System

Abstract

An object of a system according to an embodiment is to propose an optimal product when a user selects a home appliance.SOLUTION: A system according to an embodiment includes a reception unit, a provision unit, a comparison unit, and a proposal unit. The reception unit receives a question about the home appliance from the user. The providing unit provides the product information based on the question received by the receiving unit. The comparing unit compares a plurality of products based on the product information provided by the providing unit. The proposal unit asks the user about the user's needs based on the product information compared by the comparison unit, and proposes a product based on the user's needs.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for users to obtain appropriate product information when selecting home appliances, and it took a long time to select the most suitable product.

[0005] The system according to the embodiment aims to suggest the most suitable product when the user selects a home appliance. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a provision unit, a comparison unit, and a suggestion unit. The reception unit receives questions about home appliances from a user. The provision unit provides product information based on the questions received by the reception unit. The comparison unit compares multiple products based on the product information provided by the provision unit. The suggestion unit listens to the user's needs based on the product information compared by the comparison unit and suggests products based on the user's needs. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable product when the user is selecting a home appliance. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A home appliance selection support system according to an embodiment of the present invention accepts questions about home appliances from users, provides product information, compares multiple products, and recommends the most suitable product. When a user asks a question about a home appliance through an app, an AI assistant provides product information and compares multiple products. Furthermore, the system further encourages dialogue to suggest products that best suit the user's needs. For example, if a user inputs "I'm looking for an air conditioner," the AI ​​assistant provides information such as the type, price, and features of the air conditioner and compares multiple air conditioners. It then asks the user about their budget, room size, and intended use, and recommends the most suitable air conditioner. In addition to providing product information in stores, the AI ​​assistant also shows the room layout and assists in the purchase of multiple products. For example, if a user is looking for a refrigerator in a store, the AI ​​assistant provides information such as the refrigerator's size, features, and price, and recommends the optimal refrigerator for the room layout. It also suggests other home appliances to purchase along with the refrigerator, ensuring a smooth shopping experience. This allows users to easily find home appliances that meet their needs and enjoy a smooth shopping experience in stores.

[0029] A home appliance selection support system according to an embodiment includes a reception unit, a provision unit, a comparison unit, and a suggestion unit. The reception unit accepts questions about home appliances from a user. Questions about home appliances from a user include, but are not limited to, product features, prices, and usage methods. The reception unit can accept questions by voice input, text input, image input, or other methods. The provision unit provides product information based on the questions accepted by the reception unit. Product information includes, but is not limited to, specifications, prices, and reviews. The provision unit acquires information in real time using a product database. For example, the provision unit acquires the latest price information and inventory status in real time and provides it to the user. The comparison unit compares multiple products based on the product information provided by the provision unit. Comparison criteria include, but are not limited to, price, performance, and user reviews. The comparison unit can compare products based on multiple criteria, such as lowest price, highest performance, or highest user review rating. The suggestion unit listens to the user's needs based on the product information compared by the comparison unit and suggests optimal products. The user's needs include, but are not limited to, for example, the purpose of use, budget, and preferences. The suggestion unit asks the user about, for example, the user's budget, room size, purpose of use, and the like, and suggests optimal products. In this way, the home appliance selection support system according to the embodiment can efficiently support the user in selecting home appliances and suggest optimal products.

[0030] The suggestion unit can ask the user about their budget, room size, purpose of use, etc., and suggest products based on the user's needs. For example, the suggestion unit can ask the user about their budget and suggest products that are optimal within the budget. For example, the suggestion unit can receive the user's budget as input and suggest products that can be purchased within that budget. The suggestion unit can also ask the user about the size of their room and suggest products that are suitable for that size. For example, the suggestion unit can receive the user's room size as input and suggest air conditioners, refrigerators, etc. that are suitable for that size. The suggestion unit can also ask the user about their purpose of use and suggest products that are suitable for that purpose. For example, the suggestion unit can receive the user's purpose of use as input and suggest home appliances that are suitable for that purpose. In this way, it is possible to suggest optimal products based on the user's specific needs.

[0031] The providing unit can obtain information in real time by using a product database. For example, the providing unit periodically updates the product database to maintain the latest information. For example, the providing unit updates the database daily to obtain the latest price information and stock status. The providing unit can also access the database in real time to obtain the latest information. For example, the providing unit accesses the database in response to a user's question to obtain the latest information. The providing unit can also analyze the information in the database and select information to provide to the user. For example, the providing unit analyzes the information in the database and provides information that is most appropriate for the user's question. This makes it possible to provide the latest product information in real time.

[0032] The comparison unit can compare multiple products. The comparison unit, for example, sorts and compares products in ascending order of price. For example, the comparison unit receives product price information as input and sorts the products in descending order of price. The comparison unit can also sort and compare products in descending order of performance. For example, the comparison unit receives product performance information as input and sorts the products in descending order of performance. The comparison unit can also sort and compare products in descending order of user review ratings. For example, the comparison unit receives user review rating information as input and sorts the products in descending order of rating. This makes it possible to compare multiple products and provide the user with the optimal option.

[0033] The suggestion unit can provide product information in a store and display the layout of a room. For example, the suggestion unit provides information about a product that a user is looking for in a store. For example, if a user is looking for a refrigerator in a store, the suggestion unit can provide information such as the size, functions, and price of the refrigerator. The suggestion unit can also display the layout of a room and show how the user will arrange products. For example, the suggestion unit receives the layout of the user's room as input and shows where the refrigerator will be placed. The suggestion unit can also suggest products that are optimal for the layout of the room. For example, the suggestion unit suggests the optimal refrigerator based on the layout of the user's room. This can improve the shopping experience in a store.

[0034] The suggestion unit can support the purchase of multiple products. For example, when a user purchases a refrigerator, the suggestion unit suggests other home appliances that should be purchased together with the refrigerator. For example, the suggestion unit suggests a washing machine, a microwave oven, etc. that should be purchased together with the refrigerator. The suggestion unit can also suggest a set sale or bundle sale of multiple products. For example, the suggestion unit suggests a set sale of a refrigerator and a washing machine. The suggestion unit can also provide discount information regarding the purchase of multiple products. For example, the suggestion unit provides discount information for purchasing a refrigerator and a washing machine at the same time. This allows the user to purchase multiple products efficiently.

[0035] The reception unit can analyze the user's past question history and select the optimal question reception method. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. For example, the reception unit stores the user's past question history in a database and prioritizes displaying frequently asked questions. The reception unit can also prioritize suggesting a question method (voice, text, etc.) that the user has used in the past. For example, the reception unit analyzes the user's past question history and suggests the optimal question method. The reception unit can also predict and suggest the question method to be used in a specific time period based on the user's past question history. For example, the reception unit prioritizes voice input in a specific time period based on the user's past question history. This makes it possible to provide the optimal question reception method based on the user's past question history.

[0036] When receiving a question, the reception unit can filter the questions based on the user's current areas of interest. For example, the reception unit preferentially receives questions related to home appliances recently searched by the user. For example, the reception unit analyzes the user's search history and preferentially displays related questions. The reception unit can also filter questions based on categories in which the user is interested (e.g., kitchen appliances). For example, the reception unit stores the user's interest categories in a database and preferentially displays related questions. The reception unit can also analyze the user's social media activity and preferentially receive questions related to the user's areas of interest. For example, the reception unit analyzes the content of the user's social media posts and preferentially displays related questions. In this way, by filtering questions based on the user's areas of interest, more relevant questions can be received.

[0037] When accepting a question, the acceptance unit can select an acceptance means according to the user's input method. For example, when the user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. For example, the acceptance unit records the user's voice with a microphone and converts it into text using voice recognition technology. Furthermore, when the user inputs a question by text, the acceptance unit can also analyze the question using natural language processing technology. For example, the acceptance unit analyzes the user's text input and understands the intent of the question. Furthermore, when the user uploads an image, the acceptance unit can also accept the question using image recognition technology. For example, the acceptance unit analyzes the image uploaded by the user and identifies related questions. This makes it possible to provide the optimal question acceptance means according to the user's input method.

[0038] When receiving a question, the reception unit can prioritize receiving the question based on the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving questions about home appliances related to that area. For example, the reception unit acquires the user's geographical location information from GPS data or an IP address, and prioritizes displaying questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions about home appliances to be used at the user's destination. For example, the reception unit prioritizes displaying questions about home appliances to be used at the user's destination based on the user's geographical location information. Furthermore, if the user is at home, the reception unit can prioritize receiving questions about home appliances to be used at home. For example, the reception unit prioritizes displaying questions about home appliances to be used at home based on the user's geographical location information. This makes it possible to prioritize receiving questions that are highly relevant based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, if the user posts about home appliances on social media, the reception unit preferentially receives questions related to the post. For example, the reception unit analyzes the content of the user's social media post and preferentially displays related questions. The reception unit can also receive related questions by referring to the activity of the user's friends on social media. For example, if the user's friends post about home appliances on social media, the reception unit preferentially displays questions related to the post. The reception unit can also analyze the user's social media interests and preferentially receive related questions. For example, the reception unit analyzes the user's social media interests and preferentially displays related questions. This makes it possible to receive related questions based on the user's social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit, for example, suggests an optimal question reception method based on feedback provided by the user in the past. For example, the reception unit stores the user's past feedback in a database and suggests an optimal question reception method based on that feedback. The reception unit can also preferentially suggest a specific question method (voice, text, etc.) based on the user's past feedback. For example, the reception unit can analyze the user's past feedback and suggest an optimal question method. The reception unit can also analyze the user's past feedback and select an optimal question reception method. For example, the reception unit preferentially suggests a specific question method based on the user's past feedback. This makes it possible to provide an optimal question reception method based on the user's past feedback.

[0041] When providing product information, the providing unit can adjust the level of detail of the information based on the importance of the product. For example, the providing unit provides important product information (price, functions, etc.) with priority. For example, the providing unit evaluates the importance of the product and displays important product information with priority. The providing unit can also provide detailed information according to the importance of the product. For example, the providing unit displays detailed specification information and user reviews based on the importance of the product. The providing unit can also provide product information with low importance in a concise manner. For example, the providing unit displays concise information based on the importance of the product. This makes it possible to provide detailed product information according to the importance of the product.

[0042] When providing product information, the providing unit can apply different providing algorithms depending on the product category. For example, in the case of kitchen appliances, the providing unit provides information depending on the usage scene. For example, the providing unit receives the usage scene of the kitchen appliance as input and provides information depending on the scene. In addition, in the case of entertainment appliances, the providing unit can also provide visually attractive information. For example, the providing unit receives the usage scene of the entertainment appliance as input and provides visually attractive information. In addition, in the case of health appliances, the providing unit can also provide information regarding health benefits. For example, the providing unit receives the usage scene of the health appliance as input and provides information regarding health benefits. This makes it possible to provide optimal product information depending on the product category.

[0043] When providing product information, the providing unit can improve the accuracy of the provision by referring to the user's past provision results. The providing unit, for example, provides related product information based on product information purchased by the user in the past. For example, the providing unit stores the user's past purchase history in a database and provides related product information based on that history. The providing unit can also provide optimal product information based on the user's past provision results. For example, the providing unit can analyze the user's past provision results and provide optimal product information. The providing unit can also analyze the user's past provision results and improve the accuracy of the provision. For example, the providing unit improves the accuracy of the provision based on the user's past provision results. This makes it possible to provide optimal product information based on the user's past provision results.

[0044] When providing product information, the providing unit can determine the priority of information based on the time of submission of the product. The providing unit, for example, provides new product information preferentially. For example, the providing unit stores the time of submission of the product in a database and displays new product information preferentially. The providing unit can also provide seasonal product information preferentially. For example, the providing unit displays seasonal product information preferentially based on the time of submission of the product. The providing unit can also provide information on products in which the user has shown interest in the past preferentially. For example, the providing unit displays information on products in which the user has shown interest based on the user's past search history preferentially. This makes it possible to provide optimal product information based on the time of submission of the product.

[0045] When providing product information, the providing unit can adjust the order of providing the product information based on the relevance of the products. The providing unit, for example, prioritizes providing highly relevant product information. For example, the providing unit stores the product relevance in a database and prioritizes displaying highly relevant product information. The providing unit can also provide highly relevant product information based on the user's interests. For example, the providing unit prioritizes displaying information about products in which the user has shown interest based on the user's past search history. The providing unit can also adjust the order of providing the product information based on the relevance of the products. For example, the providing unit displays product information in an optimal order based on the relevance of the products. This makes it possible to provide product information in an optimal order based on the relevance of the products.

[0046] The providing unit can adjust the use of technical terms when providing product information according to the user's level of expertise. For example, if the user has technical expertise, the providing unit provides product information using technical terms. For example, the providing unit evaluates the user's level of expertise based on survey results or past purchase history, and provides product information using technical terms. The providing unit can also provide product information in simple language if the user is a beginner. For example, the providing unit provides product information in simple language based on the user's level of expertise. The providing unit can also adjust the use of technical terms when providing product information according to the user's level of expertise. For example, the providing unit selects optimal technical terms based on the user's level of expertise. This makes it possible to provide optimal product information according to the user's level of expertise.

[0047] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between products when comparing products. For example, the comparison unit improves the accuracy of the comparison by taking into account the interrelationships between related products. For example, the comparison unit stores the interrelationships between products in a database and improves the accuracy of the comparison based on the interrelationships between related products. The comparison unit can also provide optimal comparison criteria based on the interrelationships between products. For example, the comparison unit selects optimal comparison criteria based on the interrelationships between products. The comparison unit can also analyze the interrelationships between products and improve the accuracy of the comparison. For example, the comparison unit improves the accuracy of the comparison based on the interrelationships between products. In this way, the accuracy of the comparison can be improved by taking the interrelationships between products into account.

[0048] When comparing products, the comparison unit can take into account the attribute information of the person who submitted the product to make the comparison. The comparison unit improves the accuracy of the comparison, for example, by taking into account the attribute information (age, gender, etc.) of the person who submitted the product. For example, the comparison unit stores the attribute information of the person who submitted the product in a database and improves the accuracy of the comparison based on that information. The comparison unit can also provide optimal comparison criteria based on the attribute information of the person who submitted the product. For example, the comparison unit selects optimal comparison criteria based on the attribute information of the person who submitted the product. The comparison unit can also analyze the attribute information of the person who submitted the product to improve the accuracy of the comparison. For example, the comparison unit improves the accuracy of the comparison based on the attribute information of the person who submitted the product. This makes it possible to improve the accuracy of the comparison based on the attribute information of the person who submitted the product.

[0049] When comparing products, the comparison unit can weight the comparison based on the submission frequency of the products. For example, the comparison unit prioritizes the comparison of products with a high submission frequency. For example, the comparison unit stores the submission frequency of products in a database and displays products with a high submission frequency with a high submission frequency. The comparison unit can also weight the comparison based on the submission frequency. For example, the comparison unit weights the comparison based on the submission frequency of products. The comparison unit can also analyze the submission frequency and provide optimal comparison criteria. For example, the comparison unit selects optimal comparison criteria based on the submission frequency of products. By weighting the comparison based on the submission frequency of products, more appropriate comparisons are possible.

[0050] When comparing products, the comparison unit can perform the comparison based on the geographical distribution of the products. For example, the comparison unit prioritizes the comparison of geographically close products. For example, the comparison unit stores the geographical distribution of products in a database and prioritizes displaying information about geographically close products. The comparison unit can also provide optimal comparison criteria based on the geographical distribution. For example, the comparison unit selects optimal comparison criteria based on the geographical distribution of the products. The comparison unit can also analyze the geographical distribution and improve the accuracy of the comparison. For example, the comparison unit improves the accuracy of the comparison based on the geographical distribution of the products. This allows optimal comparison to be performed based on the geographical distribution of the products.

[0051] The comparison unit can improve the accuracy of the comparison when comparing products by referring to related literature for the products. For example, the comparison unit sets product comparison criteria based on the related literature. For example, the comparison unit stores related literature for the products in a database and sets comparison criteria based on the literature. The comparison unit can also improve the accuracy of the comparison by referring to the related literature. For example, the comparison unit improves the accuracy of the comparison based on related literature for the products. The comparison unit can also analyze the related literature and provide optimal comparison criteria. For example, the comparison unit selects optimal comparison criteria based on related literature for the products. In this way, the accuracy of the comparison can be improved by referring to related literature for the products.

[0052] When comparing products, the comparison unit can take the market value of the products into consideration. For example, the comparison unit prioritizes comparison of products with high market values. For example, the comparison unit stores the market values ​​of products in a database and displays products with high market values ​​preferentially. The comparison unit can also weight the comparison based on the market value. For example, the comparison unit weights the comparison based on the market value of the products. The comparison unit can also analyze the market value and provide optimal comparison criteria. For example, the comparison unit selects optimal comparison criteria based on the market value of the products. This allows optimal comparison to be performed based on the market value of the products.

[0053] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the product. For example, the proposal unit makes a detailed proposal for an important product. For example, the proposal unit evaluates the importance of the product and makes a detailed proposal for the important product. The proposal unit can also adjust the level of detail of the proposal based on the importance of the product. For example, the proposal unit makes a detailed proposal based on the importance of the product. The proposal unit can also make a concise proposal for a product with a low importance. For example, the proposal unit makes a concise proposal based on the importance of the product. This makes it possible to provide a detailed proposal according to the importance of the product.

[0054] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the product category. For example, in the case of kitchen appliances, the suggestion unit makes suggestions according to the usage scenario. For example, the suggestion unit receives the usage scenario of the kitchen appliance as input and makes suggestions according to the scenario. In addition, the suggestion unit can also make visually appealing suggestions in the case of entertainment appliances. For example, the suggestion unit receives the usage scenario of the entertainment appliance as input and makes visually appealing suggestions. In addition, the suggestion unit can also make suggestions regarding health benefits in the case of health appliances. For example, the suggestion unit receives the usage scenario of the health appliance as input and makes suggestions regarding health benefits. This makes it possible to provide optimal suggestions according to the product category.

[0055] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, suggests related products based on product information purchased by the user in the past. For example, the suggestion unit stores the user's past purchase history in a database and suggests related products based on that history. The suggestion unit can also suggest optimal products based on the user's past suggestion results. For example, the suggestion unit analyzes the user's past suggestion results and suggests optimal products. The suggestion unit can also analyze the user's past suggestion results and improve the accuracy of the suggestion. For example, the suggestion unit improves the accuracy of the suggestion based on the user's past suggestion results. This makes it possible to provide optimal suggestions based on the user's past suggestion results.

[0056] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time of submission of the product. The suggestion unit, for example, prioritizes suggesting new products. For example, the suggestion unit stores the time of submission of the product in a database and prioritizes suggesting new products. The suggestion unit can also prioritize suggesting seasonal products. For example, the suggestion unit prioritizes suggesting seasonal products based on the time of submission of the product. The suggestion unit can also prioritize suggesting products in which the user has shown interest in the past. For example, the suggestion unit prioritizes suggesting products in which the user has shown interest based on the user's past search history. This makes it possible to provide optimal suggestions based on the time of submission of the product.

[0057] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the products. The suggestion unit, for example, prioritizes suggesting highly relevant products. For example, the suggestion unit stores the relevance of products in a database and prioritizes displaying highly relevant products. The suggestion unit can also suggest highly relevant products based on the user's interests. For example, the suggestion unit prioritizes displaying information about products that the user has shown interest in based on the user's past search history. The suggestion unit can also adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit displays product information in an optimal order based on the relevance of the products. This makes it possible to provide suggestions in an optimal order based on the relevance of the products.

[0058] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes the suggestion using technical terms. For example, the suggestion unit evaluates the user's level of expertise based on survey results or past purchase history and makes the suggestion using technical terms. In addition, if the user is a beginner, the suggestion unit can make the suggestion in simple language. For example, the suggestion unit makes the suggestion in simple language based on the user's level of expertise. In addition, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, the suggestion unit selects optimal technical terms based on the user's level of expertise. This makes it possible to provide optimal suggestions according to the user's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The providing unit can provide trend information on related products based on the user's past purchase history. For example, the providing unit can suggest upgraded versions of home appliances that the user previously purchased or products with new functions. The providing unit can also suggest related accessories or complementary products based on the user's purchase history. Furthermore, the providing unit can suggest related products purchased by other users based on the user's purchase history. This makes it possible to provide more relevant product information based on the user's past purchase history.

[0061] The suggestion unit can suggest products based on the user's lifestyle. For example, if the user likes outdoor activities, the suggestion unit can suggest portable home appliances. If the user is health-conscious, the suggestion unit can also suggest home appliances with health management functions. Furthermore, if the user is interested in technology, the suggestion unit can also suggest the latest smart home appliances. This makes it possible to suggest optimal products that suit the user's lifestyle.

[0062] The comparison unit can compare products based on their eco-friendliness. For example, the comparison unit can evaluate the energy efficiency and environmental impact of products and display eco-friendly products preferentially. The comparison unit can also compare products based on their recyclability and use of sustainable materials. Furthermore, the comparison unit can take into account the environmental impact of the product's manufacturing process. This makes it possible to provide optimal comparison results to users who prioritize eco-friendliness.

[0063] The providing unit can provide region-specific promotional information based on the user's geographical location information. For example, if the user is in a specific region, the providing unit can provide information about sales and campaigns being held in that region. The providing unit can also provide region-specific discount coupons based on the user's geographical location information. Furthermore, the providing unit can also provide information about inventory status at nearby stores based on the user's geographical location information. This makes it possible to provide more relevant promotional information based on the user's geographical location information.

[0064] The suggestion unit can provide maintenance information for related products based on the user's purchasing history. For example, it can suggest maintenance schedules and precautions for home appliances purchased by the user. The suggestion unit can also provide information on related consumables and replacement parts based on the user's purchasing history. Furthermore, the suggestion unit can also provide information on product upgrades and the addition of new functions based on the user's purchasing history. This makes it possible to provide more relevant maintenance information based on the user's purchasing history.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives questions about home appliances from users. Questions from users include product features, prices, and usage methods. The reception unit can receive questions by voice input, text input, image input, and other methods. Step 2: The provider provides product information based on the question received by the reception unit. Product information includes specifications, prices, reviews, etc. The provider obtains information in real time using a product database and provides the user with the latest price information and stock status. Step 3: The comparison unit compares multiple products based on the product information provided by the provision unit. Comparison criteria include price, performance, user reviews, etc. The comparison unit can compare products based on multiple criteria, such as lowest price, highest performance, or highest user review rating. Step 4: The suggestion unit listens to the user's needs based on the product information compared by the comparison unit and suggests the most suitable product. The user's needs include the purpose of use, budget, preferences, etc. The suggestion unit listens to the user's budget, room size, purpose of use, etc. and suggests the most suitable product.

[0067] (Example 2) A home appliance selection support system according to an embodiment of the present invention accepts questions about home appliances from users, provides product information, compares multiple products, and recommends the most suitable product. When a user asks a question about a home appliance through an app, an AI assistant provides product information and compares multiple products. Furthermore, the system further encourages dialogue to suggest products that best suit the user's needs. For example, if a user inputs "I'm looking for an air conditioner," the AI ​​assistant provides information such as the type, price, and features of the air conditioner and compares multiple air conditioners. It then asks the user about their budget, room size, and intended use, and recommends the most suitable air conditioner. In addition to providing product information in stores, the AI ​​assistant also shows the room layout and assists in the purchase of multiple products. For example, if a user is looking for a refrigerator in a store, the AI ​​assistant provides information such as the refrigerator's size, features, and price, and recommends the optimal refrigerator for the room layout. It also suggests other home appliances to purchase along with the refrigerator, ensuring a smooth shopping experience. This allows users to easily find home appliances that meet their needs and enjoy a smooth shopping experience in stores.

[0068] A home appliance selection support system according to an embodiment includes a reception unit, a provision unit, a comparison unit, and a suggestion unit. The reception unit accepts questions about home appliances from a user. Questions about home appliances from a user include, but are not limited to, product features, prices, and usage methods. The reception unit can accept questions by voice input, text input, image input, or other methods. The provision unit provides product information based on the questions accepted by the reception unit. Product information includes, but is not limited to, specifications, prices, and reviews. The provision unit acquires information in real time using a product database. For example, the provision unit acquires the latest price information and inventory status in real time and provides it to the user. The comparison unit compares multiple products based on the product information provided by the provision unit. Comparison criteria include, but are not limited to, price, performance, and user reviews. The comparison unit can compare products based on multiple criteria, such as lowest price, highest performance, or highest user review rating. The suggestion unit listens to the user's needs based on the product information compared by the comparison unit and suggests optimal products. The user's needs include, but are not limited to, for example, the purpose of use, budget, and preferences. The suggestion unit asks the user about, for example, the user's budget, room size, purpose of use, and the like, and suggests optimal products. In this way, the home appliance selection support system according to the embodiment can efficiently support the user in selecting home appliances and suggest optimal products.

[0069] The suggestion unit can ask the user about their budget, room size, purpose of use, etc., and suggest products based on the user's needs. For example, the suggestion unit can ask the user about their budget and suggest products that are optimal within the budget. For example, the suggestion unit can receive the user's budget as input and suggest products that can be purchased within that budget. The suggestion unit can also ask the user about the size of their room and suggest products that are suitable for that size. For example, the suggestion unit can receive the user's room size as input and suggest air conditioners, refrigerators, etc. that are suitable for that size. The suggestion unit can also ask the user about their purpose of use and suggest products that are suitable for that purpose. For example, the suggestion unit can receive the user's purpose of use as input and suggest home appliances that are suitable for that purpose. In this way, it is possible to suggest optimal products based on the user's specific needs.

[0070] The providing unit can obtain information in real time by using a product database. For example, the providing unit periodically updates the product database to maintain the latest information. For example, the providing unit updates the database daily to obtain the latest price information and stock status. The providing unit can also access the database in real time to obtain the latest information. For example, the providing unit accesses the database in response to a user's question to obtain the latest information. The providing unit can also analyze the information in the database and select information to provide to the user. For example, the providing unit analyzes the information in the database and provides information that is most appropriate for the user's question. This makes it possible to provide the latest product information in real time.

[0071] The comparison unit can compare multiple products. The comparison unit, for example, sorts and compares products in ascending order of price. For example, the comparison unit receives product price information as input and sorts the products in descending order of price. The comparison unit can also sort and compare products in descending order of performance. For example, the comparison unit receives product performance information as input and sorts the products in descending order of performance. The comparison unit can also sort and compare products in descending order of user review ratings. For example, the comparison unit receives user review rating information as input and sorts the products in descending order of rating. This makes it possible to compare multiple products and provide the user with the optimal option.

[0072] The suggestion unit can provide product information in a store and display the layout of a room. For example, the suggestion unit provides information about a product that a user is looking for in a store. For example, if a user is looking for a refrigerator in a store, the suggestion unit can provide information such as the size, functions, and price of the refrigerator. The suggestion unit can also display the layout of a room and show how the user will arrange products. For example, the suggestion unit receives the layout of the user's room as input and shows where the refrigerator will be placed. The suggestion unit can also suggest products that are optimal for the layout of the room. For example, the suggestion unit suggests the optimal refrigerator based on the layout of the user's room. This can improve the shopping experience in a store.

[0073] The suggestion unit can support the purchase of multiple products. For example, when a user purchases a refrigerator, the suggestion unit suggests other home appliances that should be purchased together with the refrigerator. For example, the suggestion unit suggests a washing machine, a microwave oven, etc. that should be purchased together with the refrigerator. The suggestion unit can also suggest a set sale or bundle sale of multiple products. For example, the suggestion unit suggests a set sale of a refrigerator and a washing machine. The suggestion unit can also provide discount information regarding the purchase of multiple products. For example, the suggestion unit provides discount information for purchasing a refrigerator and a washing machine at the same time. This allows the user to purchase multiple products efficiently.

[0074] The reception unit can estimate the user's emotions and adjust the question reception method based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, the reception unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, the reception unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to quickly receive questions. For example, the reception unit can analyze the user's voice data in real time and estimate the user's emotions. This makes it possible to provide an optimal question reception method according to the user's emotions.

[0075] The reception unit can analyze the user's past question history and select the optimal question reception method. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. For example, the reception unit stores the user's past question history in a database and prioritizes displaying frequently asked questions. The reception unit can also prioritize suggesting a question method (voice, text, etc.) that the user has used in the past. For example, the reception unit analyzes the user's past question history and suggests the optimal question method. The reception unit can also predict and suggest the question method to be used in a specific time period based on the user's past question history. For example, the reception unit prioritizes voice input in a specific time period based on the user's past question history. This makes it possible to provide the optimal question reception method based on the user's past question history.

[0076] When receiving a question, the reception unit can filter the questions based on the user's current areas of interest. For example, the reception unit preferentially receives questions related to home appliances recently searched by the user. For example, the reception unit analyzes the user's search history and preferentially displays related questions. The reception unit can also filter questions based on categories in which the user is interested (e.g., kitchen appliances). For example, the reception unit stores the user's interest categories in a database and preferentially displays related questions. The reception unit can also analyze the user's social media activity and preferentially receive questions related to the user's areas of interest. For example, the reception unit analyzes the content of the user's social media posts and preferentially displays related questions. In this way, by filtering questions based on the user's areas of interest, more relevant questions can be received.

[0077] When accepting a question, the acceptance unit can select an acceptance means according to the user's input method. For example, when the user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. For example, the acceptance unit records the user's voice with a microphone and converts it into text using voice recognition technology. Furthermore, when the user inputs a question by text, the acceptance unit can also analyze the question using natural language processing technology. For example, the acceptance unit analyzes the user's text input and understands the intent of the question. Furthermore, when the user uploads an image, the acceptance unit can also accept the question using image recognition technology. For example, the acceptance unit analyzes the image uploaded by the user and identifies related questions. This makes it possible to provide the optimal question acceptance means according to the user's input method.

[0078] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prioritizes urgent questions. For example, the reception unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The reception unit can also prioritize detailed questions when the user is relaxed. For example, the reception unit can record the user's voice and estimate the user's emotions using voice analysis technology. The reception unit can also prioritize concise questions when the user is in a hurry. For example, the reception unit can analyze the user's voice data in real time and estimate the user's emotions. This allows for more appropriate question reception by prioritizing questions according to the user's emotions.

[0079] When receiving a question, the reception unit can prioritize receiving the question based on the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving questions about home appliances related to that area. For example, the reception unit acquires the user's geographical location information from GPS data or an IP address, and prioritizes displaying questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions about home appliances to be used at the user's destination. For example, the reception unit prioritizes displaying questions about home appliances to be used at the user's destination based on the user's geographical location information. Furthermore, if the user is at home, the reception unit can prioritize receiving questions about home appliances to be used at home. For example, the reception unit prioritizes displaying questions about home appliances to be used at home based on the user's geographical location information. This makes it possible to prioritize receiving questions that are highly relevant based on the user's geographical location information.

[0080] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, if the user posts about home appliances on social media, the reception unit preferentially receives questions related to the post. For example, the reception unit analyzes the content of the user's social media post and preferentially displays related questions. The reception unit can also receive related questions by referring to the activity of the user's friends on social media. For example, if the user's friends post about home appliances on social media, the reception unit preferentially displays questions related to the post. The reception unit can also analyze the user's social media interests and preferentially receive related questions. For example, the reception unit analyzes the user's social media interests and preferentially displays related questions. This makes it possible to receive related questions based on the user's social media activity.

[0081] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit, for example, suggests an optimal question reception method based on feedback provided by the user in the past. For example, the reception unit stores the user's past feedback in a database and suggests an optimal question reception method based on that feedback. The reception unit can also preferentially suggest a specific question method (voice, text, etc.) based on the user's past feedback. For example, the reception unit can analyze the user's past feedback and suggest an optimal question method. The reception unit can also analyze the user's past feedback and select an optimal question reception method. For example, the reception unit preferentially suggests a specific question method based on the user's past feedback. This makes it possible to provide an optimal question reception method based on the user's past feedback.

[0082] The providing unit can estimate the user's emotions and adjust the method of providing product information based on the estimated user emotions. For example, if the user is relaxed, the providing unit provides detailed product information. For example, the providing unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The providing unit can also provide concise product information if the user is in a hurry. For example, the providing unit can record the user's voice and estimate the user's emotions using voice analysis technology. For example, the providing unit can provide product information with visually stimulating effects if the user is excited. For example, the providing unit can analyze the user's voice data in real time and estimate the user's emotions. This makes it possible to provide an optimal product information provision method according to the user's emotions.

[0083] When providing product information, the providing unit can adjust the level of detail of the information based on the importance of the product. For example, the providing unit provides important product information (price, functions, etc.) with priority. For example, the providing unit evaluates the importance of the product and displays important product information with priority. The providing unit can also provide detailed information according to the importance of the product. For example, the providing unit displays detailed specification information and user reviews based on the importance of the product. The providing unit can also provide product information with low importance in a concise manner. For example, the providing unit displays concise information based on the importance of the product. This makes it possible to provide detailed product information according to the importance of the product.

[0084] When providing product information, the providing unit can apply different providing algorithms depending on the product category. For example, in the case of kitchen appliances, the providing unit provides information depending on the usage scene. For example, the providing unit receives the usage scene of the kitchen appliance as input and provides information depending on the scene. In addition, in the case of entertainment appliances, the providing unit can also provide visually attractive information. For example, the providing unit receives the usage scene of the entertainment appliance as input and provides visually attractive information. In addition, in the case of health appliances, the providing unit can also provide information regarding health benefits. For example, the providing unit receives the usage scene of the health appliance as input and provides information regarding health benefits. This makes it possible to provide optimal product information depending on the product category.

[0085] When providing product information, the providing unit can improve the accuracy of the provision by referring to the user's past provision results. The providing unit, for example, provides related product information based on product information purchased by the user in the past. For example, the providing unit stores the user's past purchase history in a database and provides related product information based on that history. The providing unit can also provide optimal product information based on the user's past provision results. For example, the providing unit can analyze the user's past provision results and provide optimal product information. The providing unit can also analyze the user's past provision results and improve the accuracy of the provision. For example, the providing unit improves the accuracy of the provision based on the user's past provision results. This makes it possible to provide optimal product information based on the user's past provision results.

[0086] The providing unit can estimate the user's emotions and adjust the length of product information provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, concise product information. For example, the providing unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Furthermore, if the user is relaxed, the providing unit can provide longer product information with detailed descriptions. For example, the providing unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the providing unit can provide product information with visually stimulating effects. For example, the providing unit can analyze the user's voice data in real time to estimate the user's emotions. This allows the optimal length of product information to be provided according to the user's emotions.

[0087] When providing product information, the providing unit can determine the priority of information based on the time of submission of the product. The providing unit, for example, provides new product information preferentially. For example, the providing unit stores the time of submission of the product in a database and displays new product information preferentially. The providing unit can also provide seasonal product information preferentially. For example, the providing unit displays seasonal product information preferentially based on the time of submission of the product. The providing unit can also provide information on products in which the user has shown interest in the past preferentially. For example, the providing unit displays information on products in which the user has shown interest based on the user's past search history preferentially. This makes it possible to provide optimal product information based on the time of submission of the product.

[0088] When providing product information, the providing unit can adjust the order of providing the product information based on the relevance of the products. The providing unit, for example, prioritizes providing highly relevant product information. For example, the providing unit stores the product relevance in a database and prioritizes displaying highly relevant product information. The providing unit can also provide highly relevant product information based on the user's interests. For example, the providing unit prioritizes displaying information about products in which the user has shown interest based on the user's past search history. The providing unit can also adjust the order of providing the product information based on the relevance of the products. For example, the providing unit displays product information in an optimal order based on the relevance of the products. This makes it possible to provide product information in an optimal order based on the relevance of the products.

[0089] The providing unit can adjust the use of technical terms when providing product information according to the user's level of expertise. For example, if the user has technical expertise, the providing unit provides product information using technical terms. For example, the providing unit evaluates the user's level of expertise based on survey results or past purchase history, and provides product information using technical terms. The providing unit can also provide product information in simple language if the user is a beginner. For example, the providing unit provides product information in simple language based on the user's level of expertise. The providing unit can also adjust the use of technical terms when providing product information according to the user's level of expertise. For example, the providing unit selects optimal technical terms based on the user's level of expertise. This makes it possible to provide optimal product information according to the user's level of expertise.

[0090] The comparison unit can estimate the user's emotions and adjust the product comparison criteria based on the estimated user emotions. For example, if the user is relaxed, the comparison unit provides detailed comparison criteria. For example, the comparison unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The comparison unit can also provide concise comparison criteria if the user is in a hurry. For example, the comparison unit can record the user's voice and estimate the emotion using voice analysis technology. For example, if the user is excited, the comparison unit can provide comparison criteria with visually stimulating effects. For example, the comparison unit can analyze the user's voice data in real time and estimate the emotion. This makes it possible to provide optimal comparison criteria according to the user's emotions.

[0091] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationships between products when comparing products. For example, the comparison unit improves the accuracy of the comparison by taking into account the interrelationships between related products. For example, the comparison unit stores the interrelationships between products in a database and improves the accuracy of the comparison based on the interrelationships between related products. The comparison unit can also provide optimal comparison criteria based on the interrelationships between products. For example, the comparison unit selects optimal comparison criteria based on the interrelationships between products. The comparison unit can also analyze the interrelationships between products and improve the accuracy of the comparison. For example, the comparison unit improves the accuracy of the comparison based on the interrelationships between products. In this way, the accuracy of the comparison can be improved by taking the interrelationships between products into account.

[0092] When comparing products, the comparison unit can take into account the attribute information of the person who submitted the product to make the comparison. The comparison unit improves the accuracy of the comparison, for example, by taking into account the attribute information (age, gender, etc.) of the person who submitted the product. For example, the comparison unit stores the attribute information of the person who submitted the product in a database and improves the accuracy of the comparison based on that information. The comparison unit can also provide optimal comparison criteria based on the attribute information of the person who submitted the product. For example, the comparison unit selects optimal comparison criteria based on the attribute information of the person who submitted the product. The comparison unit can also analyze the attribute information of the person who submitted the product to improve the accuracy of the comparison. For example, the comparison unit improves the accuracy of the comparison based on the attribute information of the person who submitted the product. This makes it possible to improve the accuracy of the comparison based on the attribute information of the person who submitted the product.

[0093] When comparing products, the comparison unit can weight the comparison based on the submission frequency of the products. For example, the comparison unit prioritizes the comparison of products with a high submission frequency. For example, the comparison unit stores the submission frequency of products in a database and displays products with a high submission frequency with a high submission frequency. The comparison unit can also weight the comparison based on the submission frequency. For example, the comparison unit weights the comparison based on the submission frequency of products. The comparison unit can also analyze the submission frequency and provide optimal comparison criteria. For example, the comparison unit selects optimal comparison criteria based on the submission frequency of products. By weighting the comparison based on the submission frequency of products, more appropriate comparisons are possible.

[0094] The comparison unit can estimate the user's emotion and adjust the display order of the comparison results based on the estimated user emotion. For example, if the user is relaxed, the comparison unit provides detailed comparison results. For example, the comparison unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The comparison unit can also provide concise comparison results if the user is in a hurry. For example, the comparison unit can record the user's voice and estimate the emotion using voice analysis technology. For example, if the user is excited, the comparison unit can provide comparison results with visually stimulating effects. For example, the comparison unit can analyze the user's voice data in real time and estimate the emotion. This makes it possible to provide an optimal display order of the comparison results according to the user's emotion.

[0095] When comparing products, the comparison unit can perform the comparison based on the geographical distribution of the products. For example, the comparison unit prioritizes the comparison of geographically close products. For example, the comparison unit stores the geographical distribution of products in a database and prioritizes displaying information about geographically close products. The comparison unit can also provide optimal comparison criteria based on the geographical distribution. For example, the comparison unit selects optimal comparison criteria based on the geographical distribution of the products. The comparison unit can also analyze the geographical distribution and improve the accuracy of the comparison. For example, the comparison unit improves the accuracy of the comparison based on the geographical distribution of the products. This allows optimal comparison to be performed based on the geographical distribution of the products.

[0096] The comparison unit can improve the accuracy of the comparison when comparing products by referring to related literature for the products. For example, the comparison unit sets product comparison criteria based on the related literature. For example, the comparison unit stores related literature for the products in a database and sets comparison criteria based on the literature. The comparison unit can also improve the accuracy of the comparison by referring to the related literature. For example, the comparison unit improves the accuracy of the comparison based on related literature for the products. The comparison unit can also analyze the related literature and provide optimal comparison criteria. For example, the comparison unit selects optimal comparison criteria based on related literature for the products. In this way, the accuracy of the comparison can be improved by referring to related literature for the products.

[0097] When comparing products, the comparison unit can take the market value of the products into consideration. For example, the comparison unit prioritizes comparison of products with high market values. For example, the comparison unit stores the market values ​​of products in a database and displays products with high market values ​​preferentially. The comparison unit can also weight the comparison based on the market value. For example, the comparison unit weights the comparison based on the market value of the products. The comparison unit can also analyze the market value and provide optimal comparison criteria. For example, the comparison unit selects optimal comparison criteria based on the market value of the products. This allows optimal comparison to be performed based on the market value of the products.

[0098] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. For example, the suggestion unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit can also provide concise suggestions if the user is in a hurry. For example, the suggestion unit records the user's voice and estimates the user's emotions using voice analysis technology. For example, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, the suggestion unit analyzes the user's voice data in real time and estimates the user's emotions. This makes it possible to provide an optimal way to present suggestions according to the user's emotions.

[0099] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the product. For example, the proposal unit makes a detailed proposal for an important product. For example, the proposal unit evaluates the importance of the product and makes a detailed proposal for the important product. The proposal unit can also adjust the level of detail of the proposal based on the importance of the product. For example, the proposal unit makes a detailed proposal based on the importance of the product. The proposal unit can also make a concise proposal for a product with a low importance. For example, the proposal unit makes a concise proposal based on the importance of the product. This makes it possible to provide a detailed proposal according to the importance of the product.

[0100] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the product category. For example, in the case of kitchen appliances, the suggestion unit makes suggestions according to the usage scenario. For example, the suggestion unit receives the usage scenario of the kitchen appliance as input and makes suggestions according to the scenario. In addition, the suggestion unit can also make visually appealing suggestions in the case of entertainment appliances. For example, the suggestion unit receives the usage scenario of the entertainment appliance as input and makes visually appealing suggestions. In addition, the suggestion unit can also make suggestions regarding health benefits in the case of health appliances. For example, the suggestion unit receives the usage scenario of the health appliance as input and makes suggestions regarding health benefits. This makes it possible to provide optimal suggestions according to the product category.

[0101] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, suggests related products based on product information purchased by the user in the past. For example, the suggestion unit stores the user's past purchase history in a database and suggests related products based on that history. The suggestion unit can also suggest optimal products based on the user's past suggestion results. For example, the suggestion unit analyzes the user's past suggestion results and suggests optimal products. The suggestion unit can also analyze the user's past suggestion results and improve the accuracy of the suggestion. For example, the suggestion unit improves the accuracy of the suggestion based on the user's past suggestion results. This makes it possible to provide optimal suggestions based on the user's past suggestion results.

[0102] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Furthermore, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. For example, the suggestion unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, the suggestion unit can analyze the user's voice data in real time to estimate the user's emotions. This allows the suggestion unit to provide the optimal length of suggestions according to the user's emotions.

[0103] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time of submission of the product. The suggestion unit, for example, prioritizes suggesting new products. For example, the suggestion unit stores the time of submission of the product in a database and prioritizes suggesting new products. The suggestion unit can also prioritize suggesting seasonal products. For example, the suggestion unit prioritizes suggesting seasonal products based on the time of submission of the product. The suggestion unit can also prioritize suggesting products in which the user has shown interest in the past. For example, the suggestion unit prioritizes suggesting products in which the user has shown interest based on the user's past search history. This makes it possible to provide optimal suggestions based on the time of submission of the product.

[0104] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the products. The suggestion unit, for example, prioritizes suggesting highly relevant products. For example, the suggestion unit stores the relevance of products in a database and prioritizes displaying highly relevant products. The suggestion unit can also suggest highly relevant products based on the user's interests. For example, the suggestion unit prioritizes displaying information about products that the user has shown interest in based on the user's past search history. The suggestion unit can also adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit displays product information in an optimal order based on the relevance of the products. This makes it possible to provide suggestions in an optimal order based on the relevance of the products.

[0105] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes the suggestion using technical terms. For example, the suggestion unit evaluates the user's level of expertise based on survey results or past purchase history and makes the suggestion using technical terms. In addition, if the user is a beginner, the suggestion unit can make the suggestion in simple language. For example, the suggestion unit makes the suggestion in simple language based on the user's level of expertise. In addition, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, the suggestion unit selects optimal technical terms based on the user's level of expertise. This makes it possible to provide optimal suggestions according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, provision unit, comparison unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and accepts voice input and text input. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires and provides product information in real time. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares multiple products. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and suggests optimal products based on the user's needs. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, provision unit, comparison unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and accepts voice input. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires and provides product information in real time. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares multiple products. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 and suggests optimal products based on the user's needs. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, provision unit, comparison unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and accepts voice input. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires and provides product information in real time. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares multiple products. The suggestion unit is realized, for example, by the control unit 46A of the headset type terminal 314 and suggests optimal products based on the user's needs. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, provision unit, comparison unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and accepts voice input. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires and provides product information in real time. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares multiple products. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and suggests optimal products based on the user's needs.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can temporarily refrain from making suggestions and wait until the user is relaxed. Also, if the user is excited, the suggestion unit can make suggestions immediately to keep the user interested. Furthermore, if the user is tired, the suggestion unit can make simple and to-the-point suggestions to reduce the user's burden. This allows suggestions to be made at the optimal timing according to the user's emotions.

[0108] The providing unit can provide trend information on related products based on the user's past purchase history. For example, the providing unit can suggest upgraded versions of home appliances that the user previously purchased or products with new functions. The providing unit can also suggest related accessories or complementary products based on the user's purchase history. Furthermore, the providing unit can suggest related products purchased by other users based on the user's purchase history. This makes it possible to provide more relevant product information based on the user's past purchase history.

[0109] The comparison unit can estimate the user's emotions and adjust the display format of the comparison results based on the estimated user's emotions. For example, if the user is relaxed, the comparison results can be displayed using detailed graphs and charts. If the user is in a hurry, the comparison results can be displayed in a concise list format. Furthermore, if the user is excited, the comparison results can be displayed with visually appealing animations. This makes it possible to provide the comparison results in an optimal display format according to the user's emotions.

[0110] The suggestion unit can suggest products based on the user's lifestyle. For example, if the user likes outdoor activities, the suggestion unit can suggest portable home appliances. If the user is health-conscious, the suggestion unit can also suggest home appliances with health management functions. Furthermore, if the user is interested in technology, the suggestion unit can also suggest the latest smart home appliances. This makes it possible to suggest optimal products that suit the user's lifestyle.

[0111] The providing unit can estimate the user's emotions and adjust the order in which product information is provided based on the estimated user's emotions. For example, if the user is relaxed, detailed information is provided first, allowing the user to read the information carefully. If the user is in a hurry, key information is provided first, allowing the user to quickly obtain the necessary information. Furthermore, if the user is excited, visually appealing information is provided first, keeping the user interested. This makes it possible to provide product information in the optimal order according to the user's emotions.

[0112] The comparison unit can compare products based on their eco-friendliness. For example, the comparison unit can evaluate the energy efficiency and environmental impact of products and display eco-friendly products preferentially. The comparison unit can also compare products based on their recyclability and use of sustainable materials. Furthermore, the comparison unit can take into account the environmental impact of the product's manufacturing process. This makes it possible to provide optimal comparison results to users who prioritize eco-friendliness.

[0113] The suggestion unit can estimate the user's emotions and customize the content of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit makes simple and easy-to-understand suggestions to reduce the user's burden. If the user is relaxed, the suggestion unit makes suggestions with detailed information to allow the user to carefully consider the suggestions. If the user is excited, the suggestion unit makes visually appealing suggestions to keep the user interested. This makes it possible to provide optimal suggestion content according to the user's emotions.

[0114] The providing unit can provide region-specific promotional information based on the user's geographical location information. For example, if the user is in a specific region, the providing unit can provide information about sales and campaigns being held in that region. The providing unit can also provide region-specific discount coupons based on the user's geographical location information. Furthermore, the providing unit can also provide information about inventory status at nearby stores based on the user's geographical location information. This makes it possible to provide more relevant promotional information based on the user's geographical location information.

[0115] The comparison unit can estimate the user's emotions and adjust the weighting of the comparison criteria based on the estimated user's emotions. For example, if the user is relaxed, the comparison unit can emphasize detailed comparison criteria, allowing the user to carefully compare. If the user is in a hurry, the comparison unit can emphasize important comparison criteria, allowing the user to quickly obtain comparison results. Furthermore, if the user is excited, the comparison unit can emphasize visually attractive comparison criteria, thereby maintaining the user's interest. This makes it possible to provide optimal comparison criteria according to the user's emotions.

[0116] The suggestion unit can provide maintenance information for related products based on the user's purchasing history. For example, it can suggest maintenance schedules and precautions for home appliances purchased by the user. The suggestion unit can also provide information on related consumables and replacement parts based on the user's purchasing history. Furthermore, the suggestion unit can also provide information on product upgrades and the addition of new functions based on the user's purchasing history. This makes it possible to provide more relevant maintenance information based on the user's purchasing history.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives questions about home appliances from users. Questions from users include product features, prices, and usage methods. The reception unit can receive questions by voice input, text input, image input, and other methods. Step 2: The provider provides product information based on the question received by the reception unit. Product information includes specifications, prices, reviews, etc. The provider obtains information in real time using a product database and provides the user with the latest price information and stock status. Step 3: The comparison unit compares multiple products based on the product information provided by the provision unit. Comparison criteria include price, performance, user reviews, etc. The comparison unit can compare products based on multiple criteria, such as lowest price, highest performance, or highest user review rating. Step 4: The suggestion unit listens to the user's needs based on the product information compared by the comparison unit and suggests the most suitable product. The user's needs include the purpose of use, budget, preferences, etc. The suggestion unit listens to the user's budget, room size, purpose of use, etc. and suggests the most suitable product.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[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 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.

[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 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.

[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 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions about home appliances from users; a providing unit that provides product information based on the question received by the receiving unit; a comparison unit that compares a plurality of products based on the product information provided by the provision unit; a suggestion unit that asks the user about their needs based on the product information compared by the comparison unit and suggests products based on the user's needs. A system characterized by:

2. The proposal unit We ask users about their budget, room size, and purpose of use, and then suggest products based on their needs.

2. The system of claim 1.

3. The providing unit Use product databases to obtain information in real time 2. The system of claim 1.

4. The comparison unit Compare multiple products 2. The system of claim 1.

5. The proposal unit Providing product information and room layouts in store 2. The system of claim 1.

6. The proposal unit Support multiple product purchases 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the way questions are accepted based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past question history and select the optimal method for accepting questions 2. The system of claim 1.

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