System

Abstract

An object of a system according to an embodiment is to select a product optimal for a user's needs and to smoothly perform a process up to purchase.SOLUTION: A system according to an embodiment includes an interaction unit, an information collection unit, an analysis unit, a recommendation unit, and a movement path setting unit. The dialoguer uses the generated AI. The information collection unit collects product information based on the user's needs collected by the interaction unit. The analysis unit summarizes and analyzes the product information collected by the information collection unit. The recommendation unit recommends a product most suitable for the user's needs based on the product information analyzed by the analysis unit. The traffic line setting part sets a traffic line for purchasing the commodity recommended by the recommendation part in the company group.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently select products that best meet users' needs and to smoothly carry out the process up to purchase.

[0005] The system according to the embodiment aims to select the product that best suits the user's needs and to make the process leading up to purchase smooth. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an information collection unit, an analysis unit, a recommendation unit, and a flow line setting unit. The dialogue unit uses a generative AI. The information collection unit collects product information based on the user needs collected by the dialogue unit. The analysis unit summarizes and analyzes the product information collected by the information collection unit. The recommendation unit recommends products that best suit the user's needs based on the product information analyzed by the analysis unit. The flow line setting unit sets a flow line for purchasing products recommended by the recommendation unit within a corporate group. [Effects of the Invention]

[0007] The system according to the embodiment can select the product that best suits the user's needs and smoothly carry out the process up to purchase. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The assistant service according to an embodiment of the present invention is a system that uses generative AI to support users in their selection and purchase. This system explores user needs through chat-style dialogue, collects, summarizes, and analyzes product information, compares products, measures the degree of match with needs, and makes recommendations. It is also possible to set a path to purchase within a specific corporate group. This allows the assistant service to enable users to efficiently and easily select and purchase products.

[0029] The assistant service according to the embodiment includes a dialogue unit, an information collection unit, an analysis unit, a recommendation unit, and a flow line setting unit. The dialogue unit uses a generation AI to engage in a chat-style dialogue with a user to collect the user's needs. For example, the generation AI asks questions such as "What kind of product are you looking for?" or "What is your budget?" to collect the user's specific requests and requirements. The information collection unit collects product information based on the user's needs collected by the dialogue unit. For example, the generation AI acquires data from online shopping sites and review sites and summarizes and analyzes it. The analysis unit summarizes and analyzes the product information collected by the information collection unit. For example, the generation AI organizes product features and reviews based on the collected information and provides them to the user. The recommendation unit recommends products that best meet the user's needs based on the product information analyzed by the analysis unit. For example, the generation AI compares multiple products, analyzes their features and reviews, and suggests products that best meet the user's needs. The flow line setting unit sets a flow line for purchasing the product recommended by the recommendation unit within the corporate group. For example, the generation AI provides a link to an online shopping site so that the user can purchase the product selected by the user. This allows the assistant service according to the embodiment to efficiently and easily select and purchase a product.

[0030] The dialogue unit generates individually customized questions based on the user's past purchase history and search history, allowing it to elicit more specific needs. For example, the dialogue unit analyzes the user's past purchase history, and the generation AI generates related questions based on that history. For example, it asks questions suggesting new products related to products previously purchased. The dialogue unit also generates questions that will interest the user based on their search history. For example, it asks questions related to products recently searched for to elicit the user's specific needs. The dialogue unit also combines the purchase history and search history, allowing the generation AI to understand the user's preferences and generate customized questions. For example, it asks questions about products in the same category as products previously purchased. This allows it to elicit more specific needs based on the user's past history.

[0031] The dialogue unit understands the context of the text entered by the user and automatically generates related follow-up questions to further explore their needs. For example, the dialogue unit analyzes the context of the text entered by the user, and the generation AI generates related follow-up questions based on that context. For example, in response to the input "I'm looking for a new smartphone," the dialogue unit asks, "Which features are important?" The dialogue unit also understands the context of the text and automatically generates questions that allow the generation AI to further explore the user's needs. For example, it asks questions such as, "What is your budget?" or "Which brand do you like?" The dialogue unit also analyzes the text entered by the user, and the generation AI generates questions that elicit specific needs based on that context. For example, it asks, "What will you use it for?" This allows the needs to be further explored based on the user's input text.

[0032] The dialogue unit can incorporate a voice assistant function and collect needs through voice dialogue. For example, the dialogue unit can incorporate a voice assistant function and collect needs by having the user answer questions by voice. For example, the dialogue unit can ask, "What kind of product are you looking for?" and the user can respond by voice. The dialogue unit can also use a voice assistant to analyze the user's voice input, and the generation AI can understand the needs based on that voice. For example, the dialogue unit can collect the user's specific requests through voice dialogue. The dialogue unit can also utilize a voice assistant function and have the generation AI dig deeper into the needs by having the user interact by voice. For example, the dialogue unit can ask, "What is your budget?" and the user can respond by voice. In this way, needs can be collected through voice dialogue.

[0033] The dialogue unit analyzes images and videos uploaded by the user during the dialogue, and is able to understand needs from visual information as well. For example, the dialogue unit analyzes images uploaded by the user during the dialogue, and the generation AI understands needs from those images. For example, the user is asked to upload a photo of a product, and questions related to that product are asked. The dialogue unit also analyzes videos, and the generation AI understands the user's needs from those videos. For example, the user is asked to upload a video showing how to use a product, and questions related to that usage are asked. The dialogue unit also analyzes images and videos, and the generation AI understands the user's needs from visual information. For example, needs related to product design and color are extracted from images and videos. This makes it possible to understand needs from visual information as well.

[0034] The information collection unit can cross-reference data from different sources and extract reliable information. For example, the generation AI collects product information from multiple sources, such as online shopping sites and review sites, and cross-references it. For example, it compares different reviews of the same product to extract reliable information. The information collection unit also analyzes data collected from different sources, and the generation AI extracts reliable information. For example, it compares information on official websites with user reviews to provide accurate product information. The information collection unit also extracts reliable product information by having the generation AI collect data from multiple sources and cross-references it. For example, it collects and compares product specifications and price information from multiple sites. This allows the generation AI to cross-reference data from different sources and extract reliable information.

[0035] The information collection unit can be customized to prioritize providing information specialized to the user's needs. For example, the generation AI in the information collection unit analyzes the user's needs and prioritizes summarizing product information specialized to those needs. For example, price information is provided to emphasized users who prioritize price. The information collection unit also customizes product information according to the user's requests and provides information specialized to those needs. For example, information about product functions is summarized in detail for users who prioritize functionality. The information collection unit also grasps the user's needs and summarizes product information specialized to those needs so that it is prioritized. For example, information about product design is emphasized for users who prioritize design. This makes it possible to prioritize providing information specialized to the user's needs.

[0036] The information collection unit collects data, including unofficial sources such as social media and blogs, and can analyze it from a wide range of perspectives. For example, the information collection unit uses a generation AI to collect product information from unofficial sources such as social media and blogs and analyze it from a wide range of perspectives. For example, it analyzes posts on Twitter and Instagram to collect product opinions. The information collection unit also analyzes data collected from unofficial sources, and the generation AI summarizes the product information from a wide range of perspectives. For example, it analyzes blog articles and forum posts to reflect user opinions. The information collection unit also uses a generation AI to collect data from unofficial sources such as social media and blogs and analyze the product information from a wide range of perspectives. For example, it analyzes user word-of-mouth and reviews to make a comprehensive judgment on product evaluations. This allows data, including unofficial sources, to be collected and analyzed from a wide range of perspectives.

[0037] When summarizing product information, the information collection unit can use visual elements (e.g., infographics and charts) to make the information visually easier to understand. For example, when the generation AI summarizes product information, the information collection unit provides information visually using infographics. For example, product features and ratings are shown using graphs and icons. Furthermore, when summarizing product information, the information collection unit allows the generation AI to use charts to make the information visually easier to understand. For example, price fluctuations and rating distributions are displayed in charts. Furthermore, the information collection unit allows the generation AI to visually summarize product information using visual elements. For example, product specifications and reviews are shown using infographics. In this way, the product information can be made visually easier to understand using visual elements.

[0038] The recommendation unit can compare detailed product specifications and functions and select the product that best meets the user's needs. For example, the generation AI in the recommendation unit compares detailed product specifications and selects the product that best meets the user's needs. For example, it comprehensively evaluates factors such as performance, price, and design. The recommendation unit also compares product functions in detail and the generation AI selects the product that best meets the user's needs. For example, for users for whom specific functions are important, it will suggest products with excellent functions. The recommendation unit also compares product specifications and functions comprehensively and selects the product that best meets the user's needs. For example, it evaluates factors that the user values, such as battery life and camera performance. This allows the generation AI to compare detailed product specifications and functions and select the product that best meets the user's needs.

[0039] The recommendation unit can dynamically adjust the degree of product match based on the user's past feedback. For example, the recommendation unit analyzes the user's past feedback, and the generation AI dynamically adjusts the degree of product match. For example, it prioritizes suggesting products in the same category as products that have received high ratings in the past. The recommendation unit also dynamically adjusts the degree of product match based on feedback data. For example, it excludes products that have the same characteristics as products that the user has been dissatisfied with in the past. The recommendation unit also utilizes the user's past feedback, and the generation AI dynamically adjusts the degree of product match. For example, it suggests products that suit the user's preferences based on past purchase history and ratings. This makes it possible to dynamically adjust the degree of product match based on the user's past feedback.

[0040] When comparing products, the recommendation unit can consider not only price but also factors such as ecological footprint and social impact. For example, when comparing products, the recommendation unit considers not only price but also ecological footprint. For example, it may prioritize products made with environmentally friendly materials. The recommendation unit also considers social impact when comparing products, and the generation AI compares products. For example, it may suggest fair trade certified products or products from companies that engage in social contribution activities. The recommendation unit also considers factors other than price when comparing products, and the generation AI compares products. For example, it evaluates environmental factors such as energy efficiency and recyclability. This makes it possible to compare products taking into account not only price but also factors such as ecological footprint and social impact.

[0041] The recommendation unit can introduce AR (augmented reality) technology so that a user can try out products selected by the user in a virtual environment. For example, the recommendation unit uses AR technology to build a system that allows a user to try out products selected by the user in a virtual environment. For example, the user can try out furniture in a room in their home. In addition, the recommendation unit uses AR technology via a generation AI so that products can be tried out in a virtual environment. For example, the user can try out clothes. In addition, the recommendation unit utilizes AR technology to develop a system that allows a user to try out products selected by the user in a virtual environment. For example, the user can virtually experience the interior of a car. This makes it possible for a user to try out products selected by the user in a virtual environment.

[0042] The flow line setting unit can guide the user through each step of the purchasing procedure, supporting them so that they can complete the purchase without hesitation. For example, the flow line setting unit has the generation AI guide them through each step of the purchasing procedure, supporting them so that they can complete the purchase without hesitation. For example, it guides them through the steps to add products to a cart. The flow line setting unit also has the generation AI support the user during the purchasing procedure, explaining each step in an easy-to-understand manner. For example, it guides them through selecting a payment method and entering a delivery address. The flow line setting unit also has the generation AI guide them through each step of the purchasing procedure in detail, supporting them so that they can complete the purchase smoothly. For example, it guides them through how to apply a coupon code. In this way, it is possible to guide the user through each step of the purchasing procedure, supporting them so that they can complete the purchase without hesitation.

[0043] The flow line setting unit can refer to the user's past purchase history during the purchase process and suggest related products and accessories. In the flow line setting unit, for example, during the purchase process, the generation AI refers to the user's past purchase history and suggests related products and accessories. For example, it suggests a case that matches a smartphone previously purchased. In addition, the flow line setting unit can have the generation AI suggest related products during the purchase process based on the past purchase history. For example, it suggests a lens that matches a camera previously purchased. In addition, the flow line setting unit can have the generation AI analyze the user's purchase history and suggest related products and accessories during the purchase process. For example, it suggests a bag that matches a laptop previously purchased. In this way, the generation AI can refer to the user's past purchase history during the purchase process and suggest related products and accessories.

[0044] The flow line setting unit can suggest gift wrapping and message card options according to the user's preferences during the purchase process. For example, the flow line setting unit allows the generation AI to suggest gift wrapping options according to the user's preferences during the purchase process. For example, it allows the user to select wrapping with a specific design or color. The flow line setting unit also allows the generation AI to suggest message card options based on the user's preferences. For example, it allows the user to select a specific message or design. The flow line setting unit also allows the generation AI to suggest gift wrapping and message card options according to the user's preferences during the purchase process. For example, it suggests wrapping and messages that suit special events. This makes it possible to suggest gift wrapping and message card options according to the user's preferences during the purchase process.

[0045] The flow line setting unit can provide the user with product usage and maintenance information as a follow-up after purchase. For example, after purchase, the generation AI provides the user with product usage information. For example, a guide is provided that explains in detail how to use a home appliance. The flow line setting unit also provides the user with maintenance information as a follow-up after purchase. For example, guidance is provided on how to perform regular inspections and oil changes for automobiles. The flow line setting unit also provides the user with product usage and maintenance information after purchase. For example, explanations are given on how to assemble and care for furniture. This allows the generation AI to provide the user with product usage and maintenance information as a follow-up after purchase.

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

[0047] The dialogue unit understands the context of the text entered by the user and automatically generates related follow-up questions to further explore their needs. For example, the dialogue unit analyzes the context of the text entered by the user, and the generation AI generates related follow-up questions based on that context. For example, in response to the input "I'm looking for a new smartphone," the dialogue unit asks, "Which features are important?" The dialogue unit also understands the context of the text and automatically generates questions that allow the generation AI to further explore the user's needs. For example, it asks questions such as, "What is your budget?" or "Which brand do you like?" The dialogue unit also analyzes the text entered by the user, and the generation AI generates questions that elicit specific needs based on that context. For example, it asks, "What will you use it for?" This allows the needs to be further explored based on the user's input text.

[0048] The dialogue unit can incorporate a voice assistant function and collect needs through voice dialogue. For example, the dialogue unit can incorporate a voice assistant function and collect needs by having the user answer questions by voice. For example, the dialogue unit can ask, "What kind of product are you looking for?" and the user can respond by voice. The dialogue unit can also use a voice assistant to analyze the user's voice input, and the generation AI can understand the needs based on that voice. For example, the dialogue unit can collect the user's specific requests through voice dialogue. The dialogue unit can also utilize a voice assistant function and have the generation AI dig deeper into the needs by having the user interact by voice. For example, the dialogue unit can ask, "What is your budget?" and the user can respond by voice. In this way, needs can be collected through voice dialogue.

[0049] The dialogue unit analyzes images and videos uploaded by the user during the dialogue, and is able to understand needs from visual information as well. For example, the dialogue unit analyzes images uploaded by the user during the dialogue, and the generation AI understands needs from those images. For example, the user is asked to upload a photo of a product, and questions related to that product are asked. The dialogue unit also analyzes videos, and the generation AI understands the user's needs from those videos. For example, the user is asked to upload a video showing how to use a product, and questions related to that usage are asked. The dialogue unit also analyzes images and videos, and the generation AI understands the user's needs from visual information. For example, needs related to product design and color are extracted from images and videos. This makes it possible to understand needs from visual information as well.

[0050] The information collection unit can cross-reference data from different sources and extract reliable information. For example, the generation AI collects product information from multiple sources, such as online shopping sites and review sites, and cross-references it. For example, it compares different reviews of the same product to extract reliable information. The information collection unit also analyzes data collected from different sources, and the generation AI extracts reliable information. For example, it compares information on official websites with user reviews to provide accurate product information. The information collection unit also extracts reliable product information by having the generation AI collect data from multiple sources and cross-references it. For example, it collects and compares product specifications and price information from multiple sites. This allows the generation AI to cross-reference data from different sources and extract reliable information.

[0051] The information collection unit can be customized to prioritize providing information specialized to the user's needs. For example, the generation AI in the information collection unit analyzes the user's needs and prioritizes summarizing product information specialized to those needs. For example, price information is provided to emphasized users who prioritize price. The information collection unit also customizes product information according to the user's requests and provides information specialized to those needs. For example, information about product functions is summarized in detail for users who prioritize functionality. The information collection unit also grasps the user's needs and summarizes product information specialized to those needs so that it is prioritized. For example, information about product design is emphasized for users who prioritize design. This makes it possible to prioritize providing information specialized to the user's needs.

[0052] The information collection unit collects data, including unofficial sources such as social media and blogs, and can analyze it from a wide range of perspectives. For example, the information collection unit uses a generation AI to collect product information from unofficial sources such as social media and blogs and analyze it from a wide range of perspectives. For example, it analyzes posts on Twitter and Instagram to collect product opinions. The information collection unit also analyzes data collected from unofficial sources, and the generation AI summarizes the product information from a wide range of perspectives. For example, it analyzes blog articles and forum posts to reflect user opinions. The information collection unit also uses a generation AI to collect data from unofficial sources such as social media and blogs and analyze the product information from a wide range of perspectives. For example, it analyzes user word-of-mouth and reviews to make a comprehensive judgment on product evaluations. This allows data, including unofficial sources, to be collected and analyzed from a wide range of perspectives.

[0053] When summarizing product information, the information collection unit can use visual elements (e.g., infographics and charts) to make the information visually easier to understand. For example, when the generation AI summarizes product information, the information collection unit provides information visually using infographics. For example, product features and ratings are shown using graphs and icons. Furthermore, when summarizing product information, the information collection unit allows the generation AI to use charts to make the information visually easier to understand. For example, price fluctuations and rating distributions are displayed in charts. Furthermore, the information collection unit allows the generation AI to visually summarize product information using visual elements. For example, product specifications and reviews are shown using infographics. In this way, the product information can be made visually easier to understand using visual elements.

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

[0055] Step 1: The dialogue unit uses the generation AI to engage in a chat-style dialogue with the user and gather information about the user's needs. For example, the generation AI may ask questions such as "What kind of product are you looking for?" or "What is your budget?" to gather the user's specific requests and requirements. Step 2: The information gathering unit collects product information based on the user needs collected by the dialogue unit. For example, the generation AI obtains data from online shopping sites and review sites, and summarizes and analyzes it. Step 3: The analysis unit summarizes and analyzes the product information collected by the information collection unit. For example, the generation AI organizes product features and evaluations based on the collected information and provides them to the user. Step 4: The recommendation unit recommends the product that best suits the user's needs based on the product information analyzed by the analysis unit. For example, the generation AI compares multiple products, analyzes their features and ratings, and suggests the product that best meets the user's needs. Step 5: The flow setting unit sets the flow for purchasing the products recommended by the recommendation unit within the corporate group. For example, the generation AI provides a link to an online shopping site so that the user can purchase the product they selected directly.

[0056] (Example 2) The assistant service according to an embodiment of the present invention is a system that uses generative AI to support users in their selection and purchase. This system explores user needs through chat-style dialogue, collects, summarizes, and analyzes product information, compares products, measures the degree of match with needs, and makes recommendations. It is also possible to set a path to purchase within a specific corporate group. This allows the assistant service to enable users to efficiently and easily select and purchase products.

[0057] The assistant service according to the embodiment includes a dialogue unit, an information collection unit, an analysis unit, a recommendation unit, and a flow line setting unit. The dialogue unit uses a generation AI to engage in a chat-style dialogue with a user to collect the user's needs. For example, the generation AI asks questions such as "What kind of product are you looking for?" or "What is your budget?" to collect the user's specific requests and requirements. The information collection unit collects product information based on the user's needs collected by the dialogue unit. For example, the generation AI acquires data from online shopping sites and review sites and summarizes and analyzes it. The analysis unit summarizes and analyzes the product information collected by the information collection unit. For example, the generation AI organizes product features and reviews based on the collected information and provides them to the user. The recommendation unit recommends products that best meet the user's needs based on the product information analyzed by the analysis unit. For example, the generation AI compares multiple products, analyzes their features and reviews, and suggests products that best meet the user's needs. The flow line setting unit sets a flow line for purchasing the product recommended by the recommendation unit within the corporate group. For example, the generation AI provides a link to an online shopping site so that the user can purchase the product selected by the user. This allows the assistant service according to the embodiment to efficiently and easily select and purchase a product.

[0058] The dialogue unit generates individually customized questions based on the user's past purchase history and search history, allowing it to elicit more specific needs. For example, the dialogue unit analyzes the user's past purchase history, and the generation AI generates related questions based on that history. For example, it asks questions suggesting new products related to products previously purchased. The dialogue unit also generates questions that will interest the user based on their search history. For example, it asks questions related to products recently searched for to elicit the user's specific needs. The dialogue unit also combines the purchase history and search history, allowing the generation AI to understand the user's preferences and generate customized questions. For example, it asks questions about products in the same category as products previously purchased. This allows it to elicit more specific needs based on the user's past history.

[0059] The dialogue unit analyzes the user's facial expressions and tone of voice during dialogue, and uses an emotion estimation function to grasp the user's emotional state and ask appropriate questions. For example, the dialogue unit analyzes the user's facial expressions using a camera, and the generation AI estimates the user's emotions based on the facial expressions. For example, if the user is smiling, positive questions are asked, and if the user has a serious expression, detailed questions are asked. The dialogue unit also analyzes the tone of voice, and the generation AI grasps the user's emotional state. For example, if the user's voice is bright, light questions are asked, and if the user's voice is deep, more probing questions are asked. The dialogue unit also combines facial expressions and tone of voice, and the generation AI grasps the user's emotional state comprehensively and generates appropriate questions. For example, if the user's face is smiling and their voice is bright, an interesting question is asked. This allows the generation AI to ask appropriate questions according to the user's emotional state.

[0060] The dialogue unit understands the context of the text entered by the user and automatically generates related follow-up questions to further explore their needs. For example, the dialogue unit analyzes the context of the text entered by the user, and the generation AI generates related follow-up questions based on that context. For example, in response to the input "I'm looking for a new smartphone," the dialogue unit asks, "Which features are important?" The dialogue unit also understands the context of the text and automatically generates questions that allow the generation AI to further explore the user's needs. For example, it asks questions such as, "What is your budget?" or "Which brand do you like?" The dialogue unit also analyzes the text entered by the user, and the generation AI generates questions that elicit specific needs based on that context. For example, it asks, "What will you use it for?" This allows the needs to be further explored based on the user's input text.

[0061] The dialogue unit can incorporate a voice assistant function and collect needs through voice dialogue. For example, the dialogue unit can incorporate a voice assistant function and collect needs by having the user answer questions by voice. For example, the dialogue unit can ask, "What kind of product are you looking for?" and the user can respond by voice. The dialogue unit can also use a voice assistant to analyze the user's voice input, and the generation AI can understand the needs based on that voice. For example, the dialogue unit can collect the user's specific requests through voice dialogue. The dialogue unit can also utilize a voice assistant function and have the generation AI dig deeper into the needs by having the user interact by voice. For example, the dialogue unit can ask, "What is your budget?" and the user can respond by voice. In this way, needs can be collected through voice dialogue.

[0062] The dialogue unit analyzes images and videos uploaded by the user during the dialogue, and is able to understand needs from visual information as well. For example, the dialogue unit analyzes images uploaded by the user during the dialogue, and the generation AI understands needs from those images. For example, the user is asked to upload a photo of a product, and questions related to that product are asked. The dialogue unit also analyzes videos, and the generation AI understands the user's needs from those videos. For example, the user is asked to upload a video showing how to use a product, and questions related to that usage are asked. The dialogue unit also analyzes images and videos, and the generation AI understands the user's needs from visual information. For example, needs related to product design and color are extracted from images and videos. This makes it possible to understand needs from visual information as well.

[0063] The dialogue unit uses the emotion estimation function to select a dialogue style according to the user's emotions and can conduct a dialogue that elicits positive emotions. For example, the dialogue unit uses the emotion estimation function to grasp the user's emotional state and select a dialogue style according to that emotion. For example, when the user is relaxed, a casual dialogue is conducted. The dialogue unit also estimates the user's emotions and the generation AI conducts a dialogue that elicits positive emotions. For example, when the user is feeling stressed, the dialogue unit offers words of encouragement. The dialogue unit also utilizes the emotion estimation function to select a dialogue style according to the user's emotions and elicits positive emotions. For example, when the user is excited, words of empathy are spoken. In this way, a dialogue style according to the user's emotions can be selected and positive emotions can be elicited.

[0064] The information collection unit can cross-reference data from different sources and extract reliable information. For example, the generation AI collects product information from multiple sources, such as online shopping sites and review sites, and cross-references it. For example, it compares different reviews of the same product to extract reliable information. The information collection unit also analyzes data collected from different sources, and the generation AI extracts reliable information. For example, it compares information on official websites with user reviews to provide accurate product information. The information collection unit also extracts reliable product information by having the generation AI collect data from multiple sources and cross-references it. For example, it collects and compares product specifications and price information from multiple sites. This allows the generation AI to cross-reference data from different sources and extract reliable information.

[0065] The information collection unit performs sentiment analysis on the collected reviews and can separate and summarize positive and negative reviews. For example, the information collection unit performs sentiment analysis on the reviews collected by the generation AI and classifies them into positive and negative reviews. For example, positive reviews include keywords such as "satisfied" and "recommended." The information collection unit also uses sentiment analysis to have the generation AI evaluate the emotions of the reviews and separate and summarize positive and negative reviews. For example, positive reviews are summarized with reasons for high ratings. The information collection unit also performs sentiment analysis on the reviews collected by the generation AI and separate and summarize positive and negative reviews. For example, negative reviews are summarized with areas for improvement and complaints. This makes it possible to perform sentiment analysis on reviews and separate and summarize positive and negative reviews.

[0066] The information collection unit can be customized to prioritize providing information specialized to the user's needs. For example, the generation AI in the information collection unit analyzes the user's needs and prioritizes summarizing product information specialized to those needs. For example, price information is provided to emphasized users who prioritize price. The information collection unit also customizes product information according to the user's requests and provides information specialized to those needs. For example, information about product functions is summarized in detail for users who prioritize functionality. The information collection unit also grasps the user's needs and summarizes product information specialized to those needs so that it is prioritized. For example, information about product design is emphasized for users who prioritize design. This makes it possible to prioritize providing information specialized to the user's needs.

[0067] The information collection unit collects data, including unofficial sources such as social media and blogs, and can analyze it from a wide range of perspectives. For example, the information collection unit uses a generation AI to collect product information from unofficial sources such as social media and blogs and analyze it from a wide range of perspectives. For example, it analyzes posts on Twitter and Instagram to collect product opinions. The information collection unit also analyzes data collected from unofficial sources, and the generation AI summarizes the product information from a wide range of perspectives. For example, it analyzes blog articles and forum posts to reflect user opinions. The information collection unit also uses a generation AI to collect data from unofficial sources such as social media and blogs and analyze the product information from a wide range of perspectives. For example, it analyzes user word-of-mouth and reviews to make a comprehensive judgment on product evaluations. This allows data, including unofficial sources, to be collected and analyzed from a wide range of perspectives.

[0068] When summarizing product information, the information collection unit can use visual elements (e.g., infographics and charts) to make the information visually easier to understand. For example, when the generation AI summarizes product information, the information collection unit provides information visually using infographics. For example, product features and ratings are shown using graphs and icons. Furthermore, when summarizing product information, the information collection unit allows the generation AI to use charts to make the information visually easier to understand. For example, price fluctuations and rating distributions are displayed in charts. Furthermore, the information collection unit allows the generation AI to visually summarize product information using visual elements. For example, product specifications and reviews are shown using infographics. In this way, the product information can be made visually easier to understand using visual elements.

[0069] The information collection unit uses the emotion estimation function to analyze the emotional trends of reviews and provide information that is likely to resonate with users emotionally. For example, the information collection unit uses the emotion estimation function to have the generation AI analyze the emotional trends of reviews and provide information that is likely to resonate with users emotionally. For example, reviews with a high level of positive emotion are preferentially displayed. The information collection unit also analyzes the emotional trends of reviews and has the generation AI provide information that is likely to resonate with users emotionally. For example, reviews with a high emotional score are summarized and provided. The information collection unit also utilizes the emotion estimation function to have the generation AI analyze the emotional trends of reviews and provide information that is likely to resonate with users emotionally. For example, reviews of products with a high level of positive emotion are highlighted. This makes it possible to analyze the emotional trends of reviews and provide information that is likely to resonate with users emotionally.

[0070] The recommendation unit can compare detailed product specifications and functions and select the product that best meets the user's needs. For example, the generation AI in the recommendation unit compares detailed product specifications and selects the product that best meets the user's needs. For example, it comprehensively evaluates factors such as performance, price, and design. The recommendation unit also compares product functions in detail and the generation AI selects the product that best meets the user's needs. For example, for users for whom specific functions are important, it will suggest products with excellent functions. The recommendation unit also compares product specifications and functions comprehensively and selects the product that best meets the user's needs. For example, it evaluates factors that the user values, such as battery life and camera performance. This allows the generation AI to compare detailed product specifications and functions and select the product that best meets the user's needs.

[0071] The recommendation unit can dynamically adjust the degree of product match based on the user's past feedback. For example, the recommendation unit analyzes the user's past feedback, and the generation AI dynamically adjusts the degree of product match. For example, it prioritizes suggesting products in the same category as products that have received high ratings in the past. The recommendation unit also dynamically adjusts the degree of product match based on feedback data. For example, it excludes products that have the same characteristics as products that the user has been dissatisfied with in the past. The recommendation unit also utilizes the user's past feedback, and the generation AI dynamically adjusts the degree of product match. For example, it suggests products that suit the user's preferences based on past purchase history and ratings. This makes it possible to dynamically adjust the degree of product match based on the user's past feedback.

[0072] The recommendation unit can use the emotion estimation function to prioritize recommend products that evoke the most positive emotions in the user. For example, the recommendation unit uses the emotion estimation function to allow the generation AI to grasp the user's emotional state and prioritize recommend products that evoke the most positive emotions. For example, the recommendation unit suggests products with the same characteristics as products that have received high ratings in the past. The recommendation unit also analyzes the user's emotional response and allows the generation AI to recommend products that elicit the most positive emotions. For example, suggestions are made based on reviews of products with high emotion scores. The recommendation unit also utilizes the emotion estimation function to allow the generation AI to recommend optimal products based on the user's emotions. For example, the generation AI analyzes the characteristics of products that evoke strong positive emotions and suggests similar products. This allows the generation AI to prioritize recommending products that evoke the most positive emotions in the user.

[0073] When comparing products, the recommendation unit can consider not only price but also factors such as ecological footprint and social impact. For example, when comparing products, the recommendation unit considers not only price but also ecological footprint. For example, it may prioritize products made with environmentally friendly materials. The recommendation unit also considers social impact when comparing products, and the generation AI compares products. For example, it may suggest fair trade certified products or products from companies that engage in social contribution activities. The recommendation unit also considers factors other than price when comparing products, and the generation AI compares products. For example, it evaluates environmental factors such as energy efficiency and recyclability. This makes it possible to compare products taking into account not only price but also factors such as ecological footprint and social impact.

[0074] The recommendation unit can introduce AR (augmented reality) technology so that a user can try out products selected by the user in a virtual environment. For example, the recommendation unit uses AR technology to build a system that allows a user to try out products selected by the user in a virtual environment. For example, the user can try out furniture in a room in their home. In addition, the recommendation unit uses AR technology via a generation AI so that products can be tried out in a virtual environment. For example, the user can try out clothes. In addition, the recommendation unit utilizes AR technology to develop a system that allows a user to try out products selected by the user in a virtual environment. For example, the user can virtually experience the interior of a car. This makes it possible for a user to try out products selected by the user in a virtual environment.

[0075] The recommendation unit uses the emotion estimation function to identify product categories in which the user is most interested and can make comparisons within that category. For example, the recommendation unit uses the emotion estimation function to identify product categories in which the generation AI will be interested in the user. For example, it analyzes categories of interest based on past emotional responses. The recommendation unit also analyzes the user's emotional responses and identifies product categories in which the generation AI will be most interested. For example, it prioritizes suggesting products in categories with high emotional scores. The recommendation unit also utilizes the emotion estimation function to identify product categories in which the generation AI will be interested in the user and make comparisons within that category. For example, it makes a detailed comparison of products in categories of interest. This allows the generation AI to identify product categories in which the user is most interested and make comparisons within that category.

[0076] The flow line setting unit can guide the user through each step of the purchasing procedure, supporting them so that they can complete the purchase without hesitation. For example, the flow line setting unit has the generation AI guide them through each step of the purchasing procedure, supporting them so that they can complete the purchase without hesitation. For example, it guides them through the steps to add products to a cart. The flow line setting unit also has the generation AI support the user during the purchasing procedure, explaining each step in an easy-to-understand manner. For example, it guides them through selecting a payment method and entering a delivery address. The flow line setting unit also has the generation AI guide them through each step of the purchasing procedure in detail, supporting them so that they can complete the purchase smoothly. For example, it guides them through how to apply a coupon code. In this way, it is possible to guide the user through each step of the purchasing procedure, supporting them so that they can complete the purchase without hesitation.

[0077] The flow line setting unit can refer to the user's past purchase history during the purchase process and suggest related products and accessories. In the flow line setting unit, for example, during the purchase process, the generation AI refers to the user's past purchase history and suggests related products and accessories. For example, it suggests a case that matches a smartphone previously purchased. In addition, the flow line setting unit can have the generation AI suggest related products during the purchase process based on the past purchase history. For example, it suggests a lens that matches a camera previously purchased. In addition, the flow line setting unit can have the generation AI analyze the user's purchase history and suggest related products and accessories during the purchase process. For example, it suggests a bag that matches a laptop previously purchased. In this way, the generation AI can refer to the user's past purchase history during the purchase process and suggest related products and accessories.

[0078] The flow line setting unit can use the emotion estimation function to monitor the user's stress level during the purchase process and make suggestions to help them relax. The flow line setting unit, for example, uses the emotion estimation function to monitor the user's stress level during the purchase process and make suggestions to help them relax. For example, if the user is feeling stressed, it can provide guidance on a simple procedure. The flow line setting unit also uses the generation AI to monitor the user's stress level during the purchase process and make suggestions to help them relax. For example, it can suggest music to reduce stress. The flow line setting unit also utilizes the emotion estimation function to monitor the user's stress level during the purchase process and make suggestions to help them relax. For example, if the user is feeling stressed, it can provide a support chat. In this way, it is possible to monitor the user's stress level during the purchase process and make suggestions to help them relax.

[0079] The flow line setting unit can suggest gift wrapping and message card options according to the user's preferences during the purchase process. For example, the flow line setting unit allows the generation AI to suggest gift wrapping options according to the user's preferences during the purchase process. For example, it allows the user to select wrapping with a specific design or color. The flow line setting unit also allows the generation AI to suggest message card options based on the user's preferences. For example, it allows the user to select a specific message or design. The flow line setting unit also allows the generation AI to suggest gift wrapping and message card options according to the user's preferences during the purchase process. For example, it suggests wrapping and messages that suit special events. This makes it possible to suggest gift wrapping and message card options according to the user's preferences during the purchase process.

[0080] The flow line setting unit can provide the user with product usage and maintenance information as a follow-up after purchase. For example, after purchase, the generation AI provides the user with product usage information. For example, a guide is provided that explains in detail how to use a home appliance. The flow line setting unit also provides the user with maintenance information as a follow-up after purchase. For example, guidance is provided on how to perform regular inspections and oil changes for automobiles. The flow line setting unit also provides the user with product usage and maintenance information after purchase. For example, explanations are given on how to assemble and care for furniture. This allows the generation AI to provide the user with product usage and maintenance information as a follow-up after purchase.

[0081] The flow line setting unit uses the emotion estimation function to provide customer support that is appropriate for the user's emotions during the purchase process, thereby promoting a positive purchasing experience. The flow line setting unit, for example, uses the emotion estimation function to understand the user's emotions during the purchase process and provide customer support that is appropriate for those emotions. For example, if the user is feeling stressed, it provides prompt support. The flow line setting unit also monitors the user's emotions during the purchase process using a generation AI and provides support to promote a positive purchasing experience. For example, if the emotion score is low, it sends an encouraging message. The flow line setting unit also utilizes the emotion estimation function to provide customer support that is appropriate for the user's emotions during the purchase process. For example, it makes suggestions to elicit positive emotions. This makes it possible to provide customer support that is appropriate for the user's emotions during the purchase process, thereby promoting a positive purchasing experience.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The dialogue unit analyzes the user's facial expressions and tone of voice during dialogue, and uses an emotion estimation function to grasp the user's emotional state and ask appropriate questions. For example, the dialogue unit analyzes the user's facial expressions using a camera, and the generation AI estimates the user's emotions based on the facial expressions. For example, if the user is smiling, positive questions are asked, and if the user has a serious expression, detailed questions are asked. The dialogue unit also analyzes the tone of voice, and the generation AI grasps the user's emotional state. For example, if the user's voice is bright, light questions are asked, and if the user's voice is deep, more probing questions are asked. The dialogue unit also combines facial expressions and tone of voice, and the generation AI grasps the user's emotional state comprehensively and generates appropriate questions. For example, if the user's face is smiling and their voice is bright, an interesting question is asked. This allows the generation AI to ask appropriate questions according to the user's emotional state.

[0084] The dialogue unit understands the context of the text entered by the user and automatically generates related follow-up questions to further explore their needs. For example, the dialogue unit analyzes the context of the text entered by the user, and the generation AI generates related follow-up questions based on that context. For example, in response to the input "I'm looking for a new smartphone," the dialogue unit asks, "Which features are important?" The dialogue unit also understands the context of the text and automatically generates questions that allow the generation AI to further explore the user's needs. For example, it asks questions such as, "What is your budget?" or "Which brand do you like?" The dialogue unit also analyzes the text entered by the user, and the generation AI generates questions that elicit specific needs based on that context. For example, it asks, "What will you use it for?" This allows the needs to be further explored based on the user's input text.

[0085] The dialogue unit can incorporate a voice assistant function and collect needs through voice dialogue. For example, the dialogue unit can incorporate a voice assistant function and collect needs by having the user answer questions by voice. For example, the dialogue unit can ask, "What kind of product are you looking for?" and the user can respond by voice. The dialogue unit can also use a voice assistant to analyze the user's voice input, and the generation AI can understand the needs based on that voice. For example, the dialogue unit can collect the user's specific requests through voice dialogue. The dialogue unit can also utilize a voice assistant function and have the generation AI dig deeper into the needs by having the user interact by voice. For example, the dialogue unit can ask, "What is your budget?" and the user can respond by voice. In this way, needs can be collected through voice dialogue.

[0086] The dialogue unit analyzes images and videos uploaded by the user during the dialogue, and is able to understand needs from visual information as well. For example, the dialogue unit analyzes images uploaded by the user during the dialogue, and the generation AI understands needs from those images. For example, the user is asked to upload a photo of a product, and questions related to that product are asked. The dialogue unit also analyzes videos, and the generation AI understands the user's needs from those videos. For example, the user is asked to upload a video showing how to use a product, and questions related to that usage are asked. The dialogue unit also analyzes images and videos, and the generation AI understands the user's needs from visual information. For example, needs related to product design and color are extracted from images and videos. This makes it possible to understand needs from visual information as well.

[0087] The dialogue unit uses the emotion estimation function to select a dialogue style according to the user's emotions and can conduct a dialogue that elicits positive emotions. For example, the dialogue unit uses the emotion estimation function to grasp the user's emotional state and select a dialogue style according to that emotion. For example, when the user is relaxed, a casual dialogue is conducted. The dialogue unit also estimates the user's emotions and the generation AI conducts a dialogue that elicits positive emotions. For example, when the user is feeling stressed, the dialogue unit offers words of encouragement. The dialogue unit also utilizes the emotion estimation function to select a dialogue style according to the user's emotions and elicits positive emotions. For example, when the user is excited, words of empathy are spoken. In this way, a dialogue style according to the user's emotions can be selected and positive emotions can be elicited.

[0088] The information collection unit can cross-reference data from different sources and extract reliable information. For example, the generation AI collects product information from multiple sources, such as online shopping sites and review sites, and cross-references it. For example, it compares different reviews of the same product to extract reliable information. The information collection unit also analyzes data collected from different sources, and the generation AI extracts reliable information. For example, it compares information on official websites with user reviews to provide accurate product information. The information collection unit also extracts reliable product information by having the generation AI collect data from multiple sources and cross-references it. For example, it collects and compares product specifications and price information from multiple sites. This allows the generation AI to cross-reference data from different sources and extract reliable information.

[0089] The information collection unit performs sentiment analysis on the collected reviews and can separate and summarize positive and negative reviews. For example, the information collection unit performs sentiment analysis on the reviews collected by the generation AI and classifies them into positive and negative reviews. For example, positive reviews include keywords such as "satisfied" and "recommended." The information collection unit also uses sentiment analysis to have the generation AI evaluate the emotions of the reviews and separate and summarize positive and negative reviews. For example, positive reviews are summarized with reasons for high ratings. The information collection unit also performs sentiment analysis on the reviews collected by the generation AI and separate and summarize positive and negative reviews. For example, negative reviews are summarized with areas for improvement and complaints. This makes it possible to perform sentiment analysis on reviews and separate and summarize positive and negative reviews.

[0090] The information collection unit can be customized to prioritize providing information specialized to the user's needs. For example, the generation AI in the information collection unit analyzes the user's needs and prioritizes summarizing product information specialized to those needs. For example, price information is provided to emphasized users who prioritize price. The information collection unit also customizes product information according to the user's requests and provides information specialized to those needs. For example, information about product functions is summarized in detail for users who prioritize functionality. The information collection unit also grasps the user's needs and summarizes product information specialized to those needs so that it is prioritized. For example, information about product design is emphasized for users who prioritize design. This makes it possible to prioritize providing information specialized to the user's needs.

[0091] The information collection unit collects data, including unofficial sources such as social media and blogs, and can analyze it from a wide range of perspectives. For example, the information collection unit uses a generation AI to collect product information from unofficial sources such as social media and blogs and analyze it from a wide range of perspectives. For example, it analyzes posts on Twitter and Instagram to collect product opinions. The information collection unit also analyzes data collected from unofficial sources, and the generation AI summarizes the product information from a wide range of perspectives. For example, it analyzes blog articles and forum posts to reflect user opinions. The information collection unit also uses a generation AI to collect data from unofficial sources such as social media and blogs and analyze the product information from a wide range of perspectives. For example, it analyzes user word-of-mouth and reviews to make a comprehensive judgment on product evaluations. This allows data, including unofficial sources, to be collected and analyzed from a wide range of perspectives.

[0092] When summarizing product information, the information collection unit can use visual elements (e.g., infographics and charts) to make the information visually easier to understand. For example, when the generation AI summarizes product information, the information collection unit provides information visually using infographics. For example, product features and ratings are shown using graphs and icons. Furthermore, when summarizing product information, the information collection unit allows the generation AI to use charts to make the information visually easier to understand. For example, price fluctuations and rating distributions are displayed in charts. Furthermore, the information collection unit allows the generation AI to visually summarize product information using visual elements. For example, product specifications and reviews are shown using infographics. In this way, the product information can be made visually easier to understand using visual elements.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The dialogue unit uses the generation AI to engage in a chat-style dialogue with the user and gather information about the user's needs. For example, the generation AI may ask questions such as "What kind of product are you looking for?" or "What is your budget?" to gather the user's specific requests and requirements. Step 2: The information gathering unit collects product information based on the user needs collected by the dialogue unit. For example, the generation AI obtains data from online shopping sites and review sites, and summarizes and analyzes it. Step 3: The analysis unit summarizes and analyzes the product information collected by the information collection unit. For example, the generation AI organizes product features and evaluations based on the collected information and provides them to the user. Step 4: The recommendation unit recommends the product that best suits the user's needs based on the product information analyzed by the analysis unit. For example, the generation AI compares multiple products, analyzes their features and ratings, and suggests the product that best meets the user's needs. Step 5: The flow setting unit sets the flow for purchasing the products recommended by the recommendation unit within the corporate group. For example, the generation AI provides a link to an online shopping site so that the user can purchase the product they selected directly.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 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 dialogue section using generative AI, an information collection unit that collects product information based on the user needs collected by the dialogue unit; an analysis unit that summarizes and analyzes the product information collected by the information collection unit; a recommendation unit that recommends a product that best suits the needs of the user based on the product information analyzed by the analysis unit; a flow line setting unit that sets a flow line for purchasing the product recommended by the recommendation unit within the corporate group. A system characterized by:

2. The dialogue unit Generate personalized questions based on the user's past purchase and search history to elicit more specific needs 2. The system of claim 1.

3. The dialogue unit Analyzing the user's facial expressions and tone of voice during the conversation, understanding the user's emotional state, and asking appropriate questions 2. The system of claim 1.

4. The dialogue unit Understand the context of the text entered by the user and automatically generate relevant follow-up questions to further explore the need.

2. The system of claim 1.

5. The dialogue unit Introducing a voice assistant function to gather the above needs through voice dialogue 2. The system of claim 1.

6. The dialogue unit Analyze images and videos uploaded by the user during the conversation to understand the user's needs from visual information.

2. The system of claim 1.

7. The dialogue unit A dialogue style is selected according to the user's emotions, and a dialogue is conducted to elicit positive emotions.

2. The system of claim 1.

8. The information collecting unit Cross-reference data from different sources to extract reliable information 2. The system of claim 1.

Images