System

Abstract

An object of a system according to an embodiment is to propose management of a disused article and an optimal purchase timing of a desired article.SOLUTION: A system includes a disuse management part, a purchase optimization part, a proposal part, and a payment part. The disused article management part proposes the timing and destination of selling, abandoning and abandoning the disused article. The purchase optimization unit proposes an optimal purchase timing or purchase source of a desired object. The proposal part notifies the user of the contents proposed by the disuse management part and the purchase optimization part. The payment part performs cashless sales and purchase of the disused article.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 manage unwanted items or suggest optimal purchasing times, so there is room for improvement.

[0005] The system according to the embodiment aims to manage unwanted items and suggest the best time to purchase desired items. [Means for solving the problem]

[0006] The system according to the embodiment includes an unwanted item management unit, a purchase optimization unit, a suggestion unit, and a payment unit. The unwanted item management unit suggests the timing and destination for selling, discarding, or giving away unwanted items. The purchase optimization unit suggests the optimal timing and place to purchase desired items. The suggestion unit notifies the user of the suggestions made by the unwanted item management unit and the purchase optimization unit. The payment unit sells, buys, sells, or purchases unwanted items cashlessly. [Effects of the Invention]

[0007] The system according to the embodiment can manage unwanted items and suggest the best time to purchase desired items. [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 unwanted item management system according to an embodiment of the present invention is a system that supports the management of unwanted items in the home and the optimization of purchases. This system uses AI to suggest the timing and destination for selling, discarding, or donating unwanted items, as well as the optimal timing and place to purchase desired items. This reduces the effort required for tidying up the home and promotes the effective use of unwanted items. Furthermore, by using PayPay, the system can expand the economic sphere and contribute to the SDGs and donation activities.

[0029] The unwanted item management system according to the embodiment includes an unwanted item management unit, a purchase optimization unit, a suggestion unit, and a payment unit. The unwanted item management unit suggests the timing and destination for selling, discarding, or donating unwanted items. For example, the generation AI suggests the optimal time to sell or donate unwanted items based on information about the unwanted items entered by the user. The unwanted item management unit can also suggest destinations such as recycle shops and donation sites. The purchase optimization unit suggests the optimal time and place to purchase desired items. For example, the generation AI analyzes sale periods and inventory status based on information about the desired items entered by the user and suggests the optimal purchase timing. The purchase optimization unit can also suggest purchasing locations such as online shops and physical stores. The suggestion unit notifies the user of the suggestions made by the unwanted item management unit and the purchase optimization unit. For example, the generation AI sends a notification to the user's smartphone and displays the suggestions. The suggestion unit can also notify the user of the suggestions via email or in-app message. The payment unit enables cashless buying, selling, and purchasing of unwanted items. For example, the user can use PayPay to pay at a recycle shop. The payment section also allows users to use PayPay when making purchases at online stores, which helps manage unwanted household items and optimize purchases, expanding the economic sphere and contributing to SDGs and donation activities.

[0030] The unwanted item management unit can make management suggestions for unwanted items by taking into account the user's lifestyle patterns and seasonal fluctuations. The unwanted item management unit, for example, uses a generation AI to analyze the user's lifestyle patterns and make management suggestions for unwanted items by season. For example, the generation AI may suggest the best time to sell winter clothes in the spring. The unwanted item management unit also analyzes the user's lifestyle rhythm and makes management suggestions for unwanted items that coincide with the timing of moving or major cleaning. For example, the generation AI may suggest the best time to sell furniture before moving. The unwanted item management unit also makes management suggestions for unwanted items by season by taking into account seasonal fluctuations. For example, the generation AI may suggest the best time to sell winter home appliances that are not used in the summer. This makes it possible to make management suggestions for unwanted items that take into account the user's lifestyle patterns and seasonal fluctuations.

[0031] The unwanted item management department can use image analysis technology to evaluate the condition of unwanted items and suggest the degree of deterioration and whether repairs are necessary. For example, the unwanted item management department can use image analysis technology to evaluate the degree of deterioration of unwanted items and suggest whether repairs are necessary. For example, the generation AI analyzes scratches and stains on furniture and determines whether repairs are necessary. The unwanted item management department can also analyze images of unwanted items and suggest the best time to sell them based on the degree of deterioration. For example, the generation AI can suggest the best time to sell before the deterioration progresses. The unwanted item management department can also use image analysis technology to evaluate the condition of unwanted items and suggest repairs or cleaning. For example, the generation AI can analyze stains and fraying on clothing and determine whether cleaning is necessary. This makes it possible to use image analysis technology to evaluate the condition of unwanted items and suggest the degree of deterioration and whether repairs are necessary.

[0032] The waste management unit can add a function to share management suggestions for waste items with other members of the household and manage them together. The waste management unit, for example, adds a function to share management suggestions for waste items with other members of the household and manage them together. For example, the generation AI provides an app that all family members can access. The waste management unit also adds a function to share management suggestions for waste items with members of the household and manage them together. For example, the generation AI provides an online platform that all family members can participate in. The waste management unit also adds a function to share management suggestions for waste items with other members of the household and manage them together. For example, the generation AI provides a calendar function that all family members can participate in. This allows management suggestions for waste items to be shared with other members of the household and manage them together.

[0033] The unwanted item management unit can link with smart home devices and receive unwanted item management suggestions through a voice assistant. For example, the unwanted item management unit can link with smart home devices to receive unwanted item management suggestions through a voice assistant. For example, the generation AI uses Amazon Alexa or Google Assistant. The unwanted item management unit can also link with smart home devices and receive unwanted item management suggestions through a voice assistant. For example, the generation AI can notify users by voice when it is time to sell unwanted items. The unwanted item management unit can also link with smart home devices to receive unwanted item management suggestions through a voice assistant. For example, the generation AI can provide information about recycle shops by voice. This allows the unit to link with smart home devices and receive unwanted item management suggestions through a voice assistant.

[0034] The purchase optimization unit can analyze past purchase history and user preferences and make individually customized purchase suggestions. The purchase optimization unit, for example, uses a generation AI to analyze past purchase history and make customized purchase suggestions based on the user's preferences. For example, the generation AI prioritizes suggestions of products from brands that the user has purchased in the past. The purchase optimization unit also analyzes user preference data and makes individually customized purchase suggestions. For example, the generation AI suggests products in colors and designs that the user prefers. The purchase optimization unit also uses a generation AI to analyze past purchase history and preferences and make individually customized purchase suggestions. For example, the generation AI suggests products in categories that the user frequently purchases. This makes it possible to analyze past purchase history and user preferences and make individually customized purchase suggestions.

[0035] The purchase optimization unit analyzes the reliability and reviews of the purchasing source and can suggest the most trustworthy purchasing source. The purchase optimization unit, for example, analyzes the reliability of the purchasing source and suggests the most trustworthy purchasing source. For example, the generation AI evaluates reliability based on past transaction history and user reviews. The purchase optimization unit also analyzes user reviews and suggests trustworthy purchasing sources. For example, the generation AI prioritizes suggesting online shops with high ratings. The purchase optimization unit also analyzes the reliability of the purchasing source and suggests the most trustworthy purchasing source. For example, the generation AI evaluates reliability based on past trouble history and refund policy. This makes it possible to analyze the reliability and reviews of the purchasing source and suggest the most trustworthy purchasing source.

[0036] The purchase optimization unit can add a function to share purchase optimization suggestions with other members of the household and to jointly create a purchasing plan. For example, the purchase optimization unit adds a function to share purchase optimization suggestions with family and friends and to jointly create a purchasing plan. For example, the generation AI provides an app that the whole family can access. The purchase optimization unit also adds a function to share purchase optimization suggestions with family and friends and to jointly create a purchasing plan. For example, the generation AI provides an online platform that the whole family can participate in. The purchase optimization unit also adds a function to share purchase optimization suggestions with family and friends and to jointly create a purchasing plan. For example, the generation AI provides a calendar function that the whole family can participate in. This allows the purchase optimization suggestions to be shared with other members of the household and to jointly create a purchasing plan.

[0037] The purchase optimization unit works in conjunction with a smartphone app, allowing the user to receive purchase optimization suggestions even when out and about. The purchase optimization unit, for example, works in conjunction with a smartphone app to provide purchase optimization suggestions, allowing the user to receive suggestions even when out and about. For example, the generation AI notifies the user of sale information through the app. The purchase optimization unit also works in conjunction with a smartphone app to provide purchase optimization suggestions, allowing the user to receive suggestions even when out and about. For example, the generation AI notifies the user of the best time to purchase through the app. The purchase optimization unit also works in conjunction with a smartphone app to provide purchase optimization suggestions, allowing the user to receive suggestions even when out and about. For example, the generation AI notifies the user of the best place to purchase through the app. This works in conjunction with a smartphone app to provide purchase optimization suggestions, allowing the user to receive purchase optimization suggestions even when out and about.

[0038] The payment unit can analyze PayPay usage history and suggest the optimal timing for cashback or point redemption to the user. For example, the generation AI can notify the user that cashback is more common on certain days of the week or during certain times of the day. The payment unit can also analyze the user's PayPay usage history and suggest the optimal timing for point redemption. For example, if the generation AI is frequently used at a certain store, it can notify the user of that store's point redemption campaign. The payment unit can also analyze PayPay usage history and suggest the optimal timing for cashback or point redemption to the user. For example, the generation AI can suggest using PayPay during a certain event or sale period. This allows the payment unit to analyze PayPay usage history and suggest the optimal timing for cashback or point redemption to the user.

[0039] The payment unit can add transaction history monitoring and fraud detection functions to enhance the security of transactions using PayPay. For example, the generation AI sends an alert if a fraudulent transaction is detected. The payment unit also adds a function to analyze transaction history and detect fraudulent transactions. For example, the generation AI detects abnormal transaction patterns and notifies the user. The payment unit also adds a fraud detection function to enhance the security of transactions using PayPay. For example, the generation AI analyzes transaction frequency and amount and automatically blocks transactions that may be fraudulent. This allows the payment unit to add transaction history monitoring and fraud detection functions to enhance the security of transactions using PayPay.

[0040] The payment unit can link PayPay with other cashless payment services to provide more diverse payment options. For example, the generation AI links it with credit cards and debit cards. The payment unit also links it with other cashless payment services to increase the payment options for transactions using PayPay. For example, the generation AI links it with electronic money and QR code payments. The payment unit also links PayPay with other cashless payment services to provide more diverse payment options. For example, the generation AI links it with bank accounts and prepaid cards. This allows PayPay to be linked with other cashless payment services to provide more diverse payment options.

[0041] The payment unit can link PayPay with smartwatches and wearable devices to make it more convenient to use. For example, the payment unit links PayPay with smartwatches to make it more convenient to use. For example, the generation AI enables payments with smartwatches. The payment unit also links with wearable devices to make transactions using PayPay more convenient. For example, the generation AI enables payments with fitness trackers. The payment unit also links PayPay with smartwatches and wearable devices to make it more convenient to use. For example, the generation AI enables payments with smart glasses. In this way, PayPay can be linked with smartwatches and wearable devices to make it more convenient to use.

[0042] The unwanted item management department can use the generation AI to analyze the market value of unwanted items in real time and suggest the best time to sell. The unwanted item management department, for example, can use the generation AI to analyze the market value of unwanted items in real time and suggest the best time to sell. For example, the generation AI analyzes the market value of antique furniture and suggests the best time to sell it for the highest price. The unwanted item management department can also analyze the market value of unwanted items in real time and suggest the best time to sell. For example, the generation AI analyzes seasonal demand and suggests the best time to sell. The unwanted item management department can also use the generation AI to analyze the market value of unwanted items in real time and suggest the best time to sell. For example, the generation AI analyzes online auction data and suggests the best time to sell it for the highest price. This allows the generation AI to analyze the market value of unwanted items in real time and suggest the best time to sell.

[0043] The unwanted item management department can make suggestions for repairs and remakes to increase the value of unwanted items. The unwanted item management department, for example, makes suggestions for repairs and remakes to increase the value of unwanted items. For example, the generation AI makes suggestions for repairing and reselling old furniture. The unwanted item management department also makes suggestions for remakes to increase the value of unwanted items. For example, the generation AI makes suggestions for remaking old clothing into new designs. The unwanted item management department also makes suggestions for repairs and remakes to increase the value of unwanted items. For example, the generation AI makes suggestions for repairing and reselling broken home appliances. This makes it possible to make suggestions for repairs and remakes to increase the value of unwanted items.

[0044] The unwanted item management unit can add a function to hold an auction in collaboration with other users in order to maximize the value of unwanted items. The unwanted item management unit, for example, adds a function to hold an auction in collaboration with other users in order to maximize the value of unwanted items. For example, the generation AI holds a joint auction on an online platform. The unwanted item management unit also adds a function to hold an auction in collaboration with other users in order to maximize the value of unwanted items. For example, the generation AI holds an auction event in a local community. The unwanted item management unit also adds a function to hold an auction in collaboration with other users in order to maximize the value of unwanted items. For example, the generation AI holds an auction in collaboration with a company or organization. This allows the addition of a function to hold an auction in collaboration with other users in order to maximize the value of unwanted items.

[0045] The unwanted item management unit can add a function that allows expert advice to be received in order to maximize the value of unwanted items. The unwanted item management unit can add a function that allows expert advice to be received in order to maximize the value of unwanted items, for example. For example, the generation AI provides a function to receive appraisals from antique furniture experts. The unwanted item management unit can also add a function that allows expert advice to be received in order to maximize the value of unwanted items. For example, the generation AI can provide a function to receive advice from repair and remake experts. The unwanted item management unit can also add a function that allows expert advice to be received in order to maximize the value of unwanted items. For example, the generation AI can provide a platform that allows consultations with experts online. This can add a function that allows expert advice to be received in order to maximize the value of unwanted items.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The unwanted item management unit can make management suggestions for unwanted items by taking into account the user's lifestyle patterns and seasonal fluctuations. For example, the generation AI can be used to analyze the user's lifestyle patterns and make management suggestions for unwanted items by season. For example, the generation AI can suggest the best time to sell winter clothes in the spring. The unwanted item management unit can also analyze the user's lifestyle rhythm and make management suggestions for unwanted items that coincide with the timing of moving or spring cleaning. For example, the generation AI can suggest the best time to sell furniture before moving. The unwanted item management unit can also make management suggestions for unwanted items by season, taking into account seasonal fluctuations. For example, the generation AI can suggest the best time to sell winter home appliances that are not used in the summer. This makes it possible to make management suggestions for unwanted items that take into account the user's lifestyle patterns and seasonal fluctuations.

[0048] The unwanted item management department uses image analysis technology to evaluate the condition of unwanted items and suggest the degree of deterioration and whether repairs are necessary. For example, the generation AI analyzes scratches and stains on furniture to determine whether repairs are necessary. The unwanted item management department also analyzes images of unwanted items and suggests the best time to sell them based on the degree of deterioration. For example, the generation AI suggests the best time to sell before the deterioration progresses. The unwanted item management department also uses image analysis technology to evaluate the condition of unwanted items and suggest repairs or cleaning. For example, the generation AI analyzes stains and fraying on clothing to determine whether cleaning is necessary. This makes it possible to evaluate the condition of unwanted items using image analysis technology and suggest the degree of deterioration and whether repairs are necessary.

[0049] The waste management unit can add a function to share waste management suggestions with other members of the household and manage them together. For example, a function to share waste management suggestions with other members of the household and manage them together is added. For example, the generation AI provides an app that all family members can access. The waste management unit also adds a function to share waste management suggestions with members of the household and manage them together. For example, the generation AI provides an online platform that all family members can participate in. The waste management unit also adds a function to share waste management suggestions with other members of the household and manage them together. For example, the generation AI provides a calendar function that all family members can participate in. This allows waste management suggestions to be shared with other members of the household and manage them together.

[0050] The junk item management unit can link with smart home devices and receive suggestions for managing junk items through a voice assistant. For example, the junk item management suggestions can be linked with smart home devices and received through a voice assistant. For example, the generation AI uses Amazon Alexa or Google Assistant. The junk item management unit can also link with smart home devices and receive suggestions for managing junk items through a voice assistant. For example, the generation AI can notify users by voice when it is time to sell their junk items. The junk item management unit can also link with smart home devices and receive suggestions through a voice assistant. For example, the generation AI can provide information about recycle shops by voice. This allows the unit to link with smart home devices and receive suggestions for managing junk items through a voice assistant.

[0051] The purchase optimization unit can analyze past purchase history and user preferences and make individually customized purchase suggestions. For example, the generation AI can be used to analyze past purchase history and make customized purchase suggestions based on the user's preferences. For example, the generation AI can prioritize suggestions of products from brands that have been purchased in the past. The purchase optimization unit can also analyze user preference data and make individually customized purchase suggestions. For example, the generation AI can suggest products in colors and designs that the user prefers. The purchase optimization unit can also use the generation AI to analyze past purchase history and preferences and make individually customized purchase suggestions. For example, the generation AI can suggest products in categories that the user frequently purchases. This makes it possible to analyze past purchase history and user preferences and make individually customized purchase suggestions.

[0052] The purchase optimization unit analyzes the reliability and reviews of the purchasing source and can suggest the most trustworthy purchasing source. For example, it analyzes the reliability of the purchasing source and suggests the most trustworthy purchasing source. For example, the generation AI evaluates reliability based on past transaction history and user reviews. The purchase optimization unit also analyzes user reviews and suggests trustworthy purchasing sources. For example, the generation AI prioritizes suggesting online shops with high ratings. The purchase optimization unit also analyzes the reliability of the purchasing source and suggests the most trustworthy purchasing source. For example, the generation AI evaluates reliability based on past trouble history and refund policy. This allows it to analyze the reliability and reviews of the purchasing source and suggest the most trustworthy purchasing source.

[0053] The purchase optimization unit may add a function to share purchase optimization suggestions with other members of the household and to jointly create a purchasing plan. For example, the generation AI may provide an app that the whole family can access. The purchase optimization unit may also add a function to share purchase optimization suggestions with family and friends and to jointly create a purchasing plan. For example, the generation AI may provide an online platform that the whole family can participate in. The purchase optimization unit may also add a function to share purchase optimization suggestions with family and friends and to jointly create a purchasing plan. For example, the generation AI may provide a calendar function that the whole family can participate in. This allows the purchase optimization suggestions to be shared with other members of the household and to jointly create a purchasing plan.

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

[0055] Step 1: The Unwanted Item Management Department suggests the best time to sell, throw away, or donate unwanted items, as well as where to send them. For example, the Generative AI can suggest the best time to sell or donate items based on the information about the unwanted items entered by the user. The Unwanted Item Management Department can also suggest destinations such as recycling shops or donation sites. Step 2: The purchase optimization unit suggests the best time and place to purchase the desired item. For example, the generation AI analyzes sale periods and stock availability based on the information about the desired item entered by the user, and suggests the best time to purchase. The purchase optimization unit can also suggest places to purchase, such as online shops or physical stores. Step 3: The suggestion unit notifies the user of the suggestions made by the unnecessary item management unit and the purchase optimization unit. For example, the generation AI sends a notification to the user's smartphone and displays the suggestions. The suggestion unit can also notify the user of the suggestions via email or in-app message. Step 4: The payment department allows you to buy, sell, or purchase unwanted items cashlessly. For example, you can use PayPay to pay at a recycle shop. You can also use PayPay when making purchases at online stores.

[0056] (Example 2) The unwanted item management system according to an embodiment of the present invention is a system that supports the management of unwanted items in the home and the optimization of purchases. This system uses AI to suggest the timing and destination for selling, discarding, or donating unwanted items, as well as the optimal timing and place to purchase desired items. This reduces the effort required for tidying up the home and promotes the effective use of unwanted items. Furthermore, by using PayPay, the system can expand the economic sphere and contribute to the SDGs and donation activities.

[0057] The unwanted item management system according to the embodiment includes an unwanted item management unit, a purchase optimization unit, a suggestion unit, and a payment unit. The unwanted item management unit suggests the timing and destination for selling, discarding, or donating unwanted items. For example, the generation AI suggests the optimal time to sell or donate unwanted items based on information about the unwanted items entered by the user. The unwanted item management unit can also suggest destinations such as recycle shops and donation sites. The purchase optimization unit suggests the optimal time and place to purchase desired items. For example, the generation AI analyzes sale periods and inventory status based on information about the desired items entered by the user and suggests the optimal purchase timing. The purchase optimization unit can also suggest purchasing locations such as online shops and physical stores. The suggestion unit notifies the user of the suggestions made by the unwanted item management unit and the purchase optimization unit. For example, the generation AI sends a notification to the user's smartphone and displays the suggestions. The suggestion unit can also notify the user of the suggestions via email or in-app message. The payment unit enables cashless buying, selling, and purchasing of unwanted items. For example, the user can use PayPay to pay at a recycle shop. The payment section also allows users to use PayPay when making purchases at online stores, which helps manage unwanted household items and optimize purchases, expanding the economic sphere and contributing to SDGs and donation activities.

[0058] The unwanted item management unit can make management suggestions for unwanted items by taking into account the user's lifestyle patterns and seasonal fluctuations. The unwanted item management unit, for example, uses a generation AI to analyze the user's lifestyle patterns and make management suggestions for unwanted items by season. For example, the generation AI may suggest the best time to sell winter clothes in the spring. The unwanted item management unit also analyzes the user's lifestyle rhythm and makes management suggestions for unwanted items that coincide with the timing of moving or major cleaning. For example, the generation AI may suggest the best time to sell furniture before moving. The unwanted item management unit also makes management suggestions for unwanted items by season by taking into account seasonal fluctuations. For example, the generation AI may suggest the best time to sell winter home appliances that are not used in the summer. This makes it possible to make management suggestions for unwanted items that take into account the user's lifestyle patterns and seasonal fluctuations.

[0059] The unwanted item management department can use image analysis technology to evaluate the condition of unwanted items and suggest the degree of deterioration and whether repairs are necessary. For example, the unwanted item management department can use image analysis technology to evaluate the degree of deterioration of unwanted items and suggest whether repairs are necessary. For example, the generation AI analyzes scratches and stains on furniture and determines whether repairs are necessary. The unwanted item management department can also analyze images of unwanted items and suggest the best time to sell them based on the degree of deterioration. For example, the generation AI can suggest the best time to sell before the deterioration progresses. The unwanted item management department can also use image analysis technology to evaluate the condition of unwanted items and suggest repairs or cleaning. For example, the generation AI can analyze stains and fraying on clothing and determine whether cleaning is necessary. This makes it possible to use image analysis technology to evaluate the condition of unwanted items and suggest the degree of deterioration and whether repairs are necessary.

[0060] The unwanted item management unit uses the emotion estimation function to analyze the emotions the user has toward unwanted items and suggests the timing when it is emotionally easy to let them go. The unwanted item management unit, for example, uses the emotion estimation function to analyze the emotions the user has toward unwanted items and suggests the timing when it is emotionally easy to let them go. For example, the generation AI suggests the timing to sell when positive emotions are strong. The unwanted item management unit also analyzes the user's emotional data and suggests the timing when it is emotionally easy to let them go. For example, the generation AI suggests disposing of unwanted items when stress is low. The unwanted item management unit also uses the emotion estimation function to analyze the emotions the user has toward unwanted items and suggests the timing when it is emotionally easy to let them go. For example, the generation AI suggests selling unwanted items when emotions are stable. This makes it possible to analyze the emotions the user has toward unwanted items and suggest the timing when it is emotionally easy to let them go.

[0061] The waste management unit can add a function to share management suggestions for waste items with other members of the household and manage them together. The waste management unit, for example, adds a function to share management suggestions for waste items with other members of the household and manage them together. For example, the generation AI provides an app that all family members can access. The waste management unit also adds a function to share management suggestions for waste items with members of the household and manage them together. For example, the generation AI provides an online platform that all family members can participate in. The waste management unit also adds a function to share management suggestions for waste items with other members of the household and manage them together. For example, the generation AI provides a calendar function that all family members can participate in. This allows management suggestions for waste items to be shared with other members of the household and manage them together.

[0062] The unwanted item management unit can link with smart home devices and receive unwanted item management suggestions through a voice assistant. For example, the unwanted item management unit can link with smart home devices to receive unwanted item management suggestions through a voice assistant. For example, the generation AI uses Amazon Alexa or Google Assistant. The unwanted item management unit can also link with smart home devices and receive unwanted item management suggestions through a voice assistant. For example, the generation AI can notify users by voice when it is time to sell unwanted items. The unwanted item management unit can also link with smart home devices to receive unwanted item management suggestions through a voice assistant. For example, the generation AI can provide information about recycle shops by voice. This allows the unit to link with smart home devices and receive unwanted item management suggestions through a voice assistant.

[0063] The unwanted item management unit can use the emotion estimation function to generate messages that make users feel positive when they let go of unwanted items. For example, the generation AI provides a message such as, "By selling this furniture, you can create new space." The unwanted item management unit also analyzes the user's emotion data and generates messages that elicit positive emotions. For example, the generation AI provides a message such as, "Donating this clothing will help many people." The unwanted item management unit also uses the emotion estimation function to generate messages that make users feel positive when they let go of unwanted items. For example, the generation AI provides a message such as, "By selling this home appliance, you can buy a new home appliance." In this way, messages can be generated that make users feel positive when they let go of unwanted items.

[0064] The purchase optimization unit can analyze past purchase history and user preferences and make individually customized purchase suggestions. The purchase optimization unit, for example, uses a generation AI to analyze past purchase history and make customized purchase suggestions based on the user's preferences. For example, the generation AI prioritizes suggestions of products from brands that the user has purchased in the past. The purchase optimization unit also analyzes user preference data and makes individually customized purchase suggestions. For example, the generation AI suggests products in colors and designs that the user prefers. The purchase optimization unit also uses a generation AI to analyze past purchase history and preferences and make individually customized purchase suggestions. For example, the generation AI suggests products in categories that the user frequently purchases. This makes it possible to analyze past purchase history and user preferences and make individually customized purchase suggestions.

[0065] The purchase optimization unit analyzes the reliability and reviews of the purchasing source and can suggest the most trustworthy purchasing source. The purchase optimization unit, for example, analyzes the reliability of the purchasing source and suggests the most trustworthy purchasing source. For example, the generation AI evaluates reliability based on past transaction history and user reviews. The purchase optimization unit also analyzes user reviews and suggests trustworthy purchasing sources. For example, the generation AI prioritizes suggesting online shops with high ratings. The purchase optimization unit also analyzes the reliability of the purchasing source and suggests the most trustworthy purchasing source. For example, the generation AI evaluates reliability based on past trouble history and refund policy. This makes it possible to analyze the reliability and reviews of the purchasing source and suggest the most trustworthy purchasing source.

[0066] The purchase optimization unit uses the emotion estimation function to analyze the expectations and anxieties the user has about purchasing, and can suggest the optimal timing for purchasing. The purchase optimization unit, for example, uses the emotion estimation function to analyze the expectations and anxieties the user has about purchasing, and can suggest the optimal timing for purchasing. For example, the generation AI suggests purchasing when positive emotions are strong. The purchase optimization unit also analyzes the user's emotion data, analyzes the expectations and anxieties about purchasing, and suggests the optimal timing for purchasing. For example, the generation AI suggests purchasing when stress is low. The purchase optimization unit also uses the emotion estimation function to analyze the expectations and anxieties the user has about purchasing, and can suggest the optimal timing for purchasing. For example, the generation AI suggests purchasing when emotions are stable. This allows the user to analyze the expectations and anxieties the user has about purchasing, and can suggest the optimal timing for purchasing.

[0067] The purchase optimization unit can add a function to share purchase optimization suggestions with other members of the household and to jointly create a purchasing plan. For example, the purchase optimization unit adds a function to share purchase optimization suggestions with family and friends and to jointly create a purchasing plan. For example, the generation AI provides an app that the whole family can access. The purchase optimization unit also adds a function to share purchase optimization suggestions with family and friends and to jointly create a purchasing plan. For example, the generation AI provides an online platform that the whole family can participate in. The purchase optimization unit also adds a function to share purchase optimization suggestions with family and friends and to jointly create a purchasing plan. For example, the generation AI provides a calendar function that the whole family can participate in. This allows the purchase optimization suggestions to be shared with other members of the household and to jointly create a purchasing plan.

[0068] The purchase optimization unit works in conjunction with a smartphone app, allowing the user to receive purchase optimization suggestions even when out and about. The purchase optimization unit, for example, works in conjunction with a smartphone app to provide purchase optimization suggestions, allowing the user to receive suggestions even when out and about. For example, the generation AI notifies the user of sale information through the app. The purchase optimization unit also works in conjunction with a smartphone app to provide purchase optimization suggestions, allowing the user to receive suggestions even when out and about. For example, the generation AI notifies the user of the best time to purchase through the app. The purchase optimization unit also works in conjunction with a smartphone app to provide purchase optimization suggestions, allowing the user to receive suggestions even when out and about. For example, the generation AI notifies the user of the best place to purchase through the app. This works in conjunction with a smartphone app to provide purchase optimization suggestions, allowing the user to receive purchase optimization suggestions even when out and about.

[0069] The purchase optimization unit can use the emotion estimation function to generate messages that will make the user feel positive when making a purchase decision. The purchase optimization unit, for example, uses the emotion estimation function to generate messages that will make the user feel positive when making a purchase decision. For example, the generation AI provides a message such as, "Purchasing this product will make your life more convenient." The purchase optimization unit also analyzes the user's emotion data and generates messages that elicit positive emotions. For example, the generation AI provides a message such as, "Purchasing this product will save you a lot of time." The purchase optimization unit also uses the emotion estimation function to generate messages that will make the user feel positive when making a purchase decision. For example, the generation AI provides a message such as, "Purchasing this product will make your whole family happy." In this way, messages can be generated that will make the user feel positive when making a purchase decision.

[0070] The payment unit can analyze PayPay usage history and suggest the optimal timing for cashback or point redemption to the user. For example, the generation AI can notify the user that cashback is more common on certain days of the week or during certain times of the day. The payment unit can also analyze the user's PayPay usage history and suggest the optimal timing for point redemption. For example, if the generation AI is frequently used at a certain store, it can notify the user of that store's point redemption campaign. The payment unit can also analyze PayPay usage history and suggest the optimal timing for cashback or point redemption to the user. For example, the generation AI can suggest using PayPay during a certain event or sale period. This allows the payment unit to analyze PayPay usage history and suggest the optimal timing for cashback or point redemption to the user.

[0071] The payment unit can add transaction history monitoring and fraud detection functions to enhance the security of transactions using PayPay. For example, the generation AI sends an alert if a fraudulent transaction is detected. The payment unit also adds a function to analyze transaction history and detect fraudulent transactions. For example, the generation AI detects abnormal transaction patterns and notifies the user. The payment unit also adds a fraud detection function to enhance the security of transactions using PayPay. For example, the generation AI analyzes transaction frequency and amount and automatically blocks transactions that may be fraudulent. This allows the payment unit to add transaction history monitoring and fraud detection functions to enhance the security of transactions using PayPay.

[0072] The payment unit can use the emotion estimation function to analyze the emotions a user feels when using PayPay and propose campaigns to promote usage. The payment unit, for example, uses the emotion estimation function to analyze the emotions a user feels when using PayPay and propose campaigns to promote usage. For example, the generation AI runs a cashback campaign when emotions are strong. The payment unit also analyzes user emotion data and proposes campaigns to promote usage of PayPay. For example, the generation AI runs a point return campaign when emotions are stable. The payment unit also uses the emotion estimation function to analyze the emotions a user feels when using PayPay and proposes campaigns to promote usage. For example, the generation AI provides limited-time coupons when emotions are positive. This allows the payment unit to analyze the emotions a user feels when using PayPay and propose campaigns to promote usage.

[0073] The payment unit can link PayPay with other cashless payment services to provide more diverse payment options. For example, the generation AI links it with credit cards and debit cards. The payment unit also links it with other cashless payment services to increase the payment options for transactions using PayPay. For example, the generation AI links it with electronic money and QR code payments. The payment unit also links PayPay with other cashless payment services to provide more diverse payment options. For example, the generation AI links it with bank accounts and prepaid cards. This allows PayPay to be linked with other cashless payment services to provide more diverse payment options.

[0074] The payment unit can link PayPay with smartwatches and wearable devices to make it more convenient to use. For example, the payment unit links PayPay with smartwatches to make it more convenient to use. For example, the generation AI enables payments with smartwatches. The payment unit also links with wearable devices to make transactions using PayPay more convenient. For example, the generation AI enables payments with fitness trackers. The payment unit also links PayPay with smartwatches and wearable devices to make it more convenient to use. For example, the generation AI enables payments with smart glasses. In this way, PayPay can be linked with smartwatches and wearable devices to make it more convenient to use.

[0075] The payment unit can use the emotion estimation function to generate messages that will make the user feel positive when using PayPay. For example, the generation AI provides a message such as, "Use PayPay to earn points." The payment unit also analyzes the user's emotion data and generates messages that elicit positive emotions. For example, the generation AI provides a message such as, "Use PayPay to receive cashback." The payment unit also uses the emotion estimation function to generate messages that will make the user feel positive when using PayPay. For example, the generation AI provides a message such as, "Use PayPay to receive a discount coupon." In this way, messages can be generated that will make the user feel positive when using PayPay.

[0076] The unwanted item management department can use the generation AI to analyze the market value of unwanted items in real time and suggest the best time to sell. The unwanted item management department, for example, can use the generation AI to analyze the market value of unwanted items in real time and suggest the best time to sell. For example, the generation AI analyzes the market value of antique furniture and suggests the best time to sell it for the highest price. The unwanted item management department can also analyze the market value of unwanted items in real time and suggest the best time to sell. For example, the generation AI analyzes seasonal demand and suggests the best time to sell. The unwanted item management department can also use the generation AI to analyze the market value of unwanted items in real time and suggest the best time to sell. For example, the generation AI analyzes online auction data and suggests the best time to sell it for the highest price. This allows the generation AI to analyze the market value of unwanted items in real time and suggest the best time to sell.

[0077] The unwanted item management department can make suggestions for repairs and remakes to increase the value of unwanted items. The unwanted item management department, for example, makes suggestions for repairs and remakes to increase the value of unwanted items. For example, the generation AI makes suggestions for repairing and reselling old furniture. The unwanted item management department also makes suggestions for remakes to increase the value of unwanted items. For example, the generation AI makes suggestions for remaking old clothing into new designs. The unwanted item management department also makes suggestions for repairs and remakes to increase the value of unwanted items. For example, the generation AI makes suggestions for repairing and reselling broken home appliances. This makes it possible to make suggestions for repairs and remakes to increase the value of unwanted items.

[0078] The unwanted item management unit can add a function to hold an auction in collaboration with other users in order to maximize the value of unwanted items. The unwanted item management unit, for example, adds a function to hold an auction in collaboration with other users in order to maximize the value of unwanted items. For example, the generation AI holds a joint auction on an online platform. The unwanted item management unit also adds a function to hold an auction in collaboration with other users in order to maximize the value of unwanted items. For example, the generation AI holds an auction event in a local community. The unwanted item management unit also adds a function to hold an auction in collaboration with other users in order to maximize the value of unwanted items. For example, the generation AI holds an auction in collaboration with a company or organization. This allows the addition of a function to hold an auction in collaboration with other users in order to maximize the value of unwanted items.

[0079] The unwanted item management unit can add a function that allows expert advice to be received in order to maximize the value of unwanted items. The unwanted item management unit can add a function that allows expert advice to be received in order to maximize the value of unwanted items, for example. For example, the generation AI provides a function to receive appraisals from antique furniture experts. The unwanted item management unit can also add a function that allows expert advice to be received in order to maximize the value of unwanted items. For example, the generation AI can provide a function to receive advice from repair and remake experts. The unwanted item management unit can also add a function that allows expert advice to be received in order to maximize the value of unwanted items. For example, the generation AI can provide a platform that allows consultations with experts online. This can add a function that allows expert advice to be received in order to maximize the value of unwanted items.

[0080] The unwanted item management unit can use the emotion estimation function to generate messages that make users feel positive when maximizing the value of unwanted items. For example, the generation AI provides a message such as, "Selling this furniture will free up new space." The unwanted item management unit also analyzes the user's emotion data and generates messages that elicit positive emotions. For example, the generation AI provides a message such as, "Donating this clothing will help many people." The unwanted item management unit also uses the emotion estimation function to generate messages that make users feel positive when maximizing the value of unwanted items. For example, the generation AI provides a message such as, "Selling this home appliance will allow you to purchase a new home appliance." In this way, messages can be generated that make users feel positive when maximizing the value of unwanted items.

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

[0082] The unwanted item management unit can make management suggestions for unwanted items by taking into account the user's lifestyle patterns and seasonal fluctuations. For example, the generation AI can be used to analyze the user's lifestyle patterns and make management suggestions for unwanted items by season. For example, the generation AI can suggest the best time to sell winter clothes in the spring. The unwanted item management unit can also analyze the user's lifestyle rhythm and make management suggestions for unwanted items that coincide with the timing of moving or spring cleaning. For example, the generation AI can suggest the best time to sell furniture before moving. The unwanted item management unit can also make management suggestions for unwanted items by season, taking into account seasonal fluctuations. For example, the generation AI can suggest the best time to sell winter home appliances that are not used in the summer. This makes it possible to make management suggestions for unwanted items that take into account the user's lifestyle patterns and seasonal fluctuations.

[0083] The unwanted item management department uses image analysis technology to evaluate the condition of unwanted items and suggest the degree of deterioration and whether repairs are necessary. For example, the generation AI analyzes scratches and stains on furniture to determine whether repairs are necessary. The unwanted item management department also analyzes images of unwanted items and suggests the best time to sell them based on the degree of deterioration. For example, the generation AI suggests the best time to sell before the deterioration progresses. The unwanted item management department also uses image analysis technology to evaluate the condition of unwanted items and suggest repairs or cleaning. For example, the generation AI analyzes stains and fraying on clothing to determine whether cleaning is necessary. This makes it possible to evaluate the condition of unwanted items using image analysis technology and suggest the degree of deterioration and whether repairs are necessary.

[0084] The unwanted item management unit uses the emotion estimation function to analyze the emotions the user has toward unwanted items and can suggest the timing when it is emotionally easy to let go of them. For example, the generation AI will suggest the timing to sell when positive emotions are strong. The unwanted item management unit also analyzes the user's emotional data and suggests the timing when it is emotionally easy to let go of them. For example, the generation AI will suggest disposing of unwanted items when stress levels are low. The unwanted item management unit also uses the emotion estimation function to analyze the emotions the user has toward unwanted items and suggests the timing when it is emotionally easy to let go of them. For example, the generation AI will suggest selling unwanted items when emotions are stable. This allows the emotions the user has toward unwanted items to be analyzed and suggests the timing when it is emotionally easy to let go of them.

[0085] The waste management unit can add a function to share waste management suggestions with other members of the household and manage them together. For example, a function to share waste management suggestions with other members of the household and manage them together is added. For example, the generation AI provides an app that all family members can access. The waste management unit also adds a function to share waste management suggestions with members of the household and manage them together. For example, the generation AI provides an online platform that all family members can participate in. The waste management unit also adds a function to share waste management suggestions with other members of the household and manage them together. For example, the generation AI provides a calendar function that all family members can participate in. This allows waste management suggestions to be shared with other members of the household and manage them together.

[0086] The junk item management unit can link with smart home devices and receive suggestions for managing junk items through a voice assistant. For example, the junk item management suggestions can be linked with smart home devices and received through a voice assistant. For example, the generation AI uses Amazon Alexa or Google Assistant. The junk item management unit can also link with smart home devices and receive suggestions for managing junk items through a voice assistant. For example, the generation AI can notify users by voice when it is time to sell their junk items. The junk item management unit can also link with smart home devices and receive suggestions through a voice assistant. For example, the generation AI can provide information about recycle shops by voice. This allows the unit to link with smart home devices and receive suggestions for managing junk items through a voice assistant.

[0087] The unwanted item management unit can use the emotion estimation function to generate messages that will make the user feel positive when letting go of unwanted items. For example, the emotion estimation function is used to generate messages that will make the user feel positive when letting go of unwanted items. For example, the generation AI provides a message such as, "Selling this furniture will free up new space." The unwanted item management unit also analyzes the user's emotion data to generate messages that elicit positive emotions. For example, the generation AI provides a message such as, "Donating this clothing will help many people." The unwanted item management unit also uses the emotion estimation function to generate messages that will make the user feel positive when letting go of unwanted items. For example, the generation AI provides a message such as, "Selling this home appliance will allow you to purchase a new home appliance." This makes it possible to generate messages that will make the user feel positive when letting go of unwanted items.

[0088] The purchase optimization unit can analyze past purchase history and user preferences and make individually customized purchase suggestions. For example, the generation AI can be used to analyze past purchase history and make customized purchase suggestions based on the user's preferences. For example, the generation AI can prioritize suggestions of products from brands that have been purchased in the past. The purchase optimization unit can also analyze user preference data and make individually customized purchase suggestions. For example, the generation AI can suggest products in colors and designs that the user prefers. The purchase optimization unit can also use the generation AI to analyze past purchase history and preferences and make individually customized purchase suggestions. For example, the generation AI can suggest products in categories that the user frequently purchases. This makes it possible to analyze past purchase history and user preferences and make individually customized purchase suggestions.

[0089] The purchase optimization unit analyzes the reliability and reviews of the purchasing source and can suggest the most trustworthy purchasing source. For example, it analyzes the reliability of the purchasing source and suggests the most trustworthy purchasing source. For example, the generation AI evaluates reliability based on past transaction history and user reviews. The purchase optimization unit also analyzes user reviews and suggests trustworthy purchasing sources. For example, the generation AI prioritizes suggesting online shops with high ratings. The purchase optimization unit also analyzes the reliability of the purchasing source and suggests the most trustworthy purchasing source. For example, the generation AI evaluates reliability based on past trouble history and refund policy. This allows it to analyze the reliability and reviews of the purchasing source and suggest the most trustworthy purchasing source.

[0090] The purchase optimization unit uses the emotion estimation function to analyze the expectations and anxieties a user has about a purchase and can suggest the optimal timing for the purchase. For example, the emotion estimation function is used to analyze the expectations and anxieties a user has about a purchase and suggest the optimal timing for the purchase. For example, the generation AI suggests purchasing when positive emotions are strong. The purchase optimization unit also analyzes the user's emotion data, analyzes their expectations and anxieties about a purchase, and suggests the optimal timing for the purchase. For example, the generation AI suggests purchasing when stress is low. The purchase optimization unit also uses the emotion estimation function to analyze the expectations and anxieties a user has about a purchase and suggest the optimal timing for the purchase. For example, the generation AI suggests purchasing when emotions are stable. This makes it possible to analyze the expectations and anxieties a user has about a purchase and suggest the optimal timing for the purchase.

[0091] The purchase optimization unit may add a function to share purchase optimization suggestions with other members of the household and to jointly create a purchasing plan. For example, the generation AI may provide an app that the whole family can access. The purchase optimization unit may also add a function to share purchase optimization suggestions with family and friends and to jointly create a purchasing plan. For example, the generation AI may provide an online platform that the whole family can participate in. The purchase optimization unit may also add a function to share purchase optimization suggestions with family and friends and to jointly create a purchasing plan. For example, the generation AI may provide a calendar function that the whole family can participate in. This allows the purchase optimization suggestions to be shared with other members of the household and to jointly create a purchasing plan.

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

[0093] Step 1: The Unwanted Item Management Department suggests the best time to sell, throw away, or donate unwanted items, as well as where to send them. For example, the Generative AI can suggest the best time to sell or donate items based on the information about the unwanted items entered by the user. The Unwanted Item Management Department can also suggest destinations such as recycling shops or donation sites. Step 2: The purchase optimization unit suggests the best time and place to purchase the desired item. For example, the generation AI analyzes sale periods and stock availability based on the information about the desired item entered by the user, and suggests the best time to purchase. The purchase optimization unit can also suggest places to purchase, such as online shops or physical stores. Step 3: The suggestion unit notifies the user of the suggestions made by the unnecessary item management unit and the purchase optimization unit. For example, the generation AI sends a notification to the user's smartphone and displays the suggestions. The suggestion unit can also notify the user of the suggestions via email or in-app message. Step 4: The payment department allows you to buy, sell, or purchase unwanted items cashlessly. For example, you can use PayPay to pay at a recycle shop. You can also use PayPay when making purchases at online stores.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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. The Unwanted Goods Management Department will suggest the timing and destination for selling, throwing away, or donating unwanted items. A purchasing optimization department that suggests the best time and place to buy the items you want, a suggestion unit that notifies a user of the content suggested by the unnecessary item management unit and the purchase optimization unit; A payment section that allows cashless buying and selling of unwanted items. A system characterized by:

2. The unwanted item management unit Propose management of unwanted items taking into account the user's lifestyle patterns and seasonal fluctuations 2. The system of claim 1.

3. The unwanted item management unit Using image analysis technology, the condition of the unwanted items is evaluated, and the degree of deterioration and need for repairs are suggested.

2. The system of claim 1.

4. The unwanted item management unit Analyze the feelings the user has about the unwanted items and suggest the timing when it is emotionally easy to let go of them.

2. The system of claim 1.

5. The unwanted item management unit Add the ability to share management suggestions with other members of your household and manage them collaboratively 2. The system of claim 1.

6. The unwanted item management unit Connect with smart home devices and receive management suggestions through your voice assistant 2. The system of claim 1.

7. The unwanted item management unit Generate a message to make the user feel positive when letting go of the unwanted items.

2. The system of claim 1.

8. The purchase optimization unit Analyze the user's past purchase history and preferences to provide individually customized purchase suggestions 2. The system of claim 1.

Images