System

Abstract

An object of a system according to an embodiment is to provide a wide variety of support when senior generations purchase home appliances.SOLUTION: A system according to an embodiment includes a proposal unit, a negotiation unit, a pickup unit, a posting unit, and a management unit. The suggestion unit suggests a home appliance suitable for a lifestyle of a senior generation. The negotiation unit performs price negotiation at the time of purchasing a home appliance. The pickup unit arranges to pick up the old home appliance. The posting unit supports purchase word-of-mouth. The management unit manages the purchased home appliance, stores a manual, and deals with a failure.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] With conventional technology, it is difficult for seniors to receive comprehensive support when purchasing home appliances, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a wide range of support to seniors when purchasing home appliances. [Means for solving the problem]

[0006] The system according to the embodiment includes a proposal unit, a negotiation unit, a collection unit, a posting unit, and a management unit. The proposal unit proposes home appliances that suit the lifestyles of seniors. The negotiation unit negotiates prices when purchasing home appliances. The collection unit arranges for the collection of old home appliances. The posting unit supports purchase reviews. The management unit manages purchased home appliances, stores manuals, and handles breakdowns. [Effects of the Invention]

[0007] The system according to the embodiment can provide a wide range of support to seniors when they purchase home appliances. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 home appliance purchasing support system according to an embodiment of the present invention uses generative AI to provide a wide range of support to seniors when they purchase home appliances. This system suggests home appliances that fit the lifestyles of seniors, negotiates prices, arranges for the collection of old appliances, supports the posting of purchase reviews and usability information, manages purchased home appliances, stores manuals, and responds to malfunctions. In this way, the home appliance purchasing support system can support seniors in making smart home appliance purchases.

[0029] An appliance purchase support system according to an embodiment includes a proposal unit, a negotiation unit, a collection unit, a posting unit, and a management unit. The proposal unit proposes appliances suited to the lifestyles of seniors. For example, if a senior lives alone, the proposal unit proposes compact and easy-to-use appliances. If health management is important, the proposal unit proposes appliances with comprehensive health management functions. The proposal unit can also predict and propose future needs by using a generation AI to analyze a user's past purchase history and usage history. The negotiation unit negotiates prices when purchasing appliances. For example, the negotiation unit allows the generation AI to collect price information from online and physical stores and propose the optimal price. The negotiation unit can also automatically negotiate prices to ensure a purchase at the optimal price. The negotiation unit can also analyze past price fluctuation data and propose the optimal purchase timing. The collection unit arranges for the collection of old appliances. For example, the collection unit allows the generation AI to search for companies offering collection services and propose the optimal company. The collection unit can also automatically complete collection procedures, allowing seniors to dispose of old appliances without hassle. The collection unit can also analyze evaluation data on collection companies and recommend the most reliable companies. The posting unit supports the posting of purchase reviews and usability information. For example, the posting unit automatically collects usage impressions and reviews of purchased home appliances using the generation AI and posts them on review sites and social media. The posting unit can also support the posting process to make it easier for seniors to post reviews. The posting unit can also analyze other users' review data, extract common evaluation points, and post them. The management unit manages purchased home appliances, stores manuals, and responds in case of malfunction. For example, the management unit centrally manages information on home appliances purchased by the generation AI and stores manuals digitally. In addition, when an appliance malfunctions, the management unit can use the generation AI to analyze the cause of the malfunction and suggest repair methods and repair companies. The management unit can also analyze usage data of home appliances and automatically notify users when maintenance is required. This allows the home appliance purchase support system to help seniors purchase home appliances smartly.

[0030] The suggestion unit can analyze a user's past purchase history and usage history to predict and suggest future needs. For example, the suggestion unit predicts future needs by using a generation AI to analyze a user's past purchase history and compare it with data from other users with similar patterns. For example, the suggestion unit can suggest the next home appliance that will be needed based on the frequency of use and lifespan of previously purchased home appliances. The suggestion unit can also analyze usage history and, if a particular home appliance is used frequently, suggest upgraded models of that appliance or related accessories. For example, it can suggest more powerful models and convenient accessories for frequently used cooking appliances. The suggestion unit can also combine and analyze purchase history and usage history to suggest home appliances according to the season or event. For example, it can suggest heaters and humidifiers in the winter and air conditioners and fans in the summer. This allows the system to predict a user's future needs and suggest the most suitable home appliances.

[0031] The suggestion unit can analyze the user's health data and suggest home appliances that are optimal for their health condition. For example, the suggestion unit uses a generation AI to analyze data from a fitness tracker and suggest home appliances that are useful for health management based on the user's exercise habits and sleep patterns. For example, it suggests exercise equipment for a user who is not getting enough exercise. The suggestion unit can also analyze medical records and suggest home appliances that address specific health conditions. For example, it suggests a blood pressure monitor or a cooking appliance that can manage salt intake for a user with high blood pressure. The suggestion unit can also suggest health management home appliances that are tailored to the user's lifestyle based on the health data. For example, it suggests a massage chair with a wide range of relaxation functions for a user who is under a lot of stress. This makes it possible to suggest the optimal home appliances according to the user's health condition.

[0032] The suggestion unit can suggest home appliances with enhanced entertainment functions based on the user's hobbies and interests. For example, the suggestion unit analyzes the user's hobbies and interests and suggests home appliances with enhanced entertainment functions based on the analysis. For example, a high-definition television or home theater system is suggested for a user whose hobby is watching movies. The suggestion unit also collects data related to hobbies and suggests home appliances that match the user's interests. For example, high-quality speakers or headphones are suggested for a user who likes music. The suggestion unit also suggests home appliances with enhanced entertainment functions based on the user's interests. For example, the latest game console or gaming chair is suggested for a user who likes games. In this way, home appliances with enhanced entertainment functions can be suggested that match the user's hobbies and interests.

[0033] The suggestion unit can analyze the user's living environment and suggest home appliances of the optimal size or design. For example, the suggestion unit analyzes the user's living environment data and suggests home appliances of the optimal size and design for the size and layout of the room. For example, compact home appliances are suggested for small rooms. The suggestion unit also analyzes the room layout and suggests home appliances that can be optimally placed. For example, it suggests a television or audio system that matches the layout of the living room. The suggestion unit also suggests home appliances with a high level of design based on the living environment data. For example, it suggests home appliances with a design that matches the interior and improves the atmosphere of the room. This makes it possible to suggest home appliances that are optimal for the user's living environment.

[0034] The negotiation unit can analyze past price fluctuation data and suggest the optimal timing for purchase. For example, the generation AI in the negotiation unit analyzes past price fluctuation data and predicts when the price of a specific home appliance will fall. For example, it suggests the optimal time to purchase based on price fluctuations during sales periods and seasonally. The negotiation unit also builds a system that notifies users of the optimal timing for purchase based on price fluctuation data. For example, it sends an alert when a price drop is predicted. The negotiation unit also analyzes past price data to identify when the price of a specific home appliance will be lowest. For example, it suggests the timing of specific events such as Black Friday or year-end sales. This allows the optimal purchase timing to be suggested, allowing users to purchase home appliances at the most advantageous price.

[0035] The negotiation unit can compare multiple sales channels and propose the most cost-effective option. For example, the generation AI in the negotiation unit collects price information from online and physical stores and compares multiple sales channels. For example, it compares prices for the same home appliance on multiple sites and proposes the cheapest option. The negotiation unit also evaluates cost performance by taking into account not only the price for each sales channel, but also shipping costs and warranty details. For example, it proposes options that come with free shipping or a long warranty. The generation AI in the negotiation unit also collects price information in real time and notifies the user of the most cost-effective option. For example, it sends an alert when the price drops. This allows the user to select the most cost-effective option.

[0036] The negotiation unit can suggest home appliances in the optimal price range based on the user's budget. For example, the generation AI analyzes the user's budget information and suggests home appliances in the optimal price range within that range. For example, if the budget is limited, it will suggest home appliances with high cost performance. The negotiation unit also compares home appliances in multiple price ranges based on the user's budget and suggests the most suitable option. For example, it will suggest home appliances with the best performance within the budget. The negotiation unit also presents the user with home appliance options for each price range based on the budget information. For example, it will generate a list of home appliances that can be purchased within the budget and provide the user with options. This makes it possible to suggest home appliances in the optimal price range according to the user's budget.

[0037] The negotiation unit can analyze a user's purchasing history and propose repeat customer discounts and special offers. For example, the generation AI analyzes a user's purchasing history and proposes repeat customer discounts and special offers. For example, a repeat customer discount is offered to a user who has previously purchased the same brand of home appliance. The negotiation unit also proposes special offers and coupons to users who have a high purchase history from a specific brand or store based on their purchasing history. For example, a discount is offered according to the number of purchases made at a specific store. The negotiation unit also analyzes a user's purchasing history and proposes special campaigns for repeat customers. For example, an upgraded model of a home appliance previously purchased is offered at a special price. In this way, repeat customer discounts and special offers are proposed, increasing the user's desire to purchase.

[0038] The collection unit can analyze collection company evaluation data and suggest the most reliable company. For example, the collection unit uses a generation AI to analyze collection company evaluation data and suggest the most reliable company based on user reviews and evaluation scores. For example, it prioritizes suggesting companies with high past user satisfaction. The collection unit also scores the reliability of collection companies based on the evaluation data and suggests the most suitable company based on that score. For example, it prioritizes displaying companies with high reliability scores. The collection unit also analyzes collection company evaluation data in real time and suggests the most reliable company based on the latest evaluation information. For example, it prioritizes suggesting companies with high recent ratings. In this way, by suggesting reliable collection companies, users can dispose of their old home appliances with peace of mind.

[0039] The collection unit can analyze the user's schedule and propose the optimal collection date and time. For example, the generation AI in the collection unit analyzes the user's schedule data and proposes the optimal collection date and time. For example, it prioritizes proposals for times when the user is at home. The collection unit also proposes the optimal date and time based on the user's calendar information and the availability of the collection company. For example, it selects and proposes a day when the user has fewer plans. The collection unit also analyzes schedule data in real time and proposes a collection date and time that suits the user's convenience. For example, it proposes a flexible date and time to accommodate sudden schedule changes. This makes it possible to propose the optimal collection date and time that suits the user's schedule.

[0040] The pickup unit can compare the fees of pickup companies and propose the most cost-effective option. For example, the generation AI in the pickup unit collects fee information from multiple pickup companies and proposes the most cost-effective option. For example, it proposes the cheapest company that offers the same services. The pickup unit also evaluates cost-effectiveness by taking into account not only the fees of pickup companies but also additional services and warranty details. For example, it proposes companies that offer free additional services. The generation AI in the pickup unit also collects fee information in real time and notifies the user of the most cost-effective option. For example, it sends an alert when the fee drops. This makes it possible to propose the most cost-effective pickup option.

[0041] The collection unit can analyze the service content of the collection company and propose additional services. For example, the generation AI in the collection unit analyzes the service content of the collection company and proposes additional services that are beneficial to the user. For example, it can propose a new home appliance installation service at the same time as collection. The collection unit also proposes recycling and disposal options based on the service content of the collection company. For example, it can propose an environmentally friendly recycling service. The generation AI in the collection unit also analyzes the service content in real time and proposes additional services that meet the user's needs. For example, it can propose a cleaning service at the same time as collection. This makes it possible to propose additional services that are beneficial to the user.

[0042] The posting unit can analyze the user's usage history and automatically generate specific usage impressions or evaluations. In the posting unit, for example, a generation AI analyzes the user's usage history and automatically generates specific usage impressions and evaluations. For example, the performance and usability of a home appliance are evaluated based on the frequency and duration of use. The posting unit also extracts the user's evaluation points based on the usage history and posts automatically generated reviews. For example, the user may specifically describe how a particular function was convenient or how it could be improved. The posting unit also analyzes the usage history using the generation AI, compares it with the evaluations of other users, and extracts common evaluation points. For example, the review is posted in comparison with the evaluations of other users who use the same home appliance. This makes it possible to automatically generate specific usage impressions and evaluations based on the user's usage history.

[0043] The posting unit can analyze other users' review data, extract common evaluation points, and post them. For example, the posting unit uses a generation AI to analyze other users' review data, extract common evaluation points, and post them. For example, it emphasizes points that many users have evaluated. The posting unit also extracts common evaluation points based on the review data and reflects them in automatically generated reviews. For example, it posts evaluations of specific functions or designs together. The posting unit also builds a system that analyzes other users' review data, extracts common evaluation points, and posts them. For example, it emphasizes points that have a lot of positive reviews and posts them. This makes it possible to analyze other users' review data, extract common evaluation points, and post them.

[0044] The posting unit can link with the user's SNS account and automatically share reviews. For example, the posting unit builds a system in which the generation AI links with the user's SNS account and automatically shares reviews. For example, it automatically posts reviews of purchased home appliances on SNS. The posting unit also links with the SNS account and automatically shares reviews, thereby sharing information with other users. For example, it posts with a specific hashtag. The posting unit also provides a function in which the generation AI links with the user's SNS account and automatically shares reviews. For example, it displays a share option to SNS when posting a review. This allows the user's reviews to be automatically shared on SNS.

[0045] The posting unit can suggest recommended home appliances to other users based on the user's ratings. For example, the posting unit uses a generation AI to analyze the user's rating data and suggest recommended home appliances to other users. For example, it suggests home appliances with the same rating points. The posting unit also builds a system that suggests recommended home appliances to other users based on the user's ratings. For example, it prioritizes suggesting home appliances with high ratings. The posting unit also provides a function that allows the generation AI to suggest recommended home appliances to other users based on the user's rating data. For example, it lists and suggests highly rated home appliances. This makes it possible to suggest recommended home appliances to other users based on the user's ratings.

[0046] The management unit can analyze the usage data of home appliances and automatically notify users when maintenance is required. For example, the management unit builds a system in which a generation AI analyzes the usage data of home appliances and automatically notifies users when maintenance is required. For example, it predicts when maintenance is required based on usage time and frequency. The management unit also identifies when home appliance maintenance is required based on usage data and notifies users. For example, it notifies users when filters need to be replaced or cleaned. The management unit also provides a function in which a generation AI analyzes usage data in real time and automatically notifies users when maintenance is required. For example, it sends maintenance alerts based on usage status. This makes it possible to automatically notify users when home appliance maintenance is required.

[0047] The management unit can analyze the failure data of home appliances and detect signs of failure early. For example, the management unit constructs a system in which a generation AI analyzes the failure data of home appliances and detects signs of failure early. For example, it analyzes abnormal behavior and error messages and predicts the possibility of failure. The management unit also detects specific patterns based on the failure data and identifies signs of failure early. For example, it notifies the user of the possibility of a failure if a specific error message occurs frequently. The management unit also provides a function in which a generation AI analyzes failure data in real time and detects signs of failure early. For example, it sends an alert when abnormal behavior is detected. This makes it possible to detect signs of failure in home appliances early.

[0048] The management unit can manage the warranty period of home appliances and notify users before the warranty expires. For example, the management unit builds a system in which a generation AI manages the warranty period of home appliances and notifies users before the warranty expires. For example, it sends an alert when the warranty period is running low. The management unit also suggests extended warranty options to users before the warranty expires based on the warranty period. For example, it sends information about extended warranties before the warranty period expires. The management unit also provides a function in which the generation AI manages the warranty period in real time and notifies users before the warranty expires. For example, it sends a reminder one month before the warranty period expires. This makes it possible to manage the warranty period of home appliances and notify users before the warranty expires.

[0049] The management unit can analyze the user manuals for home appliances and customize and display functions that users use frequently. For example, the management unit constructs a system in which a generation AI analyzes the user manuals for home appliances and customizes and displays functions that users use frequently. For example, it prioritizes and displays frequently used functions. The management unit also identifies functions that users use frequently based on the user manuals and provides customized manuals. For example, it displays detailed explanations about specific functions. The management unit also provides a function in which a generation AI analyzes the user manuals in real time and customizes and displays functions that users use frequently. For example, it displays functions that are used frequently at the top. This allows the user to customize and display functions that they use frequently.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The suggestion unit can suggest home appliances with enhanced entertainment functions based on the user's hobbies and interests. For example, the suggestion unit analyzes the user's hobbies and interests and suggests home appliances with enhanced entertainment functions based on the results. For example, a high-definition television or home theater system is suggested for a user whose hobby is watching movies. The suggestion unit also collects data related to hobbies and suggests home appliances that match the user's interests. For example, high-quality speakers or headphones are suggested for a user who likes music. The suggestion unit also suggests home appliances with enhanced entertainment functions based on the user's interests. For example, the latest game console or gaming chair is suggested for a user who likes games. In this way, home appliances with enhanced entertainment functions can be suggested that match the user's hobbies and interests.

[0052] The suggestion unit can analyze the user's living environment and suggest home appliances of the optimal size or design. For example, the suggestion unit analyzes the user's living environment data and suggests home appliances of the optimal size and design for the size and layout of the room. For example, compact home appliances are suggested for small rooms. The suggestion unit also analyzes the room layout and suggests home appliances that can be optimally placed. For example, it suggests a television or audio system that matches the layout of the living room. The suggestion unit also suggests home appliances with a high level of design based on the living environment data. For example, it suggests home appliances with a design that matches the interior and improves the atmosphere of the room. This makes it possible to suggest home appliances that are optimal for the user's living environment.

[0053] The suggestion unit can analyze the user's health data and suggest home appliances that are optimal for their health condition. For example, the suggestion unit uses a generation AI to analyze data from a fitness tracker and suggest home appliances that are useful for health management based on the user's exercise habits and sleep patterns. For example, it suggests exercise equipment for a user who is not getting enough exercise. The suggestion unit can also analyze medical records and suggest home appliances that address specific health conditions. For example, it suggests a blood pressure monitor or a cooking appliance that can manage salt intake for a user with high blood pressure. The suggestion unit can also suggest health management home appliances that are tailored to the user's lifestyle based on the health data. For example, it suggests a massage chair with a wide range of relaxation functions for a user who is under a lot of stress. This makes it possible to suggest the optimal home appliances according to the user's health condition.

[0054] The negotiation unit can analyze past price fluctuation data and suggest the optimal timing for purchase. For example, the generation AI in the negotiation unit analyzes past price fluctuation data and predicts when the price of a specific home appliance will fall. For example, it suggests the optimal time to purchase based on price fluctuations during sales periods and seasonally. The negotiation unit also builds a system that notifies users of the optimal timing for purchase based on price fluctuation data. For example, it sends an alert when a price drop is predicted. The negotiation unit also analyzes past price data to identify when the price of a specific home appliance will be lowest. For example, it suggests the timing of specific events such as Black Friday or year-end sales. This allows the optimal purchase timing to be suggested, allowing users to purchase home appliances at the most advantageous price.

[0055] The negotiation unit can compare multiple sales channels and propose the most cost-effective option. For example, the generation AI in the negotiation unit collects price information from online and physical stores and compares multiple sales channels. For example, it compares prices for the same home appliance on multiple sites and proposes the cheapest option. The negotiation unit also evaluates cost performance by taking into account not only the price for each sales channel, but also shipping costs and warranty details. For example, it proposes options that come with free shipping or a long warranty. The generation AI in the negotiation unit also collects price information in real time and notifies the user of the most cost-effective option. For example, it sends an alert when the price drops. This allows the user to select the most cost-effective option.

[0056] The collection unit can analyze the user's schedule and propose the optimal collection date and time. For example, the generation AI in the collection unit analyzes the user's schedule data and proposes the optimal collection date and time. For example, it prioritizes proposals for times when the user is at home. The collection unit also proposes the optimal date and time based on the user's calendar information and the availability of the collection company. For example, it selects and proposes a day when the user has fewer plans. The collection unit also analyzes schedule data in real time and proposes a collection date and time that suits the user's convenience. For example, it proposes a flexible date and time to accommodate sudden schedule changes. This makes it possible to propose the optimal collection date and time that suits the user's schedule.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The proposal unit suggests home appliances that suit the lifestyles of seniors. For example, if a senior lives alone, the proposal unit will suggest compact and easy-to-use home appliances. If health management is important, the proposal unit will suggest home appliances with comprehensive health management functions. Furthermore, the proposal unit can use the generation AI to analyze the user's past purchase history and usage history, predicting and suggesting future needs. Step 2: The negotiation unit negotiates the price when purchasing home appliances. For example, the negotiation unit allows the generation AI to collect price information from online and physical stores and propose the optimal price. The negotiation unit can also allow the generation AI to automatically negotiate the price and purchase at the optimal price. Furthermore, the negotiation unit can analyze past price fluctuation data and propose the optimal purchase timing. Step 3: The collection department arranges for the old appliances to be collected. For example, the generation AI searches for companies that offer collection services and suggests the most suitable company. The collection department can also automate the collection procedures, allowing seniors to dispose of their old appliances without any hassle. Furthermore, the collection department can analyze the evaluation data of collection companies and suggest the most reliable company. Step 4: The posting unit supports the posting of purchase reviews and usability information. For example, the posting unit automatically collects usage impressions and reviews of home appliances purchased by the generation AI and posts them on review sites and social media. The generation AI can also support the posting process so that seniors can easily post reviews. Furthermore, the posting unit can analyze other users' review data, extract common evaluation points, and post them. Step 5: The management department manages the purchased home appliances, stores the manuals, and responds when they break down. For example, the management department centrally manages information about the home appliances purchased by the generation AI and stores the manuals in digital format. In addition, when an appliance breaks down, the management department can have the generation AI analyze the cause of the failure and suggest repair methods and repair companies. Furthermore, the management department can analyze the usage data of the appliances and automatically notify the user when maintenance is required.

[0059] (Example 2) The home appliance purchasing support system according to an embodiment of the present invention uses generative AI to provide a wide range of support to seniors when they purchase home appliances. This system suggests home appliances that fit the lifestyles of seniors, negotiates prices, arranges for the collection of old appliances, supports the posting of purchase reviews and usability information, manages purchased home appliances, stores manuals, and responds to malfunctions. In this way, the home appliance purchasing support system can support seniors in making smart home appliance purchases.

[0060] An appliance purchase support system according to an embodiment includes a proposal unit, a negotiation unit, a collection unit, a posting unit, and a management unit. The proposal unit proposes appliances suited to the lifestyles of seniors. For example, if a senior lives alone, the proposal unit proposes compact and easy-to-use appliances. If health management is important, the proposal unit proposes appliances with comprehensive health management functions. The proposal unit can also predict and propose future needs by using a generation AI to analyze a user's past purchase history and usage history. The negotiation unit negotiates prices when purchasing appliances. For example, the negotiation unit allows the generation AI to collect price information from online and physical stores and propose the optimal price. The negotiation unit can also automatically negotiate prices to ensure a purchase at the optimal price. The negotiation unit can also analyze past price fluctuation data and propose the optimal purchase timing. The collection unit arranges for the collection of old appliances. For example, the collection unit allows the generation AI to search for companies offering collection services and propose the optimal company. The collection unit can also automatically complete collection procedures, allowing seniors to dispose of old appliances without hassle. The collection unit can also analyze evaluation data on collection companies and recommend the most reliable companies. The posting unit supports the posting of purchase reviews and usability information. For example, the posting unit automatically collects usage impressions and reviews of purchased home appliances using the generation AI and posts them on review sites and social media. The posting unit can also support the posting process to make it easier for seniors to post reviews. The posting unit can also analyze other users' review data, extract common evaluation points, and post them. The management unit manages purchased home appliances, stores manuals, and responds in case of malfunction. For example, the management unit centrally manages information on home appliances purchased by the generation AI and stores manuals digitally. In addition, when an appliance malfunctions, the management unit can use the generation AI to analyze the cause of the malfunction and suggest repair methods and repair companies. The management unit can also analyze usage data of home appliances and automatically notify users when maintenance is required. This allows the home appliance purchase support system to help seniors purchase home appliances smartly.

[0061] The suggestion unit can analyze a user's past purchase history and usage history to predict and suggest future needs. For example, the suggestion unit predicts future needs by using a generation AI to analyze a user's past purchase history and compare it with data from other users with similar patterns. For example, the suggestion unit can suggest the next home appliance that will be needed based on the frequency of use and lifespan of previously purchased home appliances. The suggestion unit can also analyze usage history and, if a particular home appliance is used frequently, suggest upgraded models of that appliance or related accessories. For example, it can suggest more powerful models and convenient accessories for frequently used cooking appliances. The suggestion unit can also combine and analyze purchase history and usage history to suggest home appliances according to the season or event. For example, it can suggest heaters and humidifiers in the winter and air conditioners and fans in the summer. This allows the system to predict a user's future needs and suggest the most suitable home appliances.

[0062] The suggestion unit can analyze the user's health data and suggest home appliances that are optimal for their health condition. For example, the suggestion unit uses a generation AI to analyze data from a fitness tracker and suggest home appliances that are useful for health management based on the user's exercise habits and sleep patterns. For example, it suggests exercise equipment for a user who is not getting enough exercise. The suggestion unit can also analyze medical records and suggest home appliances that address specific health conditions. For example, it suggests a blood pressure monitor or a cooking appliance that can manage salt intake for a user with high blood pressure. The suggestion unit can also suggest health management home appliances that are tailored to the user's lifestyle based on the health data. For example, it suggests a massage chair with a wide range of relaxation functions for a user who is under a lot of stress. This makes it possible to suggest the optimal home appliances according to the user's health condition.

[0063] The suggestion unit can use the emotion estimation function to analyze the user's emotional state and suggest home appliances that are useful for stress reduction and relaxation. For example, the suggestion unit uses the emotion estimation function to analyze the user's stress level and suggest home appliances that are useful for stress reduction. For example, it suggests an aroma diffuser or massage equipment with a relaxation effect. The suggestion unit also monitors the user's emotional state in real time and suggests home appliances that have a relaxing effect. For example, it suggests a speaker with a rich music playback function or a relaxation light. The suggestion unit also suggests home appliances that correspond to the user's emotional state based on the emotion estimation data. For example, if the user is feeling very tired, it suggests home appliances that have a refreshing effect, and if the user wants to relax, it suggests home appliances with a rich relaxation function. In this way, it is possible to suggest home appliances that correspond to the user's emotional state.

[0064] The suggestion unit can suggest home appliances with enhanced entertainment functions based on the user's hobbies and interests. For example, the suggestion unit analyzes the user's hobbies and interests and suggests home appliances with enhanced entertainment functions based on the analysis. For example, a high-definition television or home theater system is suggested for a user whose hobby is watching movies. The suggestion unit also collects data related to hobbies and suggests home appliances that match the user's interests. For example, high-quality speakers or headphones are suggested for a user who likes music. The suggestion unit also suggests home appliances with enhanced entertainment functions based on the user's interests. For example, the latest game console or gaming chair is suggested for a user who likes games. In this way, home appliances with enhanced entertainment functions can be suggested that match the user's hobbies and interests.

[0065] The suggestion unit can analyze the user's living environment and suggest home appliances of the optimal size or design. For example, the suggestion unit analyzes the user's living environment data and suggests home appliances of the optimal size and design for the size and layout of the room. For example, compact home appliances are suggested for small rooms. The suggestion unit also analyzes the room layout and suggests home appliances that can be optimally placed. For example, it suggests a television or audio system that matches the layout of the living room. The suggestion unit also suggests home appliances with a high level of design based on the living environment data. For example, it suggests home appliances with a design that matches the interior and improves the atmosphere of the room. This makes it possible to suggest home appliances that are optimal for the user's living environment.

[0066] The suggestion unit can use the emotion estimation function to monitor in real time the emotions of a user when using a home appliance and make suggestions based on the user's experience of use. For example, the suggestion unit can use the emotion estimation function to monitor in real time the emotions of a user when using a home appliance and suggest home appliances based on the user's experience of use. For example, the suggestion unit can prioritize suggesting home appliances that evoke a high level of positive emotions during use. The suggestion unit can also analyze the user's emotion data and suggest home appliances based on the user's experience of use. For example, the suggestion unit can suggest home appliances that have a high relaxation effect during use. The suggestion unit can also suggest home appliances that are appropriate for the user's experience of use based on the emotion estimation data. For example, the suggestion unit can suggest home appliances that cause less stress during use and provide a comfortable user experience. This makes it possible to suggest home appliances based on the user's experience of use.

[0067] The negotiation unit can analyze past price fluctuation data and suggest the optimal timing for purchase. For example, the generation AI in the negotiation unit analyzes past price fluctuation data and predicts when the price of a specific home appliance will fall. For example, it suggests the optimal time to purchase based on price fluctuations during sales periods and seasonally. The negotiation unit also builds a system that notifies users of the optimal timing for purchase based on price fluctuation data. For example, it sends an alert when a price drop is predicted. The negotiation unit also analyzes past price data to identify when the price of a specific home appliance will be lowest. For example, it suggests the timing of specific events such as Black Friday or year-end sales. This allows the optimal purchase timing to be suggested, allowing users to purchase home appliances at the most advantageous price.

[0068] The negotiation unit can compare multiple sales channels and propose the most cost-effective option. For example, the generation AI in the negotiation unit collects price information from online and physical stores and compares multiple sales channels. For example, it compares prices for the same home appliance on multiple sites and proposes the cheapest option. The negotiation unit also evaluates cost performance by taking into account not only the price for each sales channel, but also shipping costs and warranty details. For example, it proposes options that come with free shipping or a long warranty. The generation AI in the negotiation unit also collects price information in real time and notifies the user of the most cost-effective option. For example, it sends an alert when the price drops. This allows the user to select the most cost-effective option.

[0069] The negotiation unit can suggest home appliances in the optimal price range based on the user's budget. For example, the generation AI analyzes the user's budget information and suggests home appliances in the optimal price range within that range. For example, if the budget is limited, it will suggest home appliances with high cost performance. The negotiation unit also compares home appliances in multiple price ranges based on the user's budget and suggests the most suitable option. For example, it will suggest home appliances with the best performance within the budget. The negotiation unit also presents the user with home appliance options for each price range based on the budget information. For example, it will generate a list of home appliances that can be purchased within the budget and provide the user with options. This makes it possible to suggest home appliances in the optimal price range according to the user's budget.

[0070] The negotiation unit can analyze a user's purchasing history and propose repeat customer discounts and special offers. For example, the generation AI analyzes a user's purchasing history and proposes repeat customer discounts and special offers. For example, a repeat customer discount is offered to a user who has previously purchased the same brand of home appliance. The negotiation unit also proposes special offers and coupons to users who have a high purchase history from a specific brand or store based on their purchasing history. For example, a discount is offered according to the number of purchases made at a specific store. The negotiation unit also analyzes a user's purchasing history and proposes special campaigns for repeat customers. For example, an upgraded model of a home appliance previously purchased is offered at a special price. In this way, repeat customer discounts and special offers are proposed, increasing the user's desire to purchase.

[0071] The negotiation unit can use the emotion estimation function to provide support for reducing stress felt by the user during price negotiations. For example, the negotiation unit uses the emotion estimation function to monitor the stress felt by the user during price negotiations in real time and provide support for stress reduction. For example, by playing music with a relaxing effect. The negotiation unit also provides advice for reducing stress during price negotiations based on the user's emotion data. For example, by providing advice on how to proceed with negotiations and the timing of negotiations. The negotiation unit also provides an interface for reducing stress felt by the user during price negotiations based on the emotion estimation data. For example, by displaying visuals with a relaxing effect when stress increases. This makes it possible to provide support for reducing stress felt by the user during price negotiations.

[0072] The collection unit can analyze collection company evaluation data and suggest the most reliable company. For example, the collection unit uses a generation AI to analyze collection company evaluation data and suggest the most reliable company based on user reviews and evaluation scores. For example, it prioritizes suggesting companies with high past user satisfaction. The collection unit also scores the reliability of collection companies based on the evaluation data and suggests the most suitable company based on that score. For example, it prioritizes displaying companies with high reliability scores. The collection unit also analyzes collection company evaluation data in real time and suggests the most reliable company based on the latest evaluation information. For example, it prioritizes suggesting companies with high recent ratings. In this way, by suggesting reliable collection companies, users can dispose of their old home appliances with peace of mind.

[0073] The collection unit can analyze the user's schedule and propose the optimal collection date and time. For example, the generation AI in the collection unit analyzes the user's schedule data and proposes the optimal collection date and time. For example, it prioritizes proposals for times when the user is at home. The collection unit also proposes the optimal date and time based on the user's calendar information and the availability of the collection company. For example, it selects and proposes a day when the user has fewer plans. The collection unit also analyzes schedule data in real time and proposes a collection date and time that suits the user's convenience. For example, it proposes a flexible date and time to accommodate sudden schedule changes. This makes it possible to propose the optimal collection date and time that suits the user's schedule.

[0074] The pickup unit can use the emotion estimation function to provide support to reduce the user's anxiety during the pickup procedure. For example, the pickup unit uses the emotion estimation function to monitor the user's anxiety during the pickup procedure in real time and provide support to reduce the anxiety. For example, it displays a message that gives a sense of security. The pickup unit also provides advice to reduce the user's anxiety during the pickup procedure based on the user's emotion data. For example, it reports the progress of the procedure on a point-by-point basis. The pickup unit also provides an interface to reduce the user's anxiety during the pickup procedure based on the emotion estimation data. For example, it displays a visual that has a relaxing effect when anxiety increases. This makes it possible to provide support to reduce the user's anxiety during the pickup procedure.

[0075] The pickup unit can compare the fees of pickup companies and propose the most cost-effective option. For example, the generation AI in the pickup unit collects fee information from multiple pickup companies and proposes the most cost-effective option. For example, it proposes the cheapest company that offers the same services. The pickup unit also evaluates cost-effectiveness by taking into account not only the fees of pickup companies but also additional services and warranty details. For example, it proposes companies that offer free additional services. The generation AI in the pickup unit also collects fee information in real time and notifies the user of the most cost-effective option. For example, it sends an alert when the fee drops. This makes it possible to propose the most cost-effective pickup option.

[0076] The collection unit can analyze the service content of the collection company and propose additional services. For example, the generation AI in the collection unit analyzes the service content of the collection company and proposes additional services that are beneficial to the user. For example, it can propose a new home appliance installation service at the same time as collection. The collection unit also proposes recycling and disposal options based on the service content of the collection company. For example, it can propose an environmentally friendly recycling service. The generation AI in the collection unit also analyzes the service content in real time and proposes additional services that meet the user's needs. For example, it can propose a cleaning service at the same time as collection. This makes it possible to propose additional services that are beneficial to the user.

[0077] The pickup unit can use the emotion estimation function to monitor the user's emotions during the pickup procedure and provide support at the appropriate time. The pickup unit, for example, uses the emotion estimation function to monitor the user's emotions during the pickup procedure in real time and provide support at the appropriate time. For example, it checks whether the procedure is proceeding smoothly. The pickup unit also provides support during the pickup procedure based on the user's emotion data. For example, it reports the progress if the procedure is delayed. The pickup unit also monitors the user's emotions during the pickup procedure based on the emotion estimation data and sends reminders and alerts at the appropriate time. For example, it sends a notification when the procedure is completed. This makes it possible to monitor the user's emotions during the pickup procedure and provide support at the appropriate time.

[0078] The posting unit can analyze the user's usage history and automatically generate specific usage impressions or evaluations. In the posting unit, for example, a generation AI analyzes the user's usage history and automatically generates specific usage impressions and evaluations. For example, the performance and usability of a home appliance are evaluated based on the frequency and duration of use. The posting unit also extracts the user's evaluation points based on the usage history and posts automatically generated reviews. For example, the user may specifically describe how a particular function was convenient or how it could be improved. The posting unit also analyzes the usage history using the generation AI, compares it with the evaluations of other users, and extracts common evaluation points. For example, the review is posted in comparison with the evaluations of other users who use the same home appliance. This makes it possible to automatically generate specific usage impressions and evaluations based on the user's usage history.

[0079] The posting unit can analyze other users' review data, extract common evaluation points, and post them. For example, the posting unit uses a generation AI to analyze other users' review data, extract common evaluation points, and post them. For example, it emphasizes points that many users have evaluated. The posting unit also extracts common evaluation points based on the review data and reflects them in automatically generated reviews. For example, it posts evaluations of specific functions or designs together. The posting unit also builds a system that analyzes other users' review data, extracts common evaluation points, and posts them. For example, it emphasizes points that have a lot of positive reviews and posts them. This makes it possible to analyze other users' review data, extract common evaluation points, and post them.

[0080] The posting unit can link with the user's SNS account and automatically share reviews. For example, the posting unit builds a system in which the generation AI links with the user's SNS account and automatically shares reviews. For example, it automatically posts reviews of purchased home appliances on SNS. The posting unit also links with the SNS account and automatically shares reviews, thereby sharing information with other users. For example, it posts with a specific hashtag. The posting unit also provides a function in which the generation AI links with the user's SNS account and automatically shares reviews. For example, it displays a share option to SNS when posting a review. This allows the user's reviews to be automatically shared on SNS.

[0081] The posting unit can suggest recommended home appliances to other users based on the user's ratings. For example, the posting unit uses a generation AI to analyze the user's rating data and suggest recommended home appliances to other users. For example, it suggests home appliances with the same rating points. The posting unit also builds a system that suggests recommended home appliances to other users based on the user's ratings. For example, it prioritizes suggesting home appliances with high ratings. The posting unit also provides a function that allows the generation AI to suggest recommended home appliances to other users based on the user's rating data. For example, it lists and suggests highly rated home appliances. This makes it possible to suggest recommended home appliances to other users based on the user's ratings.

[0082] The posting unit can use the emotion estimation function to monitor the emotions of users while posting reviews and provide support for drawing out positive emotions. The posting unit, for example, uses the emotion estimation function to monitor the emotions of users while posting reviews in real time and provide support for drawing out positive emotions. For example, it displays positive feedback. The posting unit also provides advice for drawing out positive emotions while posting reviews based on the user's emotion data. For example, it displays a message encouraging positive expression. The posting unit also monitors the emotions of users while posting reviews based on the emotion estimation data and provides an interface for drawing out positive emotions. For example, it displays visuals that enhance positive emotions. This makes it possible to monitor the emotions of users while posting reviews and provide support for drawing out positive emotions.

[0083] The management unit can analyze the usage data of home appliances and automatically notify users when maintenance is required. For example, the management unit builds a system in which a generation AI analyzes the usage data of home appliances and automatically notifies users when maintenance is required. For example, it predicts when maintenance is required based on usage time and frequency. The management unit also identifies when home appliance maintenance is required based on usage data and notifies users. For example, it notifies users when filters need to be replaced or cleaned. The management unit also provides a function in which a generation AI analyzes usage data in real time and automatically notifies users when maintenance is required. For example, it sends maintenance alerts based on usage status. This makes it possible to automatically notify users when home appliance maintenance is required.

[0084] The management unit can analyze the failure data of home appliances and detect signs of failure early. For example, the management unit constructs a system in which a generation AI analyzes the failure data of home appliances and detects signs of failure early. For example, it analyzes abnormal behavior and error messages and predicts the possibility of failure. The management unit also detects specific patterns based on the failure data and identifies signs of failure early. For example, it notifies the user of the possibility of a failure if a specific error message occurs frequently. The management unit also provides a function in which a generation AI analyzes failure data in real time and detects signs of failure early. For example, it sends an alert when abnormal behavior is detected. This makes it possible to detect signs of failure in home appliances early.

[0085] The management unit can use the emotion estimation function to provide support to reduce the user's stress when a malfunction occurs. For example, the management unit uses the emotion estimation function to monitor the user's stress in real time when a malfunction occurs and provide support to reduce stress. For example, it displays a message that gives a sense of security. The management unit also provides advice to reduce stress when a malfunction occurs based on the user's emotion data. For example, it provides an easy-to-understand explanation of the cause of the malfunction and how to repair it. The management unit also provides an interface to reduce the user's stress when a malfunction occurs based on the emotion estimation data. For example, it displays a visual that has a relaxing effect when stress increases. This makes it possible to provide support to reduce the user's stress when a malfunction occurs.

[0086] The management unit can manage the warranty period of home appliances and notify users before the warranty expires. For example, the management unit builds a system in which a generation AI manages the warranty period of home appliances and notifies users before the warranty expires. For example, it sends an alert when the warranty period is running low. The management unit also suggests extended warranty options to users before the warranty expires based on the warranty period. For example, it sends information about extended warranties before the warranty period expires. The management unit also provides a function in which the generation AI manages the warranty period in real time and notifies users before the warranty expires. For example, it sends a reminder one month before the warranty period expires. This makes it possible to manage the warranty period of home appliances and notify users before the warranty expires.

[0087] The management unit can analyze the user manuals for home appliances and customize and display functions that users use frequently. For example, the management unit constructs a system in which a generation AI analyzes the user manuals for home appliances and customizes and displays functions that users use frequently. For example, it prioritizes and displays frequently used functions. The management unit also identifies functions that users use frequently based on the user manuals and provides customized manuals. For example, it displays detailed explanations about specific functions. The management unit also provides a function in which a generation AI analyzes the user manuals in real time and customizes and displays functions that users use frequently. For example, it displays functions that are used frequently at the top. This allows the user to customize and display functions that they use frequently.

[0088] The management unit can use the emotion estimation function to monitor the user's emotions when a breakdown occurs and suggest a repairer at the appropriate time. The management unit, for example, uses the emotion estimation function to monitor the user's emotions when a breakdown occurs in real time and suggest a repairer at the appropriate time. For example, it provides information about repairers when stress levels rise. The management unit also builds a system that suggests repairers at the appropriate time when a breakdown occurs based on the user's emotion data. For example, it provides information about repairers when emotions are stable. The management unit also provides a function that monitors the user's emotions when a breakdown occurs and suggests a repairer at the appropriate time based on the emotion estimation data. For example, it provides information about repairers when emotions have calmed down. This makes it possible to monitor the user's emotions when a breakdown occurs and suggest a repairer at the appropriate time.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The suggestion unit can suggest home appliances with enhanced entertainment functions based on the user's hobbies and interests. For example, the suggestion unit analyzes the user's hobbies and interests and suggests home appliances with enhanced entertainment functions based on the results. For example, a high-definition television or home theater system is suggested for a user whose hobby is watching movies. The suggestion unit also collects data related to hobbies and suggests home appliances that match the user's interests. For example, high-quality speakers or headphones are suggested for a user who likes music. The suggestion unit also suggests home appliances with enhanced entertainment functions based on the user's interests. For example, the latest game console or gaming chair is suggested for a user who likes games. In this way, home appliances with enhanced entertainment functions can be suggested that match the user's hobbies and interests.

[0091] The suggestion unit can analyze the user's living environment and suggest home appliances of the optimal size or design. For example, the suggestion unit analyzes the user's living environment data and suggests home appliances of the optimal size and design for the size and layout of the room. For example, compact home appliances are suggested for small rooms. The suggestion unit also analyzes the room layout and suggests home appliances that can be optimally placed. For example, it suggests a television or audio system that matches the layout of the living room. The suggestion unit also suggests home appliances with a high level of design based on the living environment data. For example, it suggests home appliances with a design that matches the interior and improves the atmosphere of the room. This makes it possible to suggest home appliances that are optimal for the user's living environment.

[0092] The suggestion unit can use the emotion estimation function to analyze the user's emotional state and suggest home appliances that are useful for stress reduction and relaxation. For example, the suggestion unit uses the emotion estimation function to analyze the user's stress level and suggest home appliances that are useful for stress reduction. For example, it suggests an aroma diffuser or massage equipment with a relaxation effect. The suggestion unit also monitors the user's emotional state in real time and suggests home appliances that have a relaxing effect. For example, it suggests a speaker with a rich music playback function or a relaxation light. The suggestion unit also suggests home appliances that correspond to the user's emotional state based on the emotion estimation data. For example, if the user is feeling very tired, it suggests home appliances that have a refreshing effect, and if the user wants to relax, it suggests home appliances with a rich relaxation function. In this way, it is possible to suggest home appliances that correspond to the user's emotional state.

[0093] The suggestion unit can analyze the user's health data and suggest home appliances that are optimal for their health condition. For example, the suggestion unit uses a generation AI to analyze data from a fitness tracker and suggest home appliances that are useful for health management based on the user's exercise habits and sleep patterns. For example, it suggests exercise equipment for a user who is not getting enough exercise. The suggestion unit can also analyze medical records and suggest home appliances that address specific health conditions. For example, it suggests a blood pressure monitor or a cooking appliance that can manage salt intake for a user with high blood pressure. The suggestion unit can also suggest health management home appliances that are tailored to the user's lifestyle based on the health data. For example, it suggests a massage chair with a wide range of relaxation functions for a user who is under a lot of stress. This makes it possible to suggest the optimal home appliances according to the user's health condition.

[0094] The suggestion unit can use the emotion estimation function to monitor in real time the emotions of a user when using a home appliance and make suggestions based on the user's experience of use. For example, the suggestion unit can use the emotion estimation function to monitor in real time the emotions of a user when using a home appliance and suggest home appliances based on the user's experience of use. For example, the suggestion unit can prioritize suggesting home appliances that evoke a high level of positive emotions during use. The suggestion unit can also analyze the user's emotion data and suggest home appliances based on the user's experience of use. For example, the suggestion unit can suggest home appliances that have a high relaxation effect during use. The suggestion unit can also suggest home appliances that are appropriate for the user's experience of use based on the emotion estimation data. For example, the suggestion unit can suggest home appliances that cause less stress during use and provide a comfortable user experience. This makes it possible to suggest home appliances based on the user's experience of use.

[0095] The negotiation unit can analyze past price fluctuation data and suggest the optimal timing for purchase. For example, the generation AI in the negotiation unit analyzes past price fluctuation data and predicts when the price of a specific home appliance will fall. For example, it suggests the optimal time to purchase based on price fluctuations during sales periods and seasonally. The negotiation unit also builds a system that notifies users of the optimal timing for purchase based on price fluctuation data. For example, it sends an alert when a price drop is predicted. The negotiation unit also analyzes past price data to identify when the price of a specific home appliance will be lowest. For example, it suggests the timing of specific events such as Black Friday or year-end sales. This allows the optimal purchase timing to be suggested, allowing users to purchase home appliances at the most advantageous price.

[0096] The negotiation unit can compare multiple sales channels and propose the most cost-effective option. For example, the generation AI in the negotiation unit collects price information from online and physical stores and compares multiple sales channels. For example, it compares prices for the same home appliance on multiple sites and proposes the cheapest option. The negotiation unit also evaluates cost performance by taking into account not only the price for each sales channel, but also shipping costs and warranty details. For example, it proposes options that come with free shipping or a long warranty. The generation AI in the negotiation unit also collects price information in real time and notifies the user of the most cost-effective option. For example, it sends an alert when the price drops. This allows the user to select the most cost-effective option.

[0097] The negotiation unit can use the emotion estimation function to provide support for reducing stress felt by the user during price negotiations. For example, the negotiation unit uses the emotion estimation function to monitor the stress felt by the user during price negotiations in real time and provide support for stress reduction. For example, by playing music with a relaxing effect. The negotiation unit also provides advice for reducing stress during price negotiations based on the user's emotion data. For example, by providing advice on how to proceed with negotiations and the timing of negotiations. The negotiation unit also provides an interface for reducing stress felt by the user during price negotiations based on the emotion estimation data. For example, by displaying visuals with a relaxing effect when stress increases. This makes it possible to provide support for reducing stress felt by the user during price negotiations.

[0098] The collection unit can analyze the user's schedule and propose the optimal collection date and time. For example, the generation AI in the collection unit analyzes the user's schedule data and proposes the optimal collection date and time. For example, it prioritizes proposals for times when the user is at home. The collection unit also proposes the optimal date and time based on the user's calendar information and the availability of the collection company. For example, it selects and proposes a day when the user has fewer plans. The collection unit also analyzes schedule data in real time and proposes a collection date and time that suits the user's convenience. For example, it proposes a flexible date and time to accommodate sudden schedule changes. This makes it possible to propose the optimal collection date and time that suits the user's schedule.

[0099] The pickup unit can use the emotion estimation function to provide support to reduce the user's anxiety during the pickup procedure. For example, the pickup unit uses the emotion estimation function to monitor the user's anxiety during the pickup procedure in real time and provide support to reduce the anxiety. For example, it displays a message that gives a sense of security. The pickup unit also provides advice to reduce the user's anxiety during the pickup procedure based on the user's emotion data. For example, it reports the progress of the procedure on a point-by-point basis. The pickup unit also provides an interface to reduce the user's anxiety during the pickup procedure based on the emotion estimation data. For example, it displays a visual that has a relaxing effect when anxiety increases. This makes it possible to provide support to reduce the user's anxiety during the pickup procedure.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The proposal unit suggests home appliances that suit the lifestyles of seniors. For example, if a senior lives alone, the proposal unit will suggest compact and easy-to-use home appliances. If health management is important, the proposal unit will suggest home appliances with comprehensive health management functions. Furthermore, the proposal unit can use the generation AI to analyze the user's past purchase history and usage history, predicting and suggesting future needs. Step 2: The negotiation unit negotiates the price when purchasing home appliances. For example, the negotiation unit allows the generation AI to collect price information from online and physical stores and propose the optimal price. The negotiation unit can also allow the generation AI to automatically negotiate the price and purchase at the optimal price. Furthermore, the negotiation unit can analyze past price fluctuation data and propose the optimal purchase timing. Step 3: The collection department arranges for the old appliances to be collected. For example, the generation AI searches for companies that offer collection services and suggests the most suitable company. The collection department can also automate the collection procedures, allowing seniors to dispose of their old appliances without any hassle. Furthermore, the collection department can analyze the evaluation data of collection companies and suggest the most reliable company. Step 4: The posting unit supports the posting of purchase reviews and usability information. For example, the posting unit automatically collects usage impressions and reviews of home appliances purchased by the generation AI and posts them on review sites and social media. The generation AI can also support the posting process so that seniors can easily post reviews. Furthermore, the posting unit can analyze other users' review data, extract common evaluation points, and post them. Step 5: The management department manages the purchased home appliances, stores the manuals, and responds when they break down. For example, the management department centrally manages information about the home appliances purchased by the generation AI and stores the manuals in digital format. In addition, when an appliance breaks down, the management department can have the generation AI analyze the cause of the failure and suggest repair methods and repair companies. Furthermore, the management department can analyze the usage data of the appliances and automatically notify the user when maintenance is required.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0138] 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.

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

[0140] 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).

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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. This system uses generative AI to provide a wide range of support when seniors purchase home appliances. The proposal department proposes home appliances that suit the lifestyles of the senior generation, A negotiation department that negotiates prices when purchasing home appliances, A collection department that arranges the collection of old appliances, A posting department that supports purchase reviews, It also has a management department that manages purchased home appliances, stores manuals, and handles breakdowns. A system characterized by:

2. The proposal unit Analyze users' past purchase and usage history to predict and suggest future needs 2. The system of claim 1.

3. The negotiation unit Analyzing past price fluctuation data and proposing optimal purchase timing 2. The system of claim 1.

4. The take-up unit includes: Analyze the evaluation data of the collection companies and propose the most reliable company 2. The system of claim 1.

5. The posting unit: Analyze the user's usage history and automatically generate specific usage impressions or evaluations 2. The system of claim 1.

6. The management unit To provide the above support to reduce stress for users when a malfunction occurs.

2. The system of claim 1.

7. The proposal unit Analyzing the user's emotional state and proposing home appliances that help reduce stress and promote relaxation 2. The system of claim 1.

Images