system

Abstract

The system according to the embodiment aims to provide a user with easy access to professional advice when choosing a drink at a restaurant. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a display unit. The reception unit receives a question from a user. The generation unit analyzes the question received by the reception unit and generates advice. The display unit displays the advice generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to easily receive professional advice when choosing a drink at a restaurant.

[0005] The system according to the embodiment aims to provide a user with easy access to professional advice when choosing a drink at a restaurant. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a display unit. The reception unit receives a question from a user. The generation unit analyzes the question received by the reception unit and generates advice. The display unit displays the advice generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows customers to easily receive professional advice when choosing a drink at a restaurant. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A beverage selection assistance system according to an embodiment of the present invention allows users to receive sommelier-style advice from a generation AI using a tablet installed on the table when selecting a drink, such as wine, at a restaurant. This beverage selection assistance system begins when a user accesses the tablet and inputs a question about their beverage selection. Possible questions include, "What red wine do you recommend?" or "What white wine goes well with fish dishes?" The question is input to a generation AI, which analyzes the question and generates appropriate advice. The generation AI suggests the best beverage for the user's question based on information such as the wine's type, origin, flavor, and price range. For example, in response to the question, "What red wine do you recommend?", the AI ​​provides specific advice such as, "I recommend a full-bodied Cabernet Sauvignon." The generated advice is displayed on the tablet screen, allowing the user to select a beverage based on the displayed advice. The advice also includes detailed information about the drink and the reasons for the recommendation, allowing the user to gain a deeper understanding. This service allows users to easily receive sommelier-style advice and gain confidence in their beverage selection. Restaurant operators can also reduce the burden on staff and improve the quality of their service. For example, if a user asks, "What red wine do you recommend?", the generative AI will provide specific advice such as, "I recommend a full-bodied Cabernet Sauvignon," based on information such as the type of wine, its origin, flavor, and price range. Furthermore, the advice will include detailed information and reasons for the recommendation, such as, "This wine features aromas of blackberry and cassis, and is perfect with steak and grilled dishes." This allows users to be more confident in their drink selection, and also helps restaurants reduce the burden on staff and improve the quality of their service. The drink selection assistance system allows users to input questions about drink selection and receive advice from the generative AI.

[0029] A beverage selection assistance system according to an embodiment includes a reception unit, a generation unit, and a display unit. The reception unit receives a question from a user. Examples of user questions include, but are not limited to, "What red wine do you recommend?" and "What white wine goes well with fish dishes?" The reception unit can input a question using, for example, a touchscreen of a tablet. The reception unit can also receive voice input. For example, when a user asks, "What red wine do you recommend?", the question can be converted into text data using voice recognition technology. The generation unit uses a generation AI to analyze the question received by the reception unit and generate advice. The generation unit generates advice based on information such as the type of wine, origin, flavor, and price range. The generation AI uses a text generation AI (e.g., LLM) to suggest the best drink for the user's question. The generation unit can also use a multimodal generation AI to generate advice including detailed information about the drink and reasons for the recommendation. For example, in response to the question, "What red wine do you recommend?", the generation AI may provide specific advice such as, "I recommend a full-bodied Cabernet Sauvignon." Furthermore, the generation unit may use the generation AI to generate advice including detailed information about the drink and the reason for the recommendation. For example, the generation unit may provide advice including detailed information and the reason for the recommendation, such as, "This wine has a distinctive blackberry and cassis aroma and is perfect with steak and grilled dishes." The display unit displays the advice generated by the generation unit. The display unit may display the advice on, for example, a tablet screen. The display unit may also provide advice by voice. For example, the advice generated by the generation AI may be output by voice using speech synthesis technology. This allows the drink selection assistance system according to the embodiment to allow a user to input a question about drink selection and receive advice from the generation AI. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit may generate advice using a generation AI model that receives a user's question as input and outputs advice.Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit may receive the advice generated by the generation unit as input and display the advice using a generation AI model that displays the advice.

[0030] The drink selection assistance system includes a voice reception unit that receives voice input. The voice reception unit allows a user to input a question by voice. For example, when a user asks, "What red wine do you recommend?", the voice reception unit converts the question into text data using voice recognition technology. The voice recognition technology includes, for example, a voice recognition model using deep learning. The voice reception unit collects the user's voice using, for example, a microphone and inputs the collected voice data into a voice recognition model. The voice recognition model analyzes the voice data and converts it into text data. For example, when a user asks, "What red wine do you recommend?", the voice reception unit analyzes the voice data and generates text data such as, "What red wine do you recommend?" This allows the user to input a question by voice. Some or all of the above-described processing in the voice reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the voice reception unit may convert the voice data into text data using a generation AI model that receives the user's voice data as input and outputs text data.

[0031] The beverage selection assistance system includes a voice output unit that provides advice by voice. The voice output unit allows the user to receive the advice by voice. For example, the voice output unit outputs the advice generated by the generation AI by voice using voice synthesis technology. Voice synthesis technology includes, for example, text-to-speech (TTS) technology. The voice output unit outputs the generated voice using, for example, a speaker. For example, the voice output unit outputs the advice generated by the generation AI, such as "We recommend a full-bodied Cabernet Sauvignon," by voice using voice synthesis technology. This allows the user to receive the advice by voice. Some or all of the above-described processing in the voice output unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the voice output unit may input the advice generated by the generation unit and output the advice by voice using a generation AI model that outputs voice.

[0032] The generation unit can generate advice based on information about the type, origin, flavor, and price range of wine. The generation unit generates advice based on information such as the type, origin, flavor, and price range of wine. The generation AI uses a text generation AI (e.g., LLM) to suggest the best drink for a user's question. For example, in response to the question, "What red wine do you recommend?", the generation unit provides specific advice such as, "I recommend a full-bodied Cabernet Sauvignon." Furthermore, the generation unit can also use the generation AI to generate advice that includes detailed information about the drink and the reason for the recommendation. For example, the generation unit provides advice such as, "This wine has a distinctive aroma of blackberry and cassis, and is perfect with steak and grilled dishes." This allows the best drink to be suggested for the user's question. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without the generation AI. For example, the generation unit can generate advice using a generation AI model that receives a user's question as input and outputs advice.

[0033] The display unit can display detailed information about the drink or the reason for the recommendation. For example, the display unit displays the advice generated by the generation unit on a tablet screen. The display unit can display detailed information about the drink and the reason for the recommendation. For example, if the generation unit generates advice such as "We recommend a full-bodied Cabernet Sauvignon," the display unit can display detailed information and the reason for the recommendation, such as "This wine is characterized by aromas of blackberry and cassis and is perfect with steak and grilled dishes." This allows the user to confirm the detailed information about the drink and the reason for the recommendation. Some or all of the above-described processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can display the advice using a generation AI model that receives the advice generated by the generation unit as input and displays the advice.

[0034] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit automatically displays questions frequently asked by the user in the past as candidates. For example, if the user has frequently asked, "What red wine do you recommend?" in the past, the reception unit automatically displays this question as a candidate. The reception unit can also prioritize suggesting question formats (text, voice, etc.) that the user has used in the past. For example, if the user has previously asked questions by voice, the reception unit prioritizes voice input. Furthermore, the reception unit can predict and suggest questions that will be asked at a specific time of day based on the user's past question history. For example, if the user previously asked, "What white wine goes well with fish dishes?" at dinner, the reception unit would suggest this question at dinner. This allows the system to be easy for users to use by selecting the optimal reception method based on the user's past question history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI. For example, the reception unit can select the reception method using a generation AI model that inputs the user's question history data and selects the optimal reception method.

[0035] When receiving a question, the reception unit can filter the questions based on the user's current eating and drinking situation and preferences. The reception unit, for example, preferentially receives questions related to the drink the user is currently drinking. For example, if the user is currently drinking red wine, the reception unit preferentially receives questions related to red wine. The reception unit can also suggest questions that match the user's preferences based on the user's past drink selection history. For example, if the user has previously preferred full-bodied red wine, the reception unit suggests questions related to full-bodied red wine. The reception unit can also preferentially receive questions related to the food the user is currently eating. For example, if the user is currently eating a fish dish, the reception unit preferentially receives questions related to drinks that go well with fish dishes. This allows questions to be filtered based on the user's current eating and drinking situation and preferences, thereby allowing more appropriate questions to be received. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can filter questions using a generative AI model that takes the user's eating and drinking history data as input and filters questions.

[0036] When receiving a question, the reception unit can prioritize receiving a question that is highly relevant based on the user's geographical location information. For example, if the user is in a specific region, the reception unit prioritizes receiving a question about drinks related to that region. For example, if the user is in a specific wine-producing region, the reception unit prioritizes receiving a question about wine related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving a question about drinks at the travel destination. For example, if the user asks, "What wines are recommended in this region?" while traveling, the reception unit prioritizes receiving a question about wine related to that region. Furthermore, if the user is participating in a specific event, the reception unit can prioritize receiving a question about drinks related to the event. For example, if the user is participating in a wine festival, the reception unit prioritizes receiving a question about wine related to the event. In this way, by prioritizing receiving a question that is highly relevant in consideration of the user's geographical location information, more appropriate questions can be received. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can select questions using a generative AI model that takes the user's geographical location information as input and selects highly relevant questions.

[0037] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit, for example, prioritizes receiving related questions based on drinks shared by the user on social media. For example, if a user shares on social media that "this wine was delicious," the reception unit prioritizes receiving questions related to that wine. The reception unit can also prioritize receiving related questions based on drink trends that the user follows on social media. For example, the reception unit prioritizes receiving related questions based on wine trends that the user follows on social media. Furthermore, the reception unit can prioritize receiving related questions based on information about drink-related groups in which the user participates on social media. For example, if the user participates in a group of wine lovers, the reception unit prioritizes receiving wine questions related to that group. This allows the reception unit to analyze the user's social media activity and receive related questions, thereby receiving more appropriate questions. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can select questions using a generative AI model that takes a user's social media data as input and selects relevant questions.

[0038] When generating advice, the generator can adjust the level of detail of the advice based on the importance of the drink. For example, in the case of a high-end wine, the generator provides advice including detailed information about the wine's origin and a description of its flavor. For example, in response to a question about a high-end wine, the generator provides detailed advice such as, "This wine is produced in the Bordeaux region of France and is characterized by the aroma of blackberry and cassis." The generator can also provide concise advice for casual drinks. For example, in response to a question about a casual drink, the generator provides concise advice such as, "This wine has a light mouthfeel and a fruity flavor." Furthermore, in the case of a drink for a special event, the generator can provide detailed advice tailored to the event. For example, in response to a question about a special event, the generator provides detailed advice such as, "This wine is perfect for Christmas dinner and pairs well with spicy dishes." By adjusting the level of detail of the advice based on the importance of the drink, more appropriate advice can be provided. Some or all of the above-described processing by the generator may be performed using, or without, a generation AI. For example, the generation unit can generate advice using a generation AI model that takes drink importance data as input and adjusts the level of detail of the advice.

[0039] When generating advice, the generation unit can apply different generation algorithms depending on the drink category. For example, for red wine, the generation unit provides advice based on categories such as full-bodied or light-bodied. For example, in response to a question about red wine, the generation unit provides advice such as "I recommend a full-bodied Cabernet Sauvignon." For white wine, the generation unit can provide advice based on categories such as dry or sweet. For example, in response to a question about white wine, the generation unit provides advice such as "I recommend a dry Chardonnay." For sparkling wine, the generation unit can provide advice based on categories such as champagne or prosecco. For example, in response to a question about sparkling wine, the generation unit provides advice such as "I recommend a Brut Champagne." This allows for more appropriate advice to be provided by applying different generation algorithms depending on the drink category. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI. For example, the generation unit can generate advice using a generation AI model that inputs drink category data and applies different generation algorithms.

[0040] When generating advice, the generation unit can determine the priority of advice based on the time when the drink is served. For example, the generation unit provides advice quickly for drinks that are served quickly. For example, the generation unit provides advice quickly in response to a question about drinks that are served quickly. Furthermore, the generation unit can provide detailed advice for drinks that take time to be served. For example, the generation unit provides detailed advice in response to a question about drinks that take time to be served. Furthermore, the generation unit can provide advice tailored to the event for drinks that are served at a specific event. For example, the generation unit provides advice tailored to the event in response to a question about a specific event. In this way, by determining the priority of advice based on the time when the drink is served, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate advice using a generation AI model that receives drink serving time data as input and determines the priority of advice.

[0041] When generating advice, the generation unit can adjust the order of advice based on the relevance of drinks. For example, the generation unit prioritizes advice on a drink that best matches a dish selected by the user. For example, the generation unit prioritizes advice on a drink that best matches a dish selected by the user in response to a question about the dish selected by the user. The generation unit can also prioritize advice on drinks that are highly relevant based on the user's past selection history. For example, the generation unit prioritizes advice on drinks that are highly relevant based on the user's past selection history. Furthermore, the generation unit can prioritize providing advice related to the user's current drink. For example, the generation unit prioritizes providing related advice in response to a question about the user's current drink. This allows more appropriate advice to be provided by adjusting the order of advice based on the relevance of drinks. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate advice using a generation AI model that receives drink relevance data as input and adjusts the order of advice.

[0042] The display unit can select an appropriate display method by referring to the user's past selection history. For example, the display unit displays related information based on information about drinks the user previously selected. For example, if the user previously selected "Cabernet Sauvignon," the display unit displays information related to that wine. The display unit can also provide a display method that matches the user's preferences based on the user's past selection history. For example, if the user previously preferred "full-bodied red wine," the display unit displays information related to full-bodied red wine. Furthermore, the display unit can analyze the user's past selection history and provide the most efficient display method. For example, if the user previously preferred "detailed information," the display unit provides a display method including detailed information. This allows for the optimal display method to be selected based on the user's past selection history, thereby providing a user-friendly system. Some or all of the above-described processing in the display unit may be performed using, for example, a generative AI, or may be performed without a generative AI. For example, the display unit can select a display method using a generative AI model that inputs the user's selection history data and selects the optimal display method.

[0043] The display unit can highlight detailed information about the drink and reasons for its recommendation when displaying the information. The display unit, for example, highlights detailed information about the drink's origin and production method. For example, the display unit highlights detailed information such as, "This wine is produced in the Bordeaux region of France and is made using traditional methods." The display unit can also highlight information about the drink's taste and aroma. For example, the display unit highlights information such as, "This wine is characterized by aromas of blackberry and cassis and has a full-bodied taste." Furthermore, the display unit can highlight reasons why the drink is recommended to help the user make a selection. For example, the display unit highlights reasons why the drink is recommended, such as, "This wine is perfect with steak and grilled dishes." Highlighting detailed information about the drink and reasons for its recommendation helps the user make a selection. Some or all of the above-described processing by the display unit may be performed, for example, using a generative AI, or may be performed without a generative AI. For example, the display unit can display information using a generative AI model that uses the advice generated by the generation unit as input and highlights detailed information and reasons for its recommendation.

[0044] The display unit can select an appropriate display method based on the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method tailored to the screen size. For example, the display unit can provide a display method optimized for the small screen of the smartphone. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for the large screen. For example, the display unit can provide a display method optimized for the large screen of the tablet. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. For example, the display unit can provide a display method optimized for the small screen of the smartwatch. This allows for the selection of the optimal display method in consideration of the user's device information, thereby providing a system that is easy for the user to use. Some or all of the above-described processing in the display unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the display unit can select the display method using a generation AI model that inputs the user's device information and selects the optimal display method.

[0045] The display unit can analyze the user's social media activity and display related information when displaying the information. The display unit can display related information based on, for example, drinks shared by the user on social media. For example, if a user shares on social media that "this wine was delicious," the display unit can display information related to that wine. The display unit can also display related information based on drink trends that the user follows on social media. For example, related information can be displayed based on wine trends that the user follows on social media. Furthermore, the display unit can display related information based on information about drink-related groups in which the user participates on social media. For example, if the user participates in a group of wine lovers, the display unit can display information about wines related to that group. This allows the display unit to provide more appropriate information by analyzing the user's social media activity and displaying related information. Some or all of the above-described processing by the display unit can be performed using, for example, a generative AI. For example, the display unit can display information using a generative AI model that inputs the user's social media data and selects related information.

[0046] The voice reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the voice reception unit automatically displays voice inputs that the user frequently used in the past as candidates. For example, if the user frequently voice-inputs, "What red wine do you recommend?" in the past, the voice reception unit automatically displays this voice input as a candidate. The voice reception unit can also prioritize suggestions of voice input formats (such as ways of asking questions) that the user has used in the past. For example, if the user has previously asked a question by voice, the voice reception unit prioritizes suggestions of the voice input. Furthermore, the voice reception unit can predict and suggest voice inputs to be made in a specific time period based on the user's past voice input history. For example, if the user previously voice-inputs, "What white wine would go well with fish dishes?" at dinner, the voice reception unit recommends this voice input at dinner time. This allows for the selection of the optimal reception method based on the user's past voice input history, thereby providing a system that is easy for the user to use. Some or all of the above-described processing in the voice reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the voice reception unit can select a reception method using a generative AI model that receives the user's voice input history data as input and selects the optimal reception method.

[0047] When a voice input is made, the voice receiving unit can prioritize receiving a voice input that is highly relevant, taking into account the user's geographical location information. For example, when the user is in a specific region, the voice receiving unit prioritizes receiving a voice input about drinks related to that region. For example, when the user is in a specific wine-producing region, the voice receiving unit prioritizes receiving a voice input about wines related to that region. Furthermore, when the user is traveling, the voice receiving unit can prioritize receiving a voice input about drinks at the travel destination. For example, when the user voice-inputs, "What wines do you recommend in this region?" while traveling, the voice receiving unit prioritizes receiving a voice input about wines related to that region. Furthermore, when the user is participating in a specific event, the voice receiving unit can prioritize receiving a voice input about drinks related to that event. For example, when the user is participating in a wine festival, the voice receiving unit prioritizes receiving a voice input about wines related to that event. In this way, by preferentially receiving a voice input that is highly relevant, taking into account the user's geographical location information, more appropriate voice input can be received. Some or all of the above-described processing in the voice receiving unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the voice receiving unit may select a voice input using a generation AI model that receives a user's geographical location information as input and selects a highly relevant voice input.

[0048] When outputting audio, the audio output unit can select the optimal output method by referring to the user's past selection history. For example, the audio output unit outputs related information based on information about drinks the user has previously selected. For example, if the user previously selected "Cabernet Sauvignon," the audio output unit outputs information related to that wine. The audio output unit can also provide an audio output method that matches the user's preferences based on the user's past selection history. For example, if the user previously preferred "full-bodied red wine," the audio output unit outputs information related to full-bodied red wine. Furthermore, the audio output unit can analyze the user's past selection history and provide the most efficient audio output method. For example, if the user previously preferred "detailed information," the audio output unit provides an audio output method including detailed information. This allows for the optimal output method to be selected based on the user's past selection history, thereby providing a system that is easy for the user to use. Some or all of the above-described processing in the audio output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the audio output unit can select an output method using a generative AI model that inputs the user's selection history data and selects the optimal output method.

[0049] When outputting audio, the audio output unit can select the optimal output method by taking into account the user's device information. For example, if the user is using a smartphone, the audio output unit provides audio output optimized for the device. For example, the audio output unit provides audio output optimized for the smartphone's small speaker. Furthermore, if the user is using a tablet, the audio output unit can provide audio output optimized for a large screen. For example, the audio output unit provides audio output optimized for the tablet's large speaker. Furthermore, if the user is using a smartwatch, the audio output unit can provide concise and highly visible audio output. For example, the audio output unit provides audio output optimized for the smartwatch's small speaker. This allows for selecting the optimal output method by taking into account the user's device information, thereby providing a system that is easy for the user to use. Some or all of the above-described processing in the audio output unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the audio output unit can select the output method using a generation AI model that inputs the user's device information and selects the optimal output method.

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

[0051] The reception unit can analyze the user's past drink selection history and present candidate questions based on the user's preferences. For example, if the user has previously preferred full-bodied red wine, the reception unit can preferentially present questions related to full-bodied red wine. The reception unit can also display detailed information about drinks the user has previously selected, which the user can use as reference when selecting the same drink again. Furthermore, the reception unit can predict and suggest questions related to specific time periods or events based on the user's past selection history. For example, if the user has previously selected a specific wine for Christmas dinner, the reception unit can suggest questions related to that wine during the Christmas season. This makes it possible to provide a system that is easy for users to use by presenting optimal questions based on the user's past selection history.

[0052] The display unit can adjust the display method based on the user's device usage status. For example, when the user is using a smartphone, the display unit can provide a display method optimized for the screen size. When the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the display unit can provide a simple and highly visible display method. This makes it possible to provide a system that is easy for users to use by providing an optimal display method according to the user's device usage status.

[0053] The display unit can refer to the user's past selection history and highlight related information. For example, it can highlight detailed information about drinks the user has previously selected and the reasons why they are recommended. It can also display new information related to drinks the user has previously selected. Furthermore, it can suggest drinks that suit the user's preferences based on the user's past selection history and highlight that information. This makes it possible to provide optimal information based on the user's past selection history.

[0054] The generation unit can analyze the user's past selection history and provide advice based on the user's preferences. For example, based on information about drinks the user has selected in the past, the generation unit can suggest related drinks. The generation unit can also suggest drinks related to specific time periods or events based on the user's past selection history. Furthermore, based on the user's past selection history, the generation unit can suggest drinks with the user's preferred tastes and aromas. This makes it possible to provide optimal advice based on the user's past selection history.

[0055] The reception unit can prioritize receiving highly relevant questions based on the user's geographical location information. For example, if the user is in a specific area, questions about drinks related to that area can be prioritized. Also, if the user is traveling, questions about drinks at the travel destination can be prioritized. Furthermore, if the user is participating in a specific event, questions about drinks related to the event can be prioritized. In this way, by prioritizing highly relevant questions in consideration of the user's geographical location information, more appropriate questions can be received.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit receives a question from a user. Examples of user questions include, but are not limited to, "What red wine do you recommend?" or "What white wine goes well with fish dishes?" The reception unit can input a question using, for example, a touch screen on a tablet. The reception unit can also receive voice input. For example, if a user asks by voice, "What red wine do you recommend?", the question can be converted into text data using voice recognition technology. Step 2: The generation unit uses a generation AI to analyze the question received by the reception unit and generate advice. The generation unit generates advice based on information such as the type of wine, origin, flavor, and price range. The generation AI uses a text generation AI (e.g., LLM) to suggest the best drink for the user's question. The generation unit can also use a multimodal generation AI to generate advice that includes detailed information about the drink and the reason for the recommendation. For example, in response to the question, "What red wine do you recommend?", the generation AI provides specific advice such as, "I recommend a full-bodied Cabernet Sauvignon." The generation unit can also use a generation AI to generate advice that includes detailed information about the drink and the reason for the recommendation. For example, the generation unit provides advice that includes detailed information about the drink and the reason for the recommendation, such as, "This wine is characterized by aromas of blackberry and cassis and is perfect with steak and grilled dishes." Step 3: The display unit displays the advice generated by the generation unit. The display unit can display the advice on a tablet screen, for example. The display unit can also provide the advice by voice. For example, the advice generated by the generation AI can be output by voice using voice synthesis technology.

[0058] (Example 2) A beverage selection assistance system according to an embodiment of the present invention allows users to receive sommelier-style advice from a generation AI using a tablet installed on the table when selecting a drink, such as wine, at a restaurant. This beverage selection assistance system begins when a user accesses the tablet and inputs a question about their beverage selection. Possible questions include, "What red wine do you recommend?" or "What white wine goes well with fish dishes?" The question is input to a generation AI, which analyzes the question and generates appropriate advice. The generation AI suggests the best beverage for the user's question based on information such as the wine's type, origin, flavor, and price range. For example, in response to the question, "What red wine do you recommend?", the AI ​​provides specific advice such as, "I recommend a full-bodied Cabernet Sauvignon." The generated advice is displayed on the tablet screen, allowing the user to select a beverage based on the displayed advice. The advice also includes detailed information about the drink and the reasons for the recommendation, allowing the user to gain a deeper understanding. This service allows users to easily receive sommelier-style advice and gain confidence in their beverage selection. Restaurant operators can also reduce the burden on staff and improve the quality of their service. For example, if a user asks, "What red wine do you recommend?", the generative AI will provide specific advice such as, "I recommend a full-bodied Cabernet Sauvignon," based on information such as the type of wine, its origin, flavor, and price range. Furthermore, the advice will include detailed information and reasons for the recommendation, such as, "This wine features aromas of blackberry and cassis, and is perfect with steak and grilled dishes." This allows users to be more confident in their drink selection, and also helps restaurants reduce the burden on staff and improve the quality of their service. The drink selection assistance system allows users to input questions about drink selection and receive advice from the generative AI.

[0059] A beverage selection assistance system according to an embodiment includes a reception unit, a generation unit, and a display unit. The reception unit receives a question from a user. Examples of user questions include, but are not limited to, "What red wine do you recommend?" and "What white wine goes well with fish dishes?" The reception unit can input a question using, for example, a touchscreen of a tablet. The reception unit can also receive voice input. For example, when a user asks, "What red wine do you recommend?", the question can be converted into text data using voice recognition technology. The generation unit uses a generation AI to analyze the question received by the reception unit and generate advice. The generation unit generates advice based on information such as the type of wine, origin, flavor, and price range. The generation AI uses a text generation AI (e.g., LLM) to suggest the best drink for the user's question. The generation unit can also use a multimodal generation AI to generate advice including detailed information about the drink and reasons for the recommendation. For example, in response to the question, "What red wine do you recommend?", the generation AI may provide specific advice such as, "I recommend a full-bodied Cabernet Sauvignon." Furthermore, the generation unit may use the generation AI to generate advice including detailed information about the drink and the reason for the recommendation. For example, the generation unit may provide advice including detailed information and the reason for the recommendation, such as, "This wine has a distinctive blackberry and cassis aroma and is perfect with steak and grilled dishes." The display unit displays the advice generated by the generation unit. The display unit may display the advice on, for example, a tablet screen. The display unit may also provide advice by voice. For example, the advice generated by the generation AI may be output by voice using speech synthesis technology. This allows the drink selection assistance system according to the embodiment to allow a user to input a question about drink selection and receive advice from the generation AI. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit may generate advice using a generation AI model that receives a user's question as input and outputs advice.Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit may receive the advice generated by the generation unit as input and display the advice using a generation AI model that displays the advice.

[0060] The drink selection assistance system includes a voice reception unit that receives voice input. The voice reception unit allows a user to input a question by voice. For example, when a user asks, "What red wine do you recommend?", the voice reception unit converts the question into text data using voice recognition technology. The voice recognition technology includes, for example, a voice recognition model using deep learning. The voice reception unit collects the user's voice using, for example, a microphone and inputs the collected voice data into a voice recognition model. The voice recognition model analyzes the voice data and converts it into text data. For example, when a user asks, "What red wine do you recommend?", the voice reception unit analyzes the voice data and generates text data such as, "What red wine do you recommend?" This allows the user to input a question by voice. Some or all of the above-described processing in the voice reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the voice reception unit may convert the voice data into text data using a generation AI model that receives the user's voice data as input and outputs text data.

[0061] The beverage selection assistance system includes a voice output unit that provides advice by voice. The voice output unit allows the user to receive the advice by voice. For example, the voice output unit outputs the advice generated by the generation AI by voice using voice synthesis technology. Voice synthesis technology includes, for example, text-to-speech (TTS) technology. The voice output unit outputs the generated voice using, for example, a speaker. For example, the voice output unit outputs the advice generated by the generation AI, such as "We recommend a full-bodied Cabernet Sauvignon," by voice using voice synthesis technology. This allows the user to receive the advice by voice. Some or all of the above-described processing in the voice output unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the voice output unit may input the advice generated by the generation unit and output the advice by voice using a generation AI model that outputs voice.

[0062] The generation unit can generate advice based on information about the type, origin, flavor, and price range of wine. The generation unit generates advice based on information such as the type, origin, flavor, and price range of wine. The generation AI uses a text generation AI (e.g., LLM) to suggest the best drink for a user's question. For example, in response to the question, "What red wine do you recommend?", the generation unit provides specific advice such as, "I recommend a full-bodied Cabernet Sauvignon." Furthermore, the generation unit can also use the generation AI to generate advice that includes detailed information about the drink and the reason for the recommendation. For example, the generation unit provides advice such as, "This wine has a distinctive aroma of blackberry and cassis, and is perfect with steak and grilled dishes." This allows the best drink to be suggested for the user's question. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without the generation AI. For example, the generation unit can generate advice using a generation AI model that receives a user's question as input and outputs advice.

[0063] The display unit can display detailed information about the drink or the reason for the recommendation. For example, the display unit displays the advice generated by the generation unit on a tablet screen. The display unit can display detailed information about the drink and the reason for the recommendation. For example, if the generation unit generates advice such as "We recommend a full-bodied Cabernet Sauvignon," the display unit can display detailed information and the reason for the recommendation, such as "This wine is characterized by aromas of blackberry and cassis and is perfect with steak and grilled dishes." This allows the user to confirm the detailed information about the drink and the reason for the recommendation. Some or all of the above-described processing in the display unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the display unit can display the advice using a generation AI model that receives the advice generated by the generation unit as input and displays the advice.

[0064] The reception unit can estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. For example, if the user is nervous, the reception unit delays the timing of receiving questions so that the user can relax. For example, if the user is nervous, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm estimates the user's emotion using, for example, facial expression recognition technology. For example, if the user is nervous, the reception unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is nervous. Next, the reception unit delays the timing of receiving questions so that the user can relax. Furthermore, if the user is relaxed, the reception unit can immediately accept questions. For example, if the user is relaxed, the reception unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is relaxed. Next, the reception unit immediately accepts questions. Furthermore, if the user is in a hurry, the reception unit can quickly accept questions. For example, if the user is in a hurry, the reception unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is in a hurry. Next, the reception unit quickly receives the question. This allows the timing of receiving the question to be adjusted according to the user's emotion, thereby allowing the question to be received at a more appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the reception unit can estimate the emotion using a generative AI model that estimates emotions using facial expression data of the user as input.

[0065] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit automatically displays questions frequently asked by the user in the past as candidates. For example, if the user has frequently asked, "What red wine do you recommend?" in the past, the reception unit automatically displays this question as a candidate. The reception unit can also prioritize suggesting question formats (text, voice, etc.) that the user has used in the past. For example, if the user has previously asked questions by voice, the reception unit prioritizes voice input. Furthermore, the reception unit can predict and suggest questions that will be asked at a specific time of day based on the user's past question history. For example, if the user previously asked, "What white wine goes well with fish dishes?" at dinner, the reception unit would suggest this question at dinner. This allows the system to be easy for users to use by selecting the optimal reception method based on the user's past question history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI. For example, the reception unit can select the reception method using a generation AI model that inputs the user's question history data and selects the optimal reception method.

[0066] When receiving a question, the reception unit can filter the questions based on the user's current eating and drinking situation and preferences. The reception unit, for example, preferentially receives questions related to the drink the user is currently drinking. For example, if the user is currently drinking red wine, the reception unit preferentially receives questions related to red wine. The reception unit can also suggest questions that match the user's preferences based on the user's past drink selection history. For example, if the user has previously preferred full-bodied red wine, the reception unit suggests questions related to full-bodied red wine. The reception unit can also preferentially receive questions related to the food the user is currently eating. For example, if the user is currently eating a fish dish, the reception unit preferentially receives questions related to drinks that go well with fish dishes. This allows questions to be filtered based on the user's current eating and drinking situation and preferences, thereby allowing more appropriate questions to be received. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can filter questions using a generative AI model that takes the user's eating and drinking history data as input and filters questions.

[0067] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user's emotions. For example, if the user is nervous, the reception unit preferentially receives simple questions. For example, if the user is nervous, the reception unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is nervous. Next, the reception unit preferentially receives simple questions. Furthermore, if the user is relaxed, the reception unit can preferentially receive detailed questions. For example, if the user is relaxed, the reception unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is relaxed. Next, the reception unit preferentially receives detailed questions. Furthermore, if the user is in a hurry, the reception unit can preferentially receive questions that can be answered quickly. For example, if the user is in a hurry, the reception unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is in a hurry. Next, the reception unit preferentially receives questions that can be answered quickly. In this way, by determining the priority of questions according to the user's emotions, more appropriate questions can be preferentially received. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the reception unit can input the user's facial expression data and estimate the emotion using a generative AI model that estimates emotions.

[0068] When receiving a question, the reception unit can prioritize receiving a question that is highly relevant based on the user's geographical location information. For example, if the user is in a specific region, the reception unit prioritizes receiving a question about drinks related to that region. For example, if the user is in a specific wine-producing region, the reception unit prioritizes receiving a question about wine related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving a question about drinks at the travel destination. For example, if the user asks, "What wines are recommended in this region?" while traveling, the reception unit prioritizes receiving a question about wine related to that region. Furthermore, if the user is participating in a specific event, the reception unit can prioritize receiving a question about drinks related to the event. For example, if the user is participating in a wine festival, the reception unit prioritizes receiving a question about wine related to the event. In this way, by prioritizing receiving a question that is highly relevant in consideration of the user's geographical location information, more appropriate questions can be received. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can select questions using a generative AI model that takes the user's geographical location information as input and selects highly relevant questions.

[0069] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit, for example, prioritizes receiving related questions based on drinks shared by the user on social media. For example, if a user shares on social media that "this wine was delicious," the reception unit prioritizes receiving questions related to that wine. The reception unit can also prioritize receiving related questions based on drink trends that the user follows on social media. For example, the reception unit prioritizes receiving related questions based on wine trends that the user follows on social media. Furthermore, the reception unit can prioritize receiving related questions based on information about drink-related groups in which the user participates on social media. For example, if the user participates in a group of wine lovers, the reception unit prioritizes receiving wine questions related to that group. This allows the reception unit to analyze the user's social media activity and receive related questions, thereby receiving more appropriate questions. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can select questions using a generative AI model that takes a user's social media data as input and selects relevant questions.

[0070] The generation unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is nervous, the generation unit provides advice in gentle language. For example, if the user is nervous, the generation unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is nervous. Then, the generation unit provides advice in gentle language. Furthermore, if the user is relaxed, the generation unit can provide advice including detailed information. For example, if the user is relaxed, the generation unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is relaxed. Then, the generation unit provides advice including detailed information. Furthermore, if the user is in a hurry, the generation unit can provide concise and quick advice. For example, if the user is in a hurry, the generation unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is in a hurry. Then, the generation unit provides concise and quick advice. This allows the system to provide more appropriate advice by adjusting the way the advice is expressed depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit may input a user's facial expression data and estimate emotions using a generation AI model that estimates emotions.

[0071] When generating advice, the generator can adjust the level of detail of the advice based on the importance of the drink. For example, in the case of a high-end wine, the generator provides advice including detailed information about the wine's origin and a description of its flavor. For example, in response to a question about a high-end wine, the generator provides detailed advice such as, "This wine is produced in the Bordeaux region of France and is characterized by the aroma of blackberry and cassis." The generator can also provide concise advice for casual drinks. For example, in response to a question about a casual drink, the generator provides concise advice such as, "This wine has a light mouthfeel and a fruity flavor." Furthermore, in the case of a drink for a special event, the generator can provide detailed advice tailored to the event. For example, in response to a question about a special event, the generator provides detailed advice such as, "This wine is perfect for Christmas dinner and pairs well with spicy dishes." By adjusting the level of detail of the advice based on the importance of the drink, more appropriate advice can be provided. Some or all of the above-described processing by the generator may be performed using, or without, a generation AI. For example, the generation unit can generate advice using a generation AI model that takes drink importance data as input and adjusts the level of detail of the advice.

[0072] When generating advice, the generation unit can apply different generation algorithms depending on the drink category. For example, for red wine, the generation unit provides advice based on categories such as full-bodied or light-bodied. For example, in response to a question about red wine, the generation unit provides advice such as "I recommend a full-bodied Cabernet Sauvignon." For white wine, the generation unit can provide advice based on categories such as dry or sweet. For example, in response to a question about white wine, the generation unit provides advice such as "I recommend a dry Chardonnay." For sparkling wine, the generation unit can provide advice based on categories such as champagne or prosecco. For example, in response to a question about sparkling wine, the generation unit provides advice such as "I recommend a Brut Champagne." This allows for more appropriate advice to be provided by applying different generation algorithms depending on the drink category. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI. For example, the generation unit can generate advice using a generation AI model that inputs drink category data and applies different generation algorithms.

[0073] The generation unit can estimate the user's emotions and adjust the length of advice based on the estimated user emotions. For example, if the user is nervous, the generation unit provides short, to-the-point advice. For example, if the user is nervous, the generation unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is nervous. Then, the generation unit provides short, to-the-point advice. Furthermore, if the user is relaxed, the generation unit can provide longer advice with detailed explanations. For example, if the user is relaxed, the generation unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is relaxed. Then, the generation unit provides longer advice with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can provide quick, concise advice. For example, if the user is in a hurry, the generation unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is in a hurry. Then, the generation unit provides quick, concise advice. This allows the length of advice to be adjusted according to the user's emotions, thereby providing more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit may input a user's facial expression data and estimate emotions using a generation AI model that estimates emotions.

[0074] When generating advice, the generation unit can determine the priority of advice based on the time when the drink is served. For example, the generation unit provides advice quickly for drinks that are served quickly. For example, the generation unit provides advice quickly in response to a question about drinks that are served quickly. Furthermore, the generation unit can provide detailed advice for drinks that take time to be served. For example, the generation unit provides detailed advice in response to a question about drinks that take time to be served. Furthermore, the generation unit can provide advice tailored to the event for drinks that are served at a specific event. For example, the generation unit provides advice tailored to the event in response to a question about a specific event. In this way, by determining the priority of advice based on the time when the drink is served, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate advice using a generation AI model that receives drink serving time data as input and determines the priority of advice.

[0075] When generating advice, the generation unit can adjust the order of advice based on the relevance of drinks. For example, the generation unit prioritizes advice on a drink that best matches a dish selected by the user. For example, the generation unit prioritizes advice on a drink that best matches a dish selected by the user in response to a question about the dish selected by the user. The generation unit can also prioritize advice on drinks that are highly relevant based on the user's past selection history. For example, the generation unit prioritizes advice on drinks that are highly relevant based on the user's past selection history. Furthermore, the generation unit can prioritize providing advice related to the user's current drink. For example, the generation unit prioritizes providing related advice in response to a question about the user's current drink. This allows more appropriate advice to be provided by adjusting the order of advice based on the relevance of drinks. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate advice using a generation AI model that receives drink relevance data as input and adjusts the order of advice.

[0076] The display unit can estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, if the user is nervous, the display unit provides a simple, highly visible display method. For example, if the user is nervous, the display unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is nervous. Then, the display unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the display unit can provide a display method including detailed information. For example, if the user is relaxed, the display unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is relaxed. Then, the display unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the display unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the display unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is in a hurry. Then, the display unit provides a display method that focuses on the main points. This allows the display method to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generative AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the display unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the display unit may input a user's facial expression data and estimate emotions using a generative AI model that estimates emotions.

[0077] The display unit can select an appropriate display method by referring to the user's past selection history. For example, the display unit displays related information based on information about drinks the user previously selected. For example, if the user previously selected "Cabernet Sauvignon," the display unit displays information related to that wine. The display unit can also provide a display method that matches the user's preferences based on the user's past selection history. For example, if the user previously preferred "full-bodied red wine," the display unit displays information related to full-bodied red wine. Furthermore, the display unit can analyze the user's past selection history and provide the most efficient display method. For example, if the user previously preferred "detailed information," the display unit provides a display method including detailed information. This allows for the optimal display method to be selected based on the user's past selection history, thereby providing a user-friendly system. Some or all of the above-described processing in the display unit may be performed using, for example, a generative AI, or may be performed without a generative AI. For example, the display unit can select a display method using a generative AI model that inputs the user's selection history data and selects the optimal display method.

[0078] The display unit can highlight detailed information about the drink and reasons for its recommendation when displaying the information. The display unit, for example, highlights detailed information about the drink's origin and production method. For example, the display unit highlights detailed information such as, "This wine is produced in the Bordeaux region of France and is made using traditional methods." The display unit can also highlight information about the drink's taste and aroma. For example, the display unit highlights information such as, "This wine is characterized by aromas of blackberry and cassis and has a full-bodied taste." Furthermore, the display unit can highlight reasons why the drink is recommended to help the user make a selection. For example, the display unit highlights reasons why the drink is recommended, such as, "This wine is perfect with steak and grilled dishes." Highlighting detailed information about the drink and reasons for its recommendation helps the user make a selection. Some or all of the above-described processing by the display unit may be performed, for example, using a generative AI, or may be performed without a generative AI. For example, the display unit can display information using a generative AI model that uses the advice generated by the generation unit as input and highlights detailed information and reasons for its recommendation.

[0079] The display unit can estimate the user's emotions and prioritize display content based on the estimated user emotions. For example, if the user is nervous, the display unit prioritizes displaying simple information. For example, if the user is nervous, the display unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is nervous. Then, the display unit prioritizes displaying simple information. Furthermore, if the user is relaxed, the display unit can prioritize displaying detailed information. For example, if the user is relaxed, the display unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is relaxed. Then, the display unit prioritizes displaying detailed information. Furthermore, if the user is in a hurry, the display unit can prioritize displaying information that can be quickly understood. For example, if the user is in a hurry, the display unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is in a hurry. Then, the display unit prioritizes displaying information that can be quickly understood. In this way, by prioritizing display content according to the user's emotions, more appropriate information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generative AI may be, but is not limited to, a text generative AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the display unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the display unit may input a user's facial expression data and estimate emotions using a generative AI model that estimates emotions.

[0080] The display unit can select an appropriate display method based on the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method tailored to the screen size. For example, the display unit can provide a display method optimized for the small screen of the smartphone. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for the large screen. For example, the display unit can provide a display method optimized for the large screen of the tablet. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. For example, the display unit can provide a display method optimized for the small screen of the smartwatch. This allows for the selection of the optimal display method in consideration of the user's device information, thereby providing a system that is easy for the user to use. Some or all of the above-described processing in the display unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the display unit can select the display method using a generation AI model that inputs the user's device information and selects the optimal display method.

[0081] The display unit can analyze the user's social media activity and display related information when displaying the information. The display unit can display related information based on, for example, drinks shared by the user on social media. For example, if a user shares on social media that "this wine was delicious," the display unit can display information related to that wine. The display unit can also display related information based on drink trends that the user follows on social media. For example, related information can be displayed based on wine trends that the user follows on social media. Furthermore, the display unit can display related information based on information about drink-related groups in which the user participates on social media. For example, if the user participates in a group of wine lovers, the display unit can display information about wines related to that group. This allows the display unit to provide more appropriate information by analyzing the user's social media activity and displaying related information. Some or all of the above-described processing by the display unit can be performed using, for example, a generative AI. For example, the display unit can display information using a generative AI model that inputs the user's social media data and selects related information.

[0082] The voice accepting unit can estimate the user's emotions and adjust the timing of accepting the voice input based on the estimated user's emotions. For example, if the user is nervous, the voice accepting unit delays the timing of accepting the voice input so that the user can relax. For example, if the user is nervous, the voice accepting unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is nervous. Next, the voice accepting unit delays the timing of accepting the voice input so that the user can relax. Furthermore, if the user is relaxed, the voice accepting unit can immediately accept the voice input. For example, if the user is relaxed, the voice accepting unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is relaxed. Next, the voice accepting unit immediately accepts the voice input. Furthermore, if the user is in a hurry, the voice accepting unit can quickly accept the voice input. For example, if the user is in a hurry, the voice accepting unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is in a hurry. Next, the voice accepting unit quickly accepts the voice input. In this way, by adjusting the timing of accepting the voice input according to the user's emotions, the voice input can be accepted at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the voice receiving unit may be performed, for example, using the generative AI, or may be performed without using the generative AI. For example, the voice receiving unit can input user facial expression data and estimate emotions using a generative AI model that estimates emotions.

[0083] The voice reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the voice reception unit automatically displays voice inputs that the user frequently used in the past as candidates. For example, if the user frequently voice-inputs, "What red wine do you recommend?" in the past, the voice reception unit automatically displays this voice input as a candidate. The voice reception unit can also prioritize suggestions of voice input formats (such as ways of asking questions) that the user has used in the past. For example, if the user has previously asked a question by voice, the voice reception unit prioritizes suggestions of the voice input. Furthermore, the voice reception unit can predict and suggest voice inputs to be made in a specific time period based on the user's past voice input history. For example, if the user previously voice-inputs, "What white wine would go well with fish dishes?" at dinner, the voice reception unit recommends this voice input at dinner time. This allows for the selection of the optimal reception method based on the user's past voice input history, thereby providing a system that is easy for the user to use. Some or all of the above-described processing in the voice reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the voice reception unit can select a reception method using a generative AI model that receives the user's voice input history data as input and selects the optimal reception method.

[0084] The voice accepting unit can estimate the user's emotions and determine the priority of voice inputs based on the estimated user's emotions. For example, if the user is nervous, the voice accepting unit preferentially accepts simple voice inputs. For example, if the user is nervous, the voice accepting unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is nervous. Next, the voice accepting unit preferentially accepts simple voice inputs. Furthermore, if the user is relaxed, the voice accepting unit can preferentially accept detailed voice inputs. For example, if the user is relaxed, the voice accepting unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is relaxed. Next, the voice accepting unit preferentially accepts detailed voice inputs. Furthermore, if the user is in a hurry, the voice accepting unit can preferentially accept voice inputs that can be quickly answered. For example, if the user is in a hurry, the voice accepting unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is in a hurry. Next, the voice accepting unit preferentially accepts voice inputs that can be quickly answered. This allows the priority of voice inputs to be determined according to the user's emotions, thereby enabling more appropriate voice inputs to be preferentially accepted. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the voice receiving unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the voice receiving unit can estimate emotions using a generative AI model that receives input of the user's facial expression data and estimates emotions.

[0085] When a voice input is made, the voice receiving unit can prioritize receiving a voice input that is highly relevant, taking into account the user's geographical location information. For example, when the user is in a specific region, the voice receiving unit prioritizes receiving a voice input about drinks related to that region. For example, when the user is in a specific wine-producing region, the voice receiving unit prioritizes receiving a voice input about wines related to that region. Furthermore, when the user is traveling, the voice receiving unit can prioritize receiving a voice input about drinks at the travel destination. For example, when the user voice-inputs, "What wines do you recommend in this region?" while traveling, the voice receiving unit prioritizes receiving a voice input about wines related to that region. Furthermore, when the user is participating in a specific event, the voice receiving unit can prioritize receiving a voice input about drinks related to that event. For example, when the user is participating in a wine festival, the voice receiving unit prioritizes receiving a voice input about wines related to that event. In this way, by preferentially receiving a voice input that is highly relevant, taking into account the user's geographical location information, more appropriate voice input can be received. Some or all of the above-described processing in the voice receiving unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the voice receiving unit may select a voice input using a generation AI model that receives a user's geographical location information as input and selects a highly relevant voice input.

[0086] The audio output unit can estimate the user's emotions and adjust the audio output expression method based on the estimated user's emotions. For example, if the user is nervous, the audio output unit outputs audio in a gentle voice. For example, if the user is nervous, the audio output unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is nervous. Next, the audio output unit outputs audio in a gentle voice. Furthermore, if the user is relaxed, the audio output unit can output audio including detailed information. For example, if the user is relaxed, the audio output unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is relaxed. Next, the audio output unit outputs audio including detailed information. Furthermore, if the user is in a hurry, the audio output unit can output concise and quick audio. For example, if the user is in a hurry, the audio output unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is in a hurry. Next, the audio output unit outputs concise and quick audio. In this way, by adjusting the audio output expression method according to the user's emotions, more appropriate audio output can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the audio output unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the audio output unit can estimate emotions using a generative AI model that estimates emotions based on input of a user's facial expression data.

[0087] When outputting audio, the audio output unit can select the optimal output method by referring to the user's past selection history. For example, the audio output unit outputs related information based on information about drinks the user has previously selected. For example, if the user previously selected "Cabernet Sauvignon," the audio output unit outputs information related to that wine. The audio output unit can also provide an audio output method that matches the user's preferences based on the user's past selection history. For example, if the user previously preferred "full-bodied red wine," the audio output unit outputs information related to full-bodied red wine. Furthermore, the audio output unit can analyze the user's past selection history and provide the most efficient audio output method. For example, if the user previously preferred "detailed information," the audio output unit provides an audio output method including detailed information. This allows for the optimal output method to be selected based on the user's past selection history, thereby providing a system that is easy for the user to use. Some or all of the above-described processing in the audio output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the audio output unit can select an output method using a generative AI model that inputs the user's selection history data and selects the optimal output method.

[0088] The audio output unit can estimate the user's emotions and determine the priority of audio output based on the estimated user's emotions. For example, if the user is nervous, the audio output unit prioritizes audio output of simple information. For example, if the user is nervous, the audio output unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is nervous. Next, the audio output unit prioritizes audio output of simple information. Furthermore, if the user is relaxed, the audio output unit can prioritize audio output of detailed information. For example, if the user is relaxed, the audio output unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is relaxed. Next, the audio output unit prioritizes audio output of detailed information. Furthermore, if the user is in a hurry, the audio output unit can prioritize audio output of information that can be quickly understood. For example, if the user is in a hurry, the audio output unit analyzes the user's facial expression using facial expression recognition technology and determines that the user is in a hurry. Next, the audio output unit prioritizes audio output of information that can be quickly understood. In this way, by determining the priority of audio output according to the user's emotions, it is possible to provide more appropriate audio output preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the audio output unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the audio output unit can estimate emotions using a generative AI model that estimates emotions based on input of a user's facial expression data.

[0089] When outputting audio, the audio output unit can select the optimal output method by taking into account the user's device information. For example, if the user is using a smartphone, the audio output unit provides audio output optimized for the device. For example, the audio output unit provides audio output optimized for the smartphone's small speaker. Furthermore, if the user is using a tablet, the audio output unit can provide audio output optimized for a large screen. For example, the audio output unit provides audio output optimized for the tablet's large speaker. Furthermore, if the user is using a smartwatch, the audio output unit can provide concise and highly visible audio output. For example, the audio output unit provides audio output optimized for the smartwatch's small speaker. This allows for selecting the optimal output method by taking into account the user's device information, thereby providing a system that is easy for the user to use. Some or all of the above-described processing in the audio output unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the audio output unit can select the output method using a generation AI model that inputs the user's device information and selects the optimal output method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, display unit, voice reception unit, and voice output unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized using the touch screen of the smart device 14 and receives a user's question. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice using a generation AI. The display unit displays the advice using the display 40A of the smart device 14. The voice reception unit collects the user's voice using the microphone 38B of the smart device 14 and converts it into text data using voice recognition technology. The voice output unit outputs the generated advice as voice using the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, voice reception unit, and voice output unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized using the touchscreen of the smart glasses 214 and receives a user's question. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice using a generation AI. The display unit displays the advice using the display of the smart glasses 214. The voice reception unit collects the user's voice using the microphone 238 of the smart glasses 214 and converts it into text data using voice recognition technology. The voice output unit outputs the generated advice as voice using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, voice reception unit, and voice output unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized using the touch screen of the headset type terminal 314 and receives a user's question. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice using a generation AI. The display unit displays the advice using the display 343 of the headset type terminal 314. The voice reception unit collects the user's voice using the microphone 238 of the headset type terminal 314 and converts it into text data using voice recognition technology. The voice output unit outputs the generated advice as voice using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, voice reception unit, and voice output unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized using a touchscreen of the robot 414 and receives a user's question. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice using a generation AI. The display unit displays the advice using the display of the robot 414. The voice reception unit collects the user's voice using the microphone 238 of the robot 414 and converts it into text data using voice recognition technology. The voice output unit outputs the generated advice by voice using the speaker 240 of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can analyze the user's past drink selection history and present candidate questions based on the user's preferences. For example, if the user has previously preferred full-bodied red wine, the reception unit can preferentially present questions related to full-bodied red wine. The reception unit can also display detailed information about drinks the user has previously selected, which the user can use as reference when selecting the same drink again. Furthermore, the reception unit can predict and suggest questions related to specific time periods or events based on the user's past selection history. For example, if the user has previously selected a specific wine for Christmas dinner, the reception unit can suggest questions related to that wine during the Christmas season. This makes it possible to provide a system that is easy for users to use by presenting optimal questions based on the user's past selection history.

[0092] The generation unit can adjust the advice based on the user's current mood and physical condition. For example, if the user feels like relaxing, the generation unit can suggest relaxing herbal tea or light wine. If the user feels tired, the generation unit can suggest drinks that can replenish energy or drinks that have a refreshing effect. Furthermore, if the user has a specific health condition, the generation unit can suggest drinks that are suitable for that condition. For example, if the user has a cold, the generation unit can suggest drinks rich in vitamin C or hot drinks. This makes it possible to provide optimal advice according to the user's current mood and physical condition.

[0093] The display unit can adjust the display method based on the user's device usage status. For example, when the user is using a smartphone, the display unit can provide a display method optimized for the screen size. When the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, when the user is using a smartwatch, the display unit can provide a simple and highly visible display method. This makes it possible to provide a system that is easy for users to use by providing an optimal display method according to the user's device usage status.

[0094] The generation unit can estimate the user's emotions and adjust the content of the advice based on the estimated user's emotions. For example, if the user is nervous, the generation unit can suggest a drink that has a relaxing effect. Also, if the user is relaxed, the generation unit can suggest a drink that will increase enjoyment. Furthermore, if the user is in a hurry, the generation unit can suggest a drink that can be served quickly. In this way, it is possible to provide optimal advice according to the user's emotions.

[0095] The display unit can refer to the user's past selection history and highlight related information. For example, it can highlight detailed information about drinks the user has previously selected and the reasons why they are recommended. It can also display new information related to drinks the user has previously selected. Furthermore, it can suggest drinks that suit the user's preferences based on the user's past selection history and highlight that information. This makes it possible to provide optimal information based on the user's past selection history.

[0096] The reception unit can estimate the user's emotions and adjust the method of receiving questions based on the estimated user's emotions. For example, if the user is nervous, the reception unit changes the method of receiving questions to help the user relax. Also, if the user is relaxed, the reception unit can receive detailed questions. Furthermore, if the user is in a hurry, the reception unit can quickly receive questions. This makes it possible to provide an optimal method of receiving questions according to the user's emotions.

[0097] The generation unit can analyze the user's past selection history and provide advice based on the user's preferences. For example, based on information about drinks the user has selected in the past, the generation unit can suggest related drinks. The generation unit can also suggest drinks related to specific time periods or events based on the user's past selection history. Furthermore, based on the user's past selection history, the generation unit can suggest drinks with the user's preferred tastes and aromas. This makes it possible to provide optimal advice based on the user's past selection history.

[0098] The display unit can estimate the user's emotions and adjust the display content based on the estimated user emotions. For example, if the user is nervous, the display unit can provide a simple, highly visible display method. If the user is relaxed, the display unit can provide a display method that includes detailed information. If the user is in a hurry, the display unit can provide a display method that focuses on the main points. This makes it possible to provide optimal display content according to the user's emotions.

[0099] The reception unit can prioritize receiving highly relevant questions based on the user's geographical location information. For example, if the user is in a specific area, questions about drinks related to that area can be prioritized. Also, if the user is traveling, questions about drinks at the travel destination can be prioritized. Furthermore, if the user is participating in a specific event, questions about drinks related to the event can be prioritized. In this way, by prioritizing highly relevant questions in consideration of the user's geographical location information, more appropriate questions can be received.

[0100] The generation unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is nervous, the generation unit can provide advice in gentle language. If the user is relaxed, the generation unit can provide advice that includes detailed information. If the user is in a hurry, the generation unit can provide concise and quick advice. In this way, by adjusting the way in which advice is expressed according to the user's emotions, more appropriate advice can be provided.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives a question from a user. Examples of user questions include, but are not limited to, "What red wine do you recommend?" or "What white wine goes well with fish dishes?" The reception unit can input a question using, for example, a touch screen on a tablet. The reception unit can also receive voice input. For example, if a user asks by voice, "What red wine do you recommend?", the question can be converted into text data using voice recognition technology. Step 2: The generation unit uses a generation AI to analyze the question received by the reception unit and generate advice. The generation unit generates advice based on information such as the type of wine, origin, flavor, and price range. The generation AI uses a text generation AI (e.g., LLM) to suggest the best drink for the user's question. The generation unit can also use a multimodal generation AI to generate advice that includes detailed information about the drink and the reason for the recommendation. For example, in response to the question, "What red wine do you recommend?", the generation AI provides specific advice such as, "I recommend a full-bodied Cabernet Sauvignon." The generation unit can also use a generation AI to generate advice that includes detailed information about the drink and the reason for the recommendation. For example, the generation unit provides advice that includes detailed information about the drink and the reason for the recommendation, such as, "This wine is characterized by aromas of blackberry and cassis and is perfect with steak and grilled dishes." Step 3: The display unit displays the advice generated by the generation unit. The display unit can display the advice on a tablet screen, for example. The display unit can also provide the advice by voice. For example, the advice generated by the generation AI can be output by voice using voice synthesis technology.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0117] In the 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.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

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

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

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

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; a generation unit that analyzes the question received by the reception unit and generates advice; a display unit that displays the advice generated by the generation unit. A system characterized by:

2. A voice input receiving unit is provided.

2. The system of claim 1.

3. Equipped with a voice output unit that provides advice by voice 2. The system of claim 1.

4. The generation unit Generate advice based on wine type, origin, taste, and price range 2. The system of claim 1.

5. The display unit View details about a drink or why it's recommended 2. The system of claim 1.

6. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past question history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit When receiving a question, filtering is performed based on the user's current eating and drinking habits and preferences.

2. The system of claim 1.

9. The reception unit Estimate the user's emotions and prioritize the questions to be accepted based on the estimated user emotions.

2. The system of claim 1.

10. The reception unit When accepting questions, prioritize relevant questions based on the user's geographic location.

2. The system of claim 1.

Images