system

Abstract

An object of a system according to an embodiment is to collect information from a plurality of EC sites and propose an optimal home appliance based on purchase information of a user.SOLUTION: A system includes an information collection part, a hearing part, and a proposal part. The information collection unit collects information related to a home electric appliance from a plurality of EC sites. The hearing unit may hear purchase information of a user. The proposal unit proposes an optimal home appliance based on the information collected by the information collection unit and the information heard by the hearing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, when selecting the optimal home appliance from multiple e-commerce sites, collecting and comparing information is cumbersome, which is inconvenient for users.

[0005] The system according to the embodiment aims to collect information from a plurality of EC sites and propose optimal home appliances based on the user's purchasing information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a hearing unit, and a proposal unit. The information collection unit collects information about home appliances from multiple e-commerce sites. The hearing unit hears purchasing information from users. The proposal unit proposes optimal home appliances based on the information collected by the information collection unit and the information heard by the hearing unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect information from multiple e-commerce sites and propose optimal home appliances based on the user's purchasing information. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The home appliance EC concierge system according to the embodiment of the present invention utilizes generative AI to assist users in purchasing home appliances on EC sites, providing a purchasing experience that is no different from that of a physical store. This allows users to purchase high-priced home appliances with peace of mind.

[0029] The home appliance e-commerce concierge system according to the embodiment includes an information collection unit, a hearing unit, and a proposal unit. The information collection unit collects information about home appliances from multiple e-commerce sites. For example, the information collection unit uses a generation AI to collect information such as price, reviews, point redemption, warranty details, and after-sales service. The information collection unit can also collect information such as, "This refrigerator is being sold for 100,000 yen on site A, with a review rating of 4.5 and a point redemption rate of 5%." The information collection unit can also collect information based on prompts containing user instructions from the generation AI. The hearing unit hears user purchasing information. For example, the hearing unit uses the generation AI to ask questions such as, "What kind of home appliance are you looking for?", "What is your budget?", and "What are the points you particularly value?" to understand the user's needs. The hearing unit can also collect information based on prompts containing user instructions from the generation AI. The proposal unit proposes optimal home appliances based on the information collected by the information collection unit and the information heard by the hearing unit. For example, the suggestion unit uses the generation AI to suggest something like, "The refrigerator that best suits your needs is the 100,000 yen refrigerator sold on site A. It has a review rating of 4.5 and offers a 5% point return. It also comes with a comprehensive warranty." The suggestion unit can also suggest the most suitable home appliance based on prompts containing user instructions by the generation AI. As a result, the home appliance e-commerce concierge system according to the embodiment allows users to make purchases with peace of mind, as the most suitable home appliance is suggested based on information from multiple e-commerce sites.

[0030] The information gathering unit can calculate a reliability score for each e-commerce site and present it to the user. For example, the generation AI in the information gathering unit analyzes past transaction data and user reviews of each e-commerce site to calculate a reliability score. For example, the score is determined based on the transaction success rate and the reliability of the reviews. The generation AI in the information gathering unit also evaluates the security measures and privacy policy of each e-commerce site to calculate a reliability score. For example, the score is determined based on whether or not an SSL certificate is in place and the efforts made to protect data. The generation AI in the information gathering unit also analyzes the customer support response status of each e-commerce site to calculate a reliability score. For example, the score is determined based on the response time to inquiries and the resolution rate. This allows users to select highly reliable e-commerce sites.

[0031] The information collection unit analyzes past price fluctuation data and can predict the optimal timing for purchase. For example, the generation AI in the information collection unit collects past price data from each e-commerce site and analyzes price fluctuation patterns. For example, it predicts the optimal timing for purchase based on seasonal price fluctuations and sale periods. The information collection unit also uses the generation AI to predict future price trends based on the price fluctuation data from each e-commerce site. For example, the AI ​​learns from past data and predicts the next sale period or when prices will fall. The information collection unit also uses the generation AI to analyze price fluctuation data from each e-commerce site in real time and notify the user of the optimal timing for purchase. For example, it can send an alert to the user when the price falls below a certain threshold. This allows the user to purchase home appliances at the optimal time.

[0032] The information collection unit can collect data on the energy efficiency and environmental impact of home appliances and provide it to the user. In the information collection unit, for example, the generation AI collects data on the energy efficiency of home appliances from each e-commerce site and provides it to the user. For example, it evaluates based on Energy Star certification and annual power consumption. In addition, the information collection unit, the generation AI collects data on the environmental impact of home appliances from each e-commerce site and provides it to the user. For example, it evaluates based on the product's recycling rate and CO2 emissions during the manufacturing process. In addition, the information collection unit, the generation AI analyzes data on energy efficiency and environmental impact and suggests the most environmentally friendly home appliances to the user. For example, it prioritizes the display of products with high energy efficiency and low environmental impact. This allows the user to select environmentally friendly home appliances.

[0033] The information collection unit can evaluate the compatibility of home appliances and present it to the user. In the information collection unit, for example, the generation AI collects data on the compatibility of home appliances from various e-commerce sites and provides it to the user. For example, the evaluation is based on compatibility with smart home devices and connection methods. In addition, the information collection unit uses the generation AI to analyze the compatibility data of home appliances and suggest the most compatible home appliances to the user. For example, products that are compatible with a specific smart home platform are preferentially displayed. In addition, the information collection unit uses the generation AI to analyze user reviews on the compatibility of home appliances and evaluate compatibility. For example, a compatibility score is calculated based on user feedback. This allows the user to select highly compatible home appliances.

[0034] The hearing unit analyzes the user's past purchasing history, predicts the user's purchasing trends, and reflects them in the proposals. In the hearing unit, for example, the generation AI collects the user's past purchasing history and analyzes purchasing trends. For example, trends are predicted based on the types and price ranges of home appliances purchased in the past. In addition, the hearing unit predicts which home appliances are likely to be purchased next based on the user's purchasing history. For example, it learns past purchasing patterns and suggests the next purchase candidate. In addition, the hearing unit uses the generation AI to analyze the user's purchasing history and make proposals based on purchasing trends. For example, it suggests products from the same brand or series as home appliances purchased in the past. This makes it possible to make proposals based on the user's purchasing trends.

[0035] The hearing unit can hear information about the user's lifestyle and family composition, and based on that, suggest the most suitable home appliances. For example, the generation AI in the hearing unit hears information about the user's lifestyle and based on that, suggests the most suitable home appliances. For example, the content of the suggestions is adjusted based on whether the user lives alone or with their family. The hearing unit can also hear information about the user's family composition, and based on that, suggest the most suitable home appliances. For example, the content of the suggestions is adjusted based on the number of family members and age group. The hearing unit can also analyze information about the user's lifestyle and family composition, and based on that, suggest the most suitable home appliances. For example, it can suggest home appliances that are suitable if the user has pets. This makes it possible to make suggestions based on the user's lifestyle and family composition.

[0036] The hearing unit can hear information about the user's health condition and lifestyle habits and, based on that, suggest health-conscious home appliances. For example, the generation AI in the hearing unit hears information about the user's health condition and, based on that, suggests health-conscious home appliances. For example, if the user has allergies, it can suggest an air purifier that is suitable. The hearing unit can also hear information about the user's lifestyle habits and, based on that, suggest health-conscious home appliances. For example, if the user has an exercise habit, it can suggest fitness equipment that is suitable. The hearing unit can also analyze information about the user's health condition and lifestyle habits and, based on that, suggest health-conscious home appliances. For example, if the user is health-conscious, it can suggest cooking appliances that are suitable. This makes it possible to make suggestions based on the user's health condition and lifestyle habits.

[0037] The suggestion unit can simulate usage scenarios for the proposed home appliances and visually present them to the user. For example, the suggestion unit simulates usage scenarios for the home appliances proposed by the generation AI and visually presents them to the user. For example, it displays the internal structure and storage methods of a refrigerator using a 3D model. The suggestion unit also simulates usage scenarios for the home appliances proposed by the generation AI using videos and visually presents them to the user. For example, it explains how to operate a washing machine and the washing cycle using videos. The suggestion unit also provides usage scenarios for the home appliances proposed by the generation AI as interactive simulations, allowing the user to actually operate and experience them. For example, it simulates operating an air conditioner remote control. This allows the user to visually understand the usage scenarios for the proposed home appliances.

[0038] The suggestion unit can calculate the long-term cost of the home appliances it suggests and present it to the user. For example, the suggestion unit calculates the annual electricity bill for the home appliances suggested by the generation AI and presents it to the user. For example, it calculates the electricity bill based on the annual power consumption of a refrigerator. The suggestion unit also calculates the maintenance costs for the home appliances suggested by the generation AI and presents it to the user. For example, it calculates the cost based on the costs of replacing air conditioner filters and regular inspections. The suggestion unit also comprehensively calculates the long-term cost of the home appliances suggested by the generation AI and presents it to the user. For example, it calculates the total cost including the electricity bill, maintenance costs, and warranty extension costs. This allows the user to understand the long-term cost of the suggested home appliances.

[0039] The suggestion unit presents suggested customization options for home appliances to the user, thereby expanding the options available. For example, the suggestion unit presents the user with customization options for home appliances suggested by the generation AI. For example, the suggestion unit allows the user to select the color and design of a refrigerator. The suggestion unit also simulates the customization options for home appliances and visually presents them to the user. For example, the suggestion unit changes and displays the panel design of an air conditioner. The suggestion unit also suggests customization options based on the user's preferences. For example, the suggestion unit presents optimal customization options based on the user's past selection history. This allows the user to select customization options for home appliances.

[0040] The suggestion unit can provide videos of how to use and maintain the suggested home appliances to deepen the user's understanding. For example, the suggestion unit can provide videos of how to use the home appliances suggested by the generation AI to visually explain to the user. For example, a video can show how to operate a washing machine. The suggestion unit can also provide videos of how to maintain the home appliances suggested by the generation AI to visually explain to the user. For example, a video can show how to change the filter of an air conditioner. The suggestion unit can also provide videos of how to use and maintain the home appliances suggested by the generation AI to deepen the user's understanding. For example, a video can show how to clean a refrigerator. This allows the user to understand how to use and maintain the home appliances.

[0041] The follow-up unit monitors the user's usage after purchase and can suggest optimal usage and maintenance methods. For example, the generation AI monitors the user's usage after purchase and suggests optimal usage methods. For example, it suggests refrigerator temperature settings and storage methods. The follow-up unit also monitors the user's usage after purchase and suggests optimal maintenance methods. For example, it notifies the user when it is time to replace the air conditioner filter. The follow-up unit also analyzes the user's usage data and suggests optimal usage and maintenance methods. For example, it suggests the appropriate frequency of use and maintenance methods for a washing machine. This allows the user to understand how to use and maintain their home appliances after purchase.

[0042] The follow-up section can collect user feedback after purchase and reflect it in the next proposal. In the follow-up section, for example, the generation AI collects user feedback after purchase and reflects it in the next proposal. For example, it collects the user's satisfaction with the home appliance they purchased and areas for improvement. In the follow-up section, the generation AI also analyzes the user's feedback and adjusts the next proposal. For example, it makes a proposal that improves areas where the user expressed dissatisfaction. In the follow-up section, the generation AI also optimizes the next proposal based on the post-purchase feedback. For example, it makes a proposal that matches the user's preferences and needs. This allows the next proposal to be optimized based on the user's feedback.

[0043] The follow-up department can provide feedback to manufacturers on improvements to the product based on user feedback after purchase. In the follow-up department, for example, the generation AI collects user feedback after purchase and provides feedback on improvements to the product to the manufacturer. For example, it reports defects and areas for improvement pointed out by users. In the follow-up department, the generation AI also analyzes user feedback and identifies areas for improvements to the product. For example, it prioritizes improvements to areas that users have expressed the most dissatisfaction with. In the follow-up department, the generation AI also proposes improvements to the manufacturer on the product based on post-purchase feedback. For example, it proposes new functions or design improvements that reflect user opinions. This allows manufacturers to improve their products based on user feedback.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The home appliance e-commerce concierge system can further include a history analysis unit that analyzes a user's purchasing history. The history analysis unit collects data on home appliances purchased in the past by the user and analyzes the user's purchasing trends. For example, it can predict the home appliance that the user is likely to purchase next based on the type, price range, and purchase frequency of home appliances purchased in the past. The history analysis unit can also identify preferences for specific brands or series based on the user's purchasing history and adjust the content of suggestions based on that. Furthermore, the history analysis unit can analyze the user's usage and satisfaction with home appliances purchased in the past and reflect this in the next suggestions. This enables more personalized suggestions based on the user's purchasing history.

[0046] The home appliance e-commerce concierge system can further include a lifestyle analysis unit that customizes the content of proposals based on the user's lifestyle. The lifestyle analysis unit collects information about the user's lifestyle habits and family composition, and suggests the most suitable home appliances based on that information. For example, the proposals can be adjusted based on whether the user lives alone or with their family. The lifestyle analysis unit can also customize the content of proposals based on the user's hobbies and interests. For example, it can suggest highly functional cooking appliances to a user whose hobby is cooking. Furthermore, the lifestyle analysis unit can suggest health-conscious home appliances based on the user's health condition and exercise habits. This makes it possible to make proposals tailored to the user's lifestyle.

[0047] The home appliance e-commerce concierge system can further include a health analysis unit that customizes the content of recommendations based on the user's health condition. The health analysis unit collects information about the user's health condition and lifestyle habits and recommends health-conscious home appliances based on that information. For example, if the user has allergies, it can recommend an appropriate air purifier. The health analysis unit can also recommend fitness equipment and health management home appliances based on the user's exercise habits. Furthermore, the health analysis unit can also recommend health-conscious cooking appliances based on the user's diet. This makes it possible to make recommendations tailored to the user's health condition.

[0048] The home appliance e-commerce concierge system can further include an incentive provision unit to increase users' purchasing motivation. The incentive provision unit provides benefits and discounts when users purchase home appliances. For example, it can provide point rewards or coupons when a specific home appliance is purchased. The incentive provision unit can also provide benefits that are individually customized based on the user's purchasing history and purchasing trends. For example, a user who previously purchased a specific brand can be offered a discount on new products of that brand. Furthermore, the incentive provision unit can notify users of limited-time sales information and special campaigns to increase users' purchasing motivation. This can increase users' purchasing motivation.

[0049] The home appliance e-commerce concierge system can further include an interactive suggestion unit to increase the user's desire to purchase. The interactive suggestion unit provides an interactive simulation that allows the user to actually operate and experience the appliance. For example, it can simulate the operation of an air conditioner remote control, allowing the user to actually operate and experience it. The interactive suggestion unit can also provide suggested usage scenarios for the appliance as interactive simulations, allowing the user to visually understand them. For example, it can display the internal structure and storage methods of a refrigerator using a 3D model. Furthermore, the interactive suggestion unit can select the optimal suggestion content based on the user's operation history. This allows the user to visually understand the suggested usage scenarios for the appliance, increasing their desire to purchase.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The information collection unit collects information about home appliances from multiple e-commerce sites. For example, the information collection unit uses the generation AI to collect information such as price, reviews, point redemption, warranty details, and after-sales service. The information collection unit can also collect information such as "This refrigerator is sold for 100,000 yen on site A, with a review rating of 4.5 and point redemption of 5%." Furthermore, the information collection unit can also collect information based on prompts containing user instructions from the generation AI. Step 2: The hearing department gathers purchasing information from the user. For example, the hearing department uses the generation AI to ask questions such as, "What kind of home appliance are you looking for?", "What is your budget?", and "What are the points you particularly value?" to understand the user's needs. The hearing department can also gather information based on prompts, including user instructions, from the generation AI. Step 3: The proposal unit proposes the optimal home appliance based on the information collected by the information collection unit and the information obtained by the hearing unit. For example, the proposal unit uses the generation AI to propose something like, "The refrigerator that best suits your needs is the 100,000 yen refrigerator sold on site A. It has a review rating of 4.5 and offers a 5% point return. It also has a comprehensive warranty." The proposal unit can also use the generation AI to propose the optimal home appliance based on prompts containing user instructions.

[0052] (Example 2) The home appliance EC concierge system according to the embodiment of the present invention utilizes generative AI to assist users in purchasing home appliances on EC sites, providing a purchasing experience that is no different from that of a physical store. This allows users to purchase high-priced home appliances with peace of mind.

[0053] The home appliance e-commerce concierge system according to the embodiment includes an information collection unit, a hearing unit, and a proposal unit. The information collection unit collects information about home appliances from multiple e-commerce sites. For example, the information collection unit uses a generation AI to collect information such as price, reviews, point redemption, warranty details, and after-sales service. The information collection unit can also collect information such as, "This refrigerator is being sold for 100,000 yen on site A, with a review rating of 4.5 and a point redemption rate of 5%." The information collection unit can also collect information based on prompts containing user instructions from the generation AI. The hearing unit hears user purchasing information. For example, the hearing unit uses the generation AI to ask questions such as, "What kind of home appliance are you looking for?", "What is your budget?", and "What are the points you particularly value?" to understand the user's needs. The hearing unit can also collect information based on prompts containing user instructions from the generation AI. The proposal unit proposes optimal home appliances based on the information collected by the information collection unit and the information heard by the hearing unit. For example, the suggestion unit uses the generation AI to suggest something like, "The refrigerator that best suits your needs is the 100,000 yen refrigerator sold on site A. It has a review rating of 4.5 and offers a 5% point return. It also comes with a comprehensive warranty." The suggestion unit can also suggest the most suitable home appliance based on prompts containing user instructions by the generation AI. As a result, the home appliance e-commerce concierge system according to the embodiment allows users to make purchases with peace of mind, as the most suitable home appliance is suggested based on information from multiple e-commerce sites.

[0054] The information gathering unit can calculate a reliability score for each e-commerce site and present it to the user. For example, the generation AI in the information gathering unit analyzes past transaction data and user reviews of each e-commerce site to calculate a reliability score. For example, the score is determined based on the transaction success rate and the reliability of the reviews. The generation AI in the information gathering unit also evaluates the security measures and privacy policy of each e-commerce site to calculate a reliability score. For example, the score is determined based on whether or not an SSL certificate is in place and the efforts made to protect data. The generation AI in the information gathering unit also analyzes the customer support response status of each e-commerce site to calculate a reliability score. For example, the score is determined based on the response time to inquiries and the resolution rate. This allows users to select highly reliable e-commerce sites.

[0055] The information collection unit analyzes past price fluctuation data and can predict the optimal timing for purchase. For example, the generation AI in the information collection unit collects past price data from each e-commerce site and analyzes price fluctuation patterns. For example, it predicts the optimal timing for purchase based on seasonal price fluctuations and sale periods. The information collection unit also uses the generation AI to predict future price trends based on the price fluctuation data from each e-commerce site. For example, the AI ​​learns from past data and predicts the next sale period or when prices will fall. The information collection unit also uses the generation AI to analyze price fluctuation data from each e-commerce site in real time and notify the user of the optimal timing for purchase. For example, it can send an alert to the user when the price falls below a certain threshold. This allows the user to purchase home appliances at the optimal time.

[0056] The information gathering unit can analyze the emotional tone of the reviews and display positive and negative reviews separately. For example, the information gathering unit uses a generation AI to perform sentiment analysis of reviews on each e-commerce site and classify them into positive and negative reviews. For example, it calculates the sentiment score of the reviews using natural language processing technology. The information gathering unit also uses the generation AI to analyze the emotional tone of the reviews and display positive and negative reviews separately to the user. For example, it displays positive reviews at the top and negative reviews at the bottom. The information gathering unit also prioritizes the most useful reviews for the user based on the results of the sentiment analysis of the reviews by the generation AI. For example, it highlights reviews with high sentiment scores. This allows users to increase the credibility of the reviews.

[0057] The information collection unit can collect data on the energy efficiency and environmental impact of home appliances and provide it to the user. In the information collection unit, for example, the generation AI collects data on the energy efficiency of home appliances from each e-commerce site and provides it to the user. For example, it evaluates based on Energy Star certification and annual power consumption. In addition, the information collection unit, the generation AI collects data on the environmental impact of home appliances from each e-commerce site and provides it to the user. For example, it evaluates based on the product's recycling rate and CO2 emissions during the manufacturing process. In addition, the information collection unit, the generation AI analyzes data on energy efficiency and environmental impact and suggests the most environmentally friendly home appliances to the user. For example, it prioritizes the display of products with high energy efficiency and low environmental impact. This allows the user to select environmentally friendly home appliances.

[0058] The information collection unit can evaluate the compatibility of home appliances and present it to the user. In the information collection unit, for example, the generation AI collects data on the compatibility of home appliances from various e-commerce sites and provides it to the user. For example, the evaluation is based on compatibility with smart home devices and connection methods. In addition, the information collection unit uses the generation AI to analyze the compatibility data of home appliances and suggest the most compatible home appliances to the user. For example, products that are compatible with a specific smart home platform are preferentially displayed. In addition, the information collection unit uses the generation AI to analyze user reviews on the compatibility of home appliances and evaluate compatibility. For example, a compatibility score is calculated based on user feedback. This allows the user to select highly compatible home appliances.

[0059] The information gathering unit prioritizes displaying reviews that interest the user most, thereby increasing purchasing motivation. For example, the information gathering unit uses the generation AI to perform sentiment analysis of reviews on each e-commerce site and prioritizes displaying reviews that interest the user most. For example, reviews with high sentiment scores are displayed at the top. The information gathering unit also uses the generation AI to analyze the emotional tone of reviews and prioritizes displaying reviews that are most helpful to the user. For example, reviews with strong positive sentiment are highlighted. The information gathering unit also prioritizes displaying reviews that will increase the user's purchasing motivation based on the results of the sentiment analysis of reviews by the generation AI. For example, reviews with high sentiment scores are displayed prominently. This increases the user's purchasing motivation.

[0060] The hearing unit analyzes the user's past purchasing history, predicts the user's purchasing trends, and reflects them in the proposals. In the hearing unit, for example, the generation AI collects the user's past purchasing history and analyzes purchasing trends. For example, trends are predicted based on the types and price ranges of home appliances purchased in the past. In addition, the hearing unit predicts which home appliances are likely to be purchased next based on the user's purchasing history. For example, it learns past purchasing patterns and suggests the next purchase candidate. In addition, the hearing unit uses the generation AI to analyze the user's purchasing history and make proposals based on purchasing trends. For example, it suggests products from the same brand or series as home appliances purchased in the past. This makes it possible to make proposals based on the user's purchasing trends.

[0061] The hearing unit can hear information about the user's lifestyle and family composition, and based on that, suggest the most suitable home appliances. For example, the generation AI in the hearing unit hears information about the user's lifestyle and based on that, suggests the most suitable home appliances. For example, the content of the suggestions is adjusted based on whether the user lives alone or with their family. The hearing unit can also hear information about the user's family composition, and based on that, suggest the most suitable home appliances. For example, the content of the suggestions is adjusted based on the number of family members and age group. The hearing unit can also analyze information about the user's lifestyle and family composition, and based on that, suggest the most suitable home appliances. For example, it can suggest home appliances that are suitable if the user has pets. This makes it possible to make suggestions based on the user's lifestyle and family composition.

[0062] The hearing unit analyzes the user's emotions when answering a question and can identify the points that the user values ​​most. For example, the generation AI in the hearing unit analyzes the user's emotions when answering in real time and identifies the points that the user values ​​most. For example, it analyzes facial expressions and tone of voice when answering. The generation AI also performs emotional analysis of the user's answers and identifies the points that the user values ​​most. For example, it prioritizes analysis of answers with strong positive emotions. The generation AI also identifies the points that the user values ​​most based on emotional data when the user answered. For example, it adjusts the content of suggestions based on answers with high emotional scores. This makes it possible to identify the points that the user values ​​most and optimize the content of suggestions.

[0063] The hearing unit can hear information about the user's health condition and lifestyle habits and, based on that, suggest health-conscious home appliances. For example, the generation AI in the hearing unit hears information about the user's health condition and, based on that, suggests health-conscious home appliances. For example, if the user has allergies, it can suggest an air purifier that is suitable. The hearing unit can also hear information about the user's lifestyle habits and, based on that, suggest health-conscious home appliances. For example, if the user has an exercise habit, it can suggest fitness equipment that is suitable. The hearing unit can also analyze information about the user's health condition and lifestyle habits and, based on that, suggest health-conscious home appliances. For example, if the user is health-conscious, it can suggest cooking appliances that are suitable. This makes it possible to make suggestions based on the user's health condition and lifestyle habits.

[0064] The hearing unit can identify the features of home appliances that indicate the most positive emotions from the user and emphasize them when making suggestions. For example, the generation AI in the hearing unit analyzes the emotions of the user when answering in real time and identifies the features of home appliances that indicate the most positive emotions. For example, it analyzes facial expressions and tone of voice when answering. The hearing unit also performs emotional analysis of the content of the user's answers and identifies the features of home appliances that indicate the most positive emotions. For example, it prioritizes analysis of answers with strong positive emotions. The hearing unit also identifies the features of home appliances that indicate the most positive emotions based on the emotional data of the user when answering and emphasizes them when making suggestions. For example, it adjusts the content of suggestions based on answers with high emotion scores. This allows the features of home appliances that indicate the most positive emotions to be emphasized when making suggestions.

[0065] The suggestion unit can simulate usage scenarios for the proposed home appliances and visually present them to the user. For example, the suggestion unit simulates usage scenarios for the home appliances proposed by the generation AI and visually presents them to the user. For example, it displays the internal structure and storage methods of a refrigerator using a 3D model. The suggestion unit also simulates usage scenarios for the home appliances proposed by the generation AI using videos and visually presents them to the user. For example, it explains how to operate a washing machine and the washing cycle using videos. The suggestion unit also provides usage scenarios for the home appliances proposed by the generation AI as interactive simulations, allowing the user to actually operate and experience them. For example, it simulates operating an air conditioner remote control. This allows the user to visually understand the usage scenarios for the proposed home appliances.

[0066] The suggestion unit can calculate the long-term cost of the home appliances it suggests and present it to the user. For example, the suggestion unit calculates the annual electricity bill for the home appliances suggested by the generation AI and presents it to the user. For example, it calculates the electricity bill based on the annual power consumption of a refrigerator. The suggestion unit also calculates the maintenance costs for the home appliances suggested by the generation AI and presents it to the user. For example, it calculates the cost based on the costs of replacing air conditioner filters and regular inspections. The suggestion unit also comprehensively calculates the long-term cost of the home appliances suggested by the generation AI and presents it to the user. For example, it calculates the total cost including the electricity bill, maintenance costs, and warranty extension costs. This allows the user to understand the long-term cost of the suggested home appliances.

[0067] The suggestion unit can analyze the user's emotions toward the proposed home appliances in real time and adjust the content of the suggestions. For example, the generation AI in the suggestion unit analyzes the user's facial expressions and voice to analyze their emotions toward the proposed home appliances in real time. For example, if the user smiles, it determines that the emotion is positive. The suggestion unit also adjusts the content of the suggestions in real time based on the user's emotional response. For example, if the user has a negative response, it will suggest a different home appliance. The suggestion unit also analyzes the user's emotional data and selects the content of the suggestions that elicit the most positive emotions. For example, it will highlight the features of the home appliance that the user is interested in. This allows the content of the suggestions to be optimized based on the user's emotions.

[0068] The suggestion unit presents suggested customization options for home appliances to the user, thereby expanding the options available. For example, the suggestion unit presents the user with customization options for home appliances suggested by the generation AI. For example, the suggestion unit allows the user to select the color and design of a refrigerator. The suggestion unit also simulates the customization options for home appliances and visually presents them to the user. For example, the suggestion unit changes and displays the panel design of an air conditioner. The suggestion unit also suggests customization options based on the user's preferences. For example, the suggestion unit presents optimal customization options based on the user's past selection history. This allows the user to select customization options for home appliances.

[0069] The suggestion unit can provide videos of how to use and maintain the suggested home appliances to deepen the user's understanding. For example, the suggestion unit can provide videos of how to use the home appliances suggested by the generation AI to visually explain to the user. For example, a video can show how to operate a washing machine. The suggestion unit can also provide videos of how to maintain the home appliances suggested by the generation AI to visually explain to the user. For example, a video can show how to change the filter of an air conditioner. The suggestion unit can also provide videos of how to use and maintain the home appliances suggested by the generation AI to deepen the user's understanding. For example, a video can show how to clean a refrigerator. This allows the user to understand how to use and maintain the home appliances.

[0070] The suggestion unit can optimize the content of suggestions by emphasizing the features of home appliances that interest the user most. For example, the suggestion unit uses a generation AI to analyze the user's emotional response and identify the features of the home appliance that interest the user most. For example, it emphasizes features that the user responded positively to. The suggestion unit also optimizes the content of suggestions based on the user's emotional data. For example, it emphasizes the functions and designs of home appliances that interest the user. The suggestion unit also adjusts the content of suggestions by using the emotion estimation function of the generation AI to emphasize the features of the home appliance that interest the user most. For example, it prioritizes displaying features with high emotion scores. This allows the content of suggestions to be optimized based on the user's interests.

[0071] The follow-up unit monitors the user's usage after purchase and can suggest optimal usage and maintenance methods. For example, the generation AI monitors the user's usage after purchase and suggests optimal usage methods. For example, it suggests refrigerator temperature settings and storage methods. The follow-up unit also monitors the user's usage after purchase and suggests optimal maintenance methods. For example, it notifies the user when it is time to replace the air conditioner filter. The follow-up unit also analyzes the user's usage data and suggests optimal usage and maintenance methods. For example, it suggests the appropriate frequency of use and maintenance methods for a washing machine. This allows the user to understand how to use and maintain their home appliances after purchase.

[0072] The follow-up section can collect user feedback after purchase and reflect it in the next proposal. In the follow-up section, for example, the generation AI collects user feedback after purchase and reflects it in the next proposal. For example, it collects the user's satisfaction with the home appliance they purchased and areas for improvement. In the follow-up section, the generation AI also analyzes the user's feedback and adjusts the next proposal. For example, it makes a proposal that improves areas where the user expressed dissatisfaction. In the follow-up section, the generation AI also optimizes the next proposal based on the post-purchase feedback. For example, it makes a proposal that matches the user's preferences and needs. This allows the next proposal to be optimized based on the user's feedback.

[0073] The follow-up unit can analyze the emotions a user feels after a purchase and provide support to provide a positive experience. For example, the generation AI analyzes the user's post-purchase emotions in real time and provides support to provide a positive experience. For example, if the user is satisfied, additional advice is provided. The follow-up unit also adjusts the post-purchase support content based on the user's emotional data. For example, it responds quickly if the user expresses dissatisfaction. The follow-up unit also uses the emotion estimation function of the generation AI to analyze the emotions a user feels after a purchase and provides support to provide a positive experience. For example, additional services are provided if the emotion score is high. This makes it possible to provide a positive experience based on the user's emotions.

[0074] The follow-up department can provide feedback to manufacturers on improvements to the product based on user feedback after purchase. In the follow-up department, for example, the generation AI collects user feedback after purchase and provides feedback on improvements to the product to the manufacturer. For example, it reports defects and areas for improvement pointed out by users. In the follow-up department, the generation AI also analyzes user feedback and identifies areas for improvements to the product. For example, it prioritizes improvements to areas that users have expressed the most dissatisfaction with. In the follow-up department, the generation AI also proposes improvements to the manufacturer on the product based on post-purchase feedback. For example, it proposes new functions or design improvements that reflect user opinions. This allows manufacturers to improve their products based on user feedback.

[0075] The follow-up unit can use the emotion estimation function to identify and provide the support content that will most satisfy the user. In the follow-up unit, for example, the generation AI analyzes the user's emotion data and identifies the support content that will most satisfy the user. For example, the support content for which the user expressed positive emotions is provided preferentially. In addition, the follow-up unit can use the emotion estimation function to identify and provide the support content that will most satisfy the user. For example, the support content with a high emotion score is emphasized. In addition, the follow-up unit can use the generation AI to provide the support content that will most satisfy the user based on the user's emotional response. For example, if the user is satisfied, additional advice or services are provided. This allows the user to receive the most satisfying support.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The home appliance e-commerce concierge system can further include a history analysis unit that analyzes a user's purchasing history. The history analysis unit collects data on home appliances purchased in the past by the user and analyzes the user's purchasing trends. For example, it can predict the home appliance that the user is likely to purchase next based on the type, price range, and purchase frequency of home appliances purchased in the past. The history analysis unit can also identify preferences for specific brands or series based on the user's purchasing history and adjust the content of suggestions based on that. Furthermore, the history analysis unit can analyze the user's usage and satisfaction with home appliances purchased in the past and reflect this in the next suggestions. This enables more personalized suggestions based on the user's purchasing history.

[0078] The home appliance e-commerce concierge system can further include a lifestyle analysis unit that customizes the content of proposals based on the user's lifestyle. The lifestyle analysis unit collects information about the user's lifestyle habits and family composition, and suggests the most suitable home appliances based on that information. For example, the proposals can be adjusted based on whether the user lives alone or with their family. The lifestyle analysis unit can also customize the content of proposals based on the user's hobbies and interests. For example, it can suggest highly functional cooking appliances to a user whose hobby is cooking. Furthermore, the lifestyle analysis unit can suggest health-conscious home appliances based on the user's health condition and exercise habits. This makes it possible to make proposals tailored to the user's lifestyle.

[0079] The home appliance e-commerce concierge system can further include an emotion estimation unit that estimates the user's emotions and adjusts the content of the suggestions. The emotion estimation unit analyzes the user's facial expressions and voice tone to estimate the user's emotions in real time. For example, if the user smiles, it can be determined that the emotion is positive and the content of the suggestions can be emphasized. The emotion estimation unit can also select the content of the suggestions that elicit the most positive emotions based on the user's emotional data. For example, it can emphasize the features of home appliances that the user is interested in. Furthermore, the emotion estimation unit can adjust the content of the suggestions in real time based on the user's emotional response. This makes it possible to make optimal suggestions based on the user's emotions.

[0080] The home appliance e-commerce concierge system can further include a health analysis unit that customizes the content of recommendations based on the user's health condition. The health analysis unit collects information about the user's health condition and lifestyle habits and recommends health-conscious home appliances based on that information. For example, if the user has allergies, it can recommend an appropriate air purifier. The health analysis unit can also recommend fitness equipment and health management home appliances based on the user's exercise habits. Furthermore, the health analysis unit can also recommend health-conscious cooking appliances based on the user's diet. This makes it possible to make recommendations tailored to the user's health condition.

[0081] The home appliance e-commerce concierge system can further include a review analysis unit that estimates user emotions and evaluates the reliability of reviews. The review analysis unit performs sentiment analysis on reviews from each e-commerce site and classifies them into positive and negative reviews. For example, it can use natural language processing technology to calculate a sentiment score for each review and prioritize the display of highly reliable reviews. The review analysis unit can also highlight the most useful reviews based on user sentiment data. For example, reviews with high sentiment scores can be displayed at the top. Furthermore, the review analysis unit can evaluate the reliability of reviews based on the user's emotional response and provide the user with the most useful reviews. This allows users to select home appliances based on reliable reviews.

[0082] The home appliance e-commerce concierge system can further include an incentive provision unit to increase users' purchasing motivation. The incentive provision unit provides benefits and discounts when users purchase home appliances. For example, it can provide point rewards or coupons when a specific home appliance is purchased. The incentive provision unit can also provide benefits that are individually customized based on the user's purchasing history and purchasing trends. For example, a user who previously purchased a specific brand can be offered a discount on new products of that brand. Furthermore, the incentive provision unit can notify users of limited-time sales information and special campaigns to increase users' purchasing motivation. This can increase users' purchasing motivation.

[0083] The home appliance e-commerce concierge system can further include a timing suggestion unit that estimates the user's emotions and suggests the optimal timing for purchase. The timing suggestion unit predicts the optimal timing for purchase based on the user's emotional data. For example, it can make a suggestion encouraging a purchase when the user shows positive emotions. The timing suggestion unit can also analyze past price fluctuation data and predict the optimal timing for purchase. For example, it can predict sale periods and price drop times and notify the user. Furthermore, the timing suggestion unit can also suggest the optimal timing for purchase in real time based on the user's emotional response. This allows the user to purchase home appliances at the optimal time.

[0084] The home appliance e-commerce concierge system can further include a proposal optimization unit that estimates the user's emotions and optimizes the content of proposals. The proposal optimization unit selects proposals that elicit the most positive emotions based on the user's emotional data. For example, it can emphasize the features of home appliances that the user is interested in. The proposal optimization unit can also adjust the proposals in real time based on the user's emotional response. For example, if the user has a negative response, it can suggest a different home appliance. Furthermore, the proposal optimization unit can analyze the user's emotional data and select proposals that elicit the most positive emotions. This makes it possible to make optimal proposals based on the user's emotions.

[0085] The home appliance e-commerce concierge system can further include a support optimization unit that estimates the user's emotions and optimizes the post-purchase support content. The support optimization unit identifies the most satisfying support content based on the user's emotional data. For example, it can prioritize the support content for which the user expressed positive emotions. The support optimization unit can also adjust the post-purchase support content in real time based on the user's emotional response. For example, it can respond quickly if the user expresses dissatisfaction. Furthermore, the support optimization unit can analyze the user's emotional data and identify the most satisfying support content. This makes it possible to provide optimal support based on the user's emotions.

[0086] The home appliance e-commerce concierge system can further include an interactive suggestion unit to increase the user's desire to purchase. The interactive suggestion unit provides an interactive simulation that allows the user to actually operate and experience the appliance. For example, it can simulate the operation of an air conditioner remote control, allowing the user to actually operate and experience it. The interactive suggestion unit can also provide suggested usage scenarios for the appliance as interactive simulations, allowing the user to visually understand them. For example, it can display the internal structure and storage methods of a refrigerator using a 3D model. Furthermore, the interactive suggestion unit can select the optimal suggestion content based on the user's operation history. This allows the user to visually understand the suggested usage scenarios for the appliance, increasing their desire to purchase.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The information collection unit collects information about home appliances from multiple e-commerce sites. For example, the information collection unit uses the generation AI to collect information such as price, reviews, point redemption, warranty details, and after-sales service. The information collection unit can also collect information such as "This refrigerator is sold for 100,000 yen on site A, with a review rating of 4.5 and point redemption of 5%." Furthermore, the information collection unit can also collect information based on prompts containing user instructions from the generation AI. Step 2: The hearing department gathers purchasing information from the user. For example, the hearing department uses the generation AI to ask questions such as, "What kind of home appliance are you looking for?", "What is your budget?", and "What are the points you particularly value?" to understand the user's needs. The hearing department can also gather information based on prompts, including user instructions, from the generation AI. Step 3: The proposal unit proposes the optimal home appliance based on the information collected by the information collection unit and the information obtained by the hearing unit. For example, the proposal unit uses the generation AI to propose something like, "The refrigerator that best suits your needs is the 100,000 yen refrigerator sold on site A. It has a review rating of 4.5 and offers a 5% point return. It also has a comprehensive warranty." The proposal unit can also use the generation AI to propose the optimal home appliance based on prompts containing user instructions.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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. An information gathering department that collects information about home appliances from multiple e-commerce sites; a hearing section for hearing user purchasing information; a proposal unit that proposes an optimal home appliance based on the information collected by the information collection unit and the information heard by the hearing unit. A system characterized by:

2. The information collecting unit Calculate the reliability score of each e-commerce site and present it to the user 2. The system of claim 1.

3. The information collecting unit Analyzing past price fluctuation data to predict optimal purchasing timing 2. The system of claim 1.

4. The information collecting unit Analyzes the emotional tone of reviews and displays positive and negative reviews separately 2. The system of claim 1.

5. The information collecting unit Collecting data on the energy efficiency and environmental impact of home appliances and providing it to users 2. The system of claim 1.

Images