Use voice input and image recognition to collect this information and provide a more intuitive interface 2. The system of claim 1.

Images