System

Abstract

An object of a system according to an embodiment is to analyze a conversation history in a group chat, collect necessary information, and complete a reservation.SOLUTION: A system includes a conversation history analysis unit, a hearing unit, a confirmation unit, and a reservation unit. The conversation history analysis unit analyzes a conversation history in a group chat. The hearing unit hears the user about the insufficient information on the basis of the information analyzed by the conversation history analysis unit. The confirmation unit performs final confirmation based on the information obtained by the hearing unit. The reservation unit completes the reservation based on the information confirmed by the confirmation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when making a restaurant reservation within a group chat, the process of collecting the necessary information and completing the reservation was complicated.

[0005] The system according to the embodiment aims to analyze the conversation history in the group chat, collect necessary information, and complete the reservation. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation history analysis unit, a hearing unit, a confirmation unit, and a reservation unit. The conversation history analysis unit analyzes the conversation history in the group chat. The hearing unit asks users for missing information based on the information analyzed by the conversation history analysis unit. The confirmation unit performs final confirmation based on the information obtained by the hearing unit. The reservation unit completes the reservation based on the information confirmed by the confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the conversation history in the group chat, collect necessary information, and complete the reservation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The restaurant reservation system according to an embodiment of the present invention analyzes conversation history within a LINE (registered trademark) group chat, uses a generation AI to understand the requirements for a drinking party, and then automatically completes the reservation process by asking the user for any missing information and making final confirmations. As a result, the restaurant reservation system provides users with the convenience of making various adjustments and making reservations on their behalf, and can generate customer traffic for restaurants.

[0029] A restaurant reservation system according to an embodiment includes a conversation history analysis unit, a hearing unit, a confirmation unit, and a reservation unit. The conversation history analysis unit analyzes conversation history in a LINE (registered trademark) group chat. For example, the conversation history analysis unit analyzes text messages to extract the date, time, and location of a drinking party. The conversation history analysis unit can also analyze voice messages to understand the requirements for the drinking party. The conversation history analysis unit can also analyze messages with images to understand the theme and atmosphere of the drinking party. For example, the conversation history analysis unit uses natural language processing technology to analyze the content of text messages and extract the date, time, and location of the drinking party. Voice messages are converted into text using speech recognition technology, and the content is analyzed. For messages with images, image analysis technology is used to extract information from the images and understand the theme and atmosphere of the drinking party. The hearing unit hears from a user about missing information based on the information analyzed by the conversation history analysis unit. For example, if the date and time of the drinking party have not been decided, the hearing unit asks the user, "When would you like to hold the drinking party?" Furthermore, if the location of the drinking party has not been decided, the hearing unit can ask the user, "In which area do you want to hold the drinking party?" Furthermore, if the number of participants has not been decided, the hearing unit can ask the user, "How many participants will there be?" For example, the hearing unit presents options to the user and provides an interface that allows the user to easily enter missing information. The confirmation unit performs final confirmation based on the information obtained by the hearing unit. For example, the confirmation unit may ask the user, "Is it okay to proceed with making a reservation at a yakiniku restaurant in Shinjuku at 7 p.m. on Friday?" The confirmation unit may also ask the user, "Is the number of participants five?" The confirmation unit may also ask the user, "Is yakiniku the theme of the drinking party?" For example, the confirmation unit may refer to the user's past reservation history and suggest the optimal option. The reservation unit completes the reservation based on the information confirmed by the confirmation unit. For example, the reservation unit may access the restaurant's reservation system and complete the reservation. The reservation unit can also notify the user that the reservation has been completed.The reservation unit can also automatically set a reminder and notify the user after the reservation is completed. For example, the reservation unit accesses a restaurant's reservation system and completes the reservation. Once the reservation is completed, the reservation unit sends the user a notification that the reservation is complete. The reservation unit sets a reminder after the reservation is completed and notifies the user the day before the drinking party. In this way, the restaurant reservation system according to the embodiment can reduce the user's effort and automatically complete the drinking party reservation. For example, a user can easily make a drinking party reservation through a conversation in a LINE (registered trademark) group chat. Restaurants can enjoy the customer referral effect of reservations generated by the generation AI.

[0030] The conversation history analysis unit can learn the user's past drinking party history or preferences and make personalized suggestions. For example, the generation AI in the conversation history analysis unit learns the user's past drinking party history and understands preferences and patterns. For example, it records the restaurants visited in the past and the menu items ordered, and reflects this in the next suggestion. To learn the user's preferences, the generation AI analyzes the user's past conversation history and extracts preferences for specific restaurants and cuisines. For example, it can suggest sushi restaurants based on a statement such as "I like sushi." The conversation history analysis unit can also suggest restaurants that are popular on specific days of the week or at specific times of the day based on the user's past drinking party history. For example, it can make similar suggestions based on data on restaurants visited on Friday nights. This enables suggestions tailored to the user's preferences.

[0031] The conversation history analysis unit uses natural language processing technology to analyze the tone or nuance of the conversation and select an appropriate restaurant. For example, the generation AI in the conversation history analysis unit uses natural language processing technology to analyze the tone of the conversation and select a restaurant that matches the user's mood. For example, a cafe may be suggested for a conversation with a relaxed tone. The conversation history analysis unit also analyzes the nuances of the conversation, allowing the generation AI to understand the context and select an appropriate restaurant. For example, a high-end restaurant may be suggested based on a statement such as "Today is a special day." The conversation history analysis unit also analyzes the tone and nuance of the conversation and selects a restaurant that matches the user's mood. For example, a lively izakaya may be suggested for a conversation with a lively tone. This makes it possible to select a restaurant that matches the user's mood.

[0032] The conversation history analysis unit can also analyze conversation histories from other messaging apps to understand the necessary requirements. For example, the generation AI can analyze conversation histories from messaging apps other than LINE (registered trademark) (e.g., WhatsApp and Facebook (registered trademark) Messenger) to understand the necessary requirements. For example, information can be integrated from multiple apps. Furthermore, in order to analyze conversation histories from other messaging apps, the generation AI can obtain data using an API to understand the requirements for a drinking party. For example, the conversation history analysis unit can analyze WhatsApp conversation history. Furthermore, the generation AI can support multiple messaging apps and analyze the conversation history to understand the necessary requirements. For example, the generation AI can determine the date and time of a drinking party based on the conversation history from Facebook (registered trademark) Messenger. This makes it possible to support multiple messaging apps and understand the necessary requirements.

[0033] The conversation history analysis unit can link the analysis results of the conversation history with the user's calendar app and automatically adjust the schedule. For example, the generation AI in the conversation history analysis unit links the analysis results of the conversation history with the user's calendar app and automatically adjusts the schedule. For example, the date and time of a drinking party can be automatically added to the calendar. Furthermore, the generation AI in the conversation history analysis unit links the analysis results of the conversation history with the user's calendar app and adjusts the schedule. For example, the date and time can be changed to avoid overlapping appointments. Furthermore, the generation AI in the conversation history analysis unit links the analysis results of the conversation history with the calendar app and automatically adjusts the schedule. For example, the date and time of a drinking party can be added to the calendar and a reminder can be set. This makes it possible to automatically adjust the user's schedule.

[0034] The hearing unit presents options to make it easier for the user to respond and can provide an interface that allows the user to easily enter missing information. For example, the generation AI in the hearing unit presents options to the user and provides an interface that allows the user to easily enter missing information. For example, options such as "Please select a date and time" can be displayed. The hearing unit also presents options to make it easier for the user to respond and provides an interface. For example, options such as "Please select an area" can be displayed. The hearing unit also presents options to the user and provides an interface that allows the user to easily enter missing information. For example, options such as "Please select the number of participants" can be displayed. This allows the user to easily enter missing information.

[0035] The hearing unit can add a function to learn the user's past response history and automatically complete missing information. For example, the hearing unit adds a function in which the generation AI learns the user's past response history and automatically completes missing information. For example, it automatically suggests a date, time, and location based on past responses. In addition, the hearing unit analyzes data to learn the user's past response history and automatically completes missing information. For example, it can predict the number of participants based on past responses. In addition, the hearing unit adds a function in which the generation AI learns the user's past response history and automatically completes missing information. For example, it can automatically suggest types of restaurants based on past responses. This makes it possible to automatically complete missing information based on the user's past response history.

[0036] The hearing unit can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can ask, "Please tell me the date and time." The hearing unit also provides an interface that supports voice input so that the user can respond by voice. For example, the generation AI can ask, "Please tell me the area." The generation AI can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can ask, "Please tell me the number of participants." This allows the user to respond by voice to the missing information.

[0037] The hearing unit allows the generation AI to translate in real time when a user answers a question, making it possible to accommodate users of different languages. For example, the hearing unit allows the generation AI to translate the user's answer in real time, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in Japanese in English. Furthermore, the hearing unit allows the generation AI to translate in real time when a user answers a question in English, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in English in Spanish. Furthermore, the hearing unit allows the generation AI to translate in real time, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in French in German. This makes it possible to accommodate users of different languages.

[0038] The confirmation unit can refer to the user's past reservation history and suggest the optimal option. In the confirmation unit, for example, the generation AI refers to the user's past reservation history and suggests the optimal option. For example, similar suggestions are made based on data on restaurants visited in the past. In addition, the confirmation unit refers to the user's past reservation history, so the generation AI analyzes the data and suggests the optimal option. For example, it can suggest a date, time, and location based on the past reservation history. In addition, the confirmation unit can refer to the user's past reservation history and suggest the optimal option. For example, it can re-suggest restaurants that were well-received in the past. This makes it possible to suggest the optimal option based on the user's past reservation history.

[0039] The confirmation unit can add a function to automatically set a reminder after a reservation is completed and notify the user. For example, the generation AI can send a reminder the day before a drinking party. The confirmation unit can also add a function to automatically set a reminder after a reservation is completed and notify the user. For example, a reminder can be sent on the day of the drinking party. The confirmation unit can also add a function to automatically set a reminder after a reservation is completed and notify the user. For example, a reminder can be sent one hour before a drinking party. This allows the user to be automatically notified of the reminder.

[0040] The confirmation unit can also link the final confirmation and completion of the reservation with other reservation platforms. For example, the generation AI in the confirmation unit can link the final confirmation and completion of the reservation with other reservation platforms (e.g., OpenTable or Gurunavi). For example, it can select the optimal reservation from multiple platforms. In addition, to link with other reservation platforms, the generation AI in the confirmation unit can obtain data using an API and perform the final confirmation and completion of the reservation. For example, it can use OpenTable data. In addition, the generation AI in the confirmation unit can link the final confirmation and completion of the reservation with other reservation platforms. For example, it can make a reservation based on Gurunavi data. This allows reservations to be completed in collaboration with multiple reservation platforms.

[0041] The confirmation unit can add a function to automatically reserve transportation or provide a map after a reservation is completed. For example, the confirmation unit adds a function to automatically reserve transportation after the generation AI has completed a reservation. For example, it can automatically reserve a taxi. The confirmation unit also adds a function to automatically provide a map after a reservation has been completed. For example, it can display directions to a restaurant on a map. The confirmation unit also adds a function to automatically reserve transportation or provide a map after the generation AI has completed a reservation. For example, it can provide a public transportation timetable. This allows transportation to be reserved or a map to be provided to the user automatically.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The conversation history analysis unit can learn a user's past drinking party history or preferences and make personalized suggestions. For example, it can record restaurants visited in the past and menu items ordered, and reflect these in suggestions for the next time. In addition, to learn the user's preferences, the generation AI analyzes past conversation history and extracts preferences for specific restaurants and cuisines. For example, it can suggest sushi restaurants based on a statement such as "I like sushi." The conversation history analysis unit can also suggest restaurants that are popular on specific days of the week or at specific times of the day based on the user's past drinking party history. For example, it can make similar suggestions based on data on restaurants visited on Friday nights. This makes it possible to make suggestions tailored to the user's preferences.

[0044] The conversation history analysis unit uses natural language processing technology to analyze the tone or nuance of the conversation and select an appropriate restaurant. For example, if the conversation has a relaxed tone, it will suggest a cafe. In addition, the conversation history analysis unit analyzes the nuances of the conversation, so the generation AI understands the context and selects an appropriate restaurant. For example, it can suggest a high-end restaurant based on a statement such as "Today is a special day." In addition, the conversation history analysis unit analyzes the tone and nuance of the conversation and selects a restaurant that matches the user's mood. For example, it can suggest a lively izakaya if the conversation has a lively tone. This makes it possible to select a restaurant that matches the user's mood.

[0045] The conversation history analysis unit can also analyze conversation histories from other messaging apps to understand the necessary requirements. For example, the generation AI can analyze conversation histories from messaging apps other than LINE (registered trademark) (e.g., WhatsApp and Facebook (registered trademark) Messenger) to understand the necessary requirements. For example, information from multiple apps can be integrated. Furthermore, to analyze conversation histories from other messaging apps, the generation AI acquires data using an API to understand the requirements for a drinking party. For example, the conversation history analysis unit can analyze WhatsApp conversation history. Furthermore, the conversation history analysis unit supports multiple messaging apps, analyzes conversation histories, and understands the necessary requirements. For example, the generation AI can determine the date and time of a drinking party based on the conversation history from Facebook (registered trademark) Messenger. This allows the generation AI to support multiple messaging apps and understand the necessary requirements.

[0046] The conversation history analysis unit can link the analysis results of the conversation history with the user's calendar app and automatically adjust the schedule. For example, the generation AI can link the analysis results of the conversation history with the user's calendar app and automatically adjust the schedule. For example, the date and time of a drinking party can be automatically added to the calendar. Furthermore, based on the analysis results of the conversation history, the generation AI can link with the user's calendar app and adjust the schedule. For example, the date and time can be changed to avoid overlapping appointments. Furthermore, the conversation history analysis unit can link the analysis results of the conversation history with the calendar app and automatically adjust the schedule. For example, the date and time of a drinking party can be added to the calendar and a reminder can be set. This makes it possible to automatically adjust the user's schedule.

[0047] The hearing unit presents options to the user to make it easier for them to respond, and can provide an interface that allows them to easily enter missing information. For example, the generation AI presents options to the user and provides an interface that allows them to easily enter missing information. For example, options such as "Please select a date and time" can be displayed. The hearing unit also presents options to the user to make it easier for them to respond, and provides an interface. For example, options such as "Please select an area" can be displayed. The hearing unit also presents options to the user to make it easier for them to enter missing information. For example, options such as "Please select the number of participants" can be displayed. This allows the user to easily enter missing information.

[0048] The hearing unit can add a function that learns the user's past response history and automatically completes missing information. For example, the generation AI can learn the user's past response history and add a function that automatically completes missing information. For example, it can automatically suggest a date, time, and location based on past responses. In addition, the hearing unit analyzes the data to learn the user's past response history and automatically completes missing information. For example, it can predict the number of participants based on past responses. In addition, the hearing unit adds a function that learns the user's past response history and automatically completes missing information. For example, it can automatically suggest types of restaurants based on past responses. This makes it possible to automatically complete missing information based on the user's past response history.

[0049] The hearing unit can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can ask, "Please tell me the date and time" by voice. The hearing unit also provides an interface that supports voice input so that the user can respond by voice. For example, the generation AI can ask, "Please tell me the area" by voice. The generation AI can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can ask, "Please tell me the number of participants" by voice. This allows the user to respond by voice to the missing information.

[0050] When a user answers a question in a hearing, the generation AI translates in real time, making it possible to accommodate users of different languages. For example, the generation AI translates the user's answer in real time, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in Japanese in English. Furthermore, when a user answers a question in a hearing, the generation AI translates in real time, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in English in Spanish. Furthermore, the hearing unit translates in real time, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in French in German. This makes it possible to accommodate users of different languages.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The conversation history analysis unit analyzes the conversation history in the LINE (registered trademark) group chat. For example, it analyzes text messages to extract the date, time, and location of the drinking party. It can also analyze voice messages to understand the requirements for the drinking party. It can also analyze messages with images to understand the theme and atmosphere of the drinking party. This is done using natural language processing technology, voice recognition technology, and image analysis technology. Step 2: The hearing unit hears the missing information from the user based on the information analyzed by the conversation history analysis unit. For example, if the date and time of the drinking party have not been decided, the hearing unit can ask the user, "When would you like to hold the drinking party?". If the location of the drinking party has not been decided, the hearing unit can also ask the user, "In what area would you like to hold the drinking party?". Furthermore, if the number of participants has not been decided, the hearing unit can also ask the user, "How many participants will there be?". The hearing unit presents the user with options and provides an interface that allows them to easily enter the missing information. Step 3: The confirmation section performs final confirmation based on the information obtained by the hearing section. For example, the confirmation section asks the user, "Is it okay to proceed with making a reservation at a yakiniku restaurant in Shinjuku at 7pm on Friday night?". The confirmation section can also ask the user for confirmation, such as, "Is it okay for there to be five participants?" or "Is it okay for the theme of the drinking party to be yakiniku?". The confirmation section may also refer to the user's past reservation history and suggest the best option. Step 4: The reservation unit completes the reservation based on the information confirmed by the confirmation unit. For example, it accesses the restaurant's reservation system and completes the reservation. Once the reservation is complete, it sends a notification of the reservation completion to the user. Furthermore, after the reservation is completed, it sets a reminder and notifies the user the day before the drinking party.

[0053] (Example 2) The restaurant reservation system according to an embodiment of the present invention analyzes conversation history within a LINE (registered trademark) group chat, uses a generation AI to understand the requirements for a drinking party, and then automatically completes the reservation process by asking the user for any missing information and making final confirmations. As a result, the restaurant reservation system provides users with the convenience of making various adjustments and making reservations on their behalf, and can generate customer traffic for restaurants.

[0054] A restaurant reservation system according to an embodiment includes a conversation history analysis unit, a hearing unit, a confirmation unit, and a reservation unit. The conversation history analysis unit analyzes conversation history in a LINE (registered trademark) group chat. For example, the conversation history analysis unit analyzes text messages to extract the date, time, and location of a drinking party. The conversation history analysis unit can also analyze voice messages to understand the requirements for the drinking party. The conversation history analysis unit can also analyze messages with images to understand the theme and atmosphere of the drinking party. For example, the conversation history analysis unit uses natural language processing technology to analyze the content of text messages and extract the date, time, and location of the drinking party. Voice messages are converted into text using speech recognition technology, and the content is analyzed. For messages with images, image analysis technology is used to extract information from the images and understand the theme and atmosphere of the drinking party. The hearing unit hears from a user about missing information based on the information analyzed by the conversation history analysis unit. For example, if the date and time of the drinking party have not been decided, the hearing unit asks the user, "When would you like to hold the drinking party?" Furthermore, if the location of the drinking party has not been decided, the hearing unit can ask the user, "In which area do you want to hold the drinking party?" Furthermore, if the number of participants has not been decided, the hearing unit can ask the user, "How many participants will there be?" For example, the hearing unit presents options to the user and provides an interface that allows the user to easily enter missing information. The confirmation unit performs final confirmation based on the information obtained by the hearing unit. For example, the confirmation unit may ask the user, "Is it okay to proceed with making a reservation at a yakiniku restaurant in Shinjuku at 7 p.m. on Friday?" The confirmation unit may also ask the user, "Is the number of participants five?" The confirmation unit may also ask the user, "Is yakiniku the theme of the drinking party?" For example, the confirmation unit may refer to the user's past reservation history and suggest the optimal option. The reservation unit completes the reservation based on the information confirmed by the confirmation unit. For example, the reservation unit may access the restaurant's reservation system and complete the reservation. The reservation unit can also notify the user that the reservation has been completed.The reservation unit can also automatically set a reminder and notify the user after the reservation is completed. For example, the reservation unit accesses a restaurant's reservation system and completes the reservation. Once the reservation is completed, the reservation unit sends the user a notification that the reservation is complete. The reservation unit sets a reminder after the reservation is completed and notifies the user the day before the drinking party. In this way, the restaurant reservation system according to the embodiment can reduce the user's effort and automatically complete the drinking party reservation. For example, a user can easily make a drinking party reservation through a conversation in a LINE (registered trademark) group chat. Restaurants can enjoy the customer referral effect of reservations generated by the generation AI.

[0055] The conversation history analysis unit can learn the user's past drinking party history or preferences and make personalized suggestions. For example, the generation AI in the conversation history analysis unit learns the user's past drinking party history and understands preferences and patterns. For example, it records the restaurants visited in the past and the menu items ordered, and reflects this in the next suggestion. To learn the user's preferences, the generation AI analyzes the user's past conversation history and extracts preferences for specific restaurants and cuisines. For example, it can suggest sushi restaurants based on a statement such as "I like sushi." The conversation history analysis unit can also suggest restaurants that are popular on specific days of the week or at specific times of the day based on the user's past drinking party history. For example, it can make similar suggestions based on data on restaurants visited on Friday nights. This enables suggestions tailored to the user's preferences.

[0056] The conversation history analysis unit uses natural language processing technology to analyze the tone or nuance of the conversation and select an appropriate restaurant. For example, the generation AI in the conversation history analysis unit uses natural language processing technology to analyze the tone of the conversation and select a restaurant that matches the user's mood. For example, a cafe may be suggested for a conversation with a relaxed tone. The conversation history analysis unit also analyzes the nuances of the conversation, allowing the generation AI to understand the context and select an appropriate restaurant. For example, a high-end restaurant may be suggested based on a statement such as "Today is a special day." The conversation history analysis unit also analyzes the tone and nuance of the conversation and selects a restaurant that matches the user's mood. For example, a lively izakaya may be suggested for a conversation with a lively tone. This makes it possible to select a restaurant that matches the user's mood.

[0057] The conversation history analysis unit can use the emotion estimation function to analyze the user's emotions during a conversation and prioritize selecting dates, times, or places where positive emotions are prevalent. The conversation history analysis unit, for example, uses the emotion estimation function to analyze the user's emotions during a conversation and select dates, times, or places where positive emotions are prevalent. For example, it can suggest places that have fond memories from the past. The conversation history analysis unit also uses the generation AI to analyze emotions during a conversation in real time and prioritize selecting dates, times, or places where positive emotions are prevalent. For example, it can suggest places that make the user feel happy. The conversation history analysis unit can also use the emotion estimation function to analyze the user's emotions during a conversation and select dates, times, or places where positive emotions are prevalent. For example, it can make suggestions based on dates and times when positive emotions were prevalent in the past. This makes it possible to select dates, times, and places that will elicit positive emotions from the user.

[0058] The conversation history analysis unit can also analyze conversation histories from other messaging apps to understand the necessary requirements. For example, the generation AI can analyze conversation histories from messaging apps other than LINE (registered trademark) (e.g., WhatsApp and Facebook (registered trademark) Messenger) to understand the necessary requirements. For example, information can be integrated from multiple apps. Furthermore, in order to analyze conversation histories from other messaging apps, the generation AI can obtain data using an API to understand the requirements for a drinking party. For example, the conversation history analysis unit can analyze WhatsApp conversation history. Furthermore, the generation AI can support multiple messaging apps and analyze the conversation history to understand the necessary requirements. For example, the generation AI can determine the date and time of a drinking party based on the conversation history from Facebook (registered trademark) Messenger. This makes it possible to support multiple messaging apps and understand the necessary requirements.

[0059] The conversation history analysis unit can link the analysis results of the conversation history with the user's calendar app and automatically adjust the schedule. For example, the generation AI in the conversation history analysis unit links the analysis results of the conversation history with the user's calendar app and automatically adjusts the schedule. For example, the date and time of a drinking party can be automatically added to the calendar. Furthermore, the generation AI in the conversation history analysis unit links the analysis results of the conversation history with the user's calendar app and adjusts the schedule. For example, the date and time can be changed to avoid overlapping appointments. Furthermore, the generation AI in the conversation history analysis unit links the analysis results of the conversation history with the calendar app and automatically adjusts the schedule. For example, the date and time of a drinking party can be added to the calendar and a reminder can be set. This makes it possible to automatically adjust the user's schedule.

[0060] The conversation history analysis unit can use the emotion estimation function to analyze the user's emotions during a conversation in real time and make suggestions that will elicit positive emotions. The conversation history analysis unit can, for example, use the emotion estimation function to analyze the user's emotions during a conversation in real time and make suggestions that will elicit positive emotions. For example, it can suggest restaurants that will make the user happy. The conversation history analysis unit also uses the generation AI to analyze the emotions during a conversation in real time and make suggestions that will elicit positive emotions. For example, it can suggest places where the user can relax. The conversation history analysis unit can also use the emotion estimation function to analyze the user's emotions during a conversation in real time and make suggestions that will elicit positive emotions. For example, it can suggest places that have fond memories from the past. This makes it possible to make suggestions that will elicit positive emotions from the user.

[0061] The hearing unit presents options to make it easier for the user to respond and can provide an interface that allows the user to easily enter missing information. For example, the generation AI in the hearing unit presents options to the user and provides an interface that allows the user to easily enter missing information. For example, options such as "Please select a date and time" can be displayed. The hearing unit also presents options to make it easier for the user to respond and provides an interface. For example, options such as "Please select an area" can be displayed. The hearing unit also presents options to the user and provides an interface that allows the user to easily enter missing information. For example, options such as "Please select the number of participants" can be displayed. This allows the user to easily enter missing information.

[0062] The hearing unit can add a function to learn the user's past response history and automatically complete missing information. For example, the hearing unit adds a function in which the generation AI learns the user's past response history and automatically completes missing information. For example, it automatically suggests a date, time, and location based on past responses. In addition, the hearing unit analyzes data to learn the user's past response history and automatically completes missing information. For example, it can predict the number of participants based on past responses. In addition, the hearing unit adds a function in which the generation AI learns the user's past response history and automatically completes missing information. For example, it can automatically suggest types of restaurants based on past responses. This makes it possible to automatically complete missing information based on the user's past response history.

[0063] The hearing unit can use the emotion estimation function to analyze the emotions the user feels when answering questions and adjust the order or content of questions so that the user does not feel stressed. For example, the hearing unit can use the emotion estimation function to analyze the emotions the user feels when answering questions and adjust the order or content of questions so that the user does not feel stressed. For example, the questions can be arranged so that the user can answer in a relaxed state. The generation AI in the hearing unit can also use the emotion estimation function to analyze the user's emotions and adjust the order or content of questions so that the user does not feel stressed. For example, the generation AI can start with simple questions. The hearing unit can also use the emotion estimation function to analyze the emotions the user feels when answering questions and adjust the order or content of questions so that the user does not feel stressed. For example, the generation AI can prioritize questions that elicit positive emotions. This allows the user to answer without feeling stressed.

[0064] The hearing unit can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can ask, "Please tell me the date and time." The hearing unit also provides an interface that supports voice input so that the user can respond by voice. For example, the generation AI can ask, "Please tell me the area." The generation AI can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can ask, "Please tell me the number of participants." This allows the user to respond by voice to the missing information.

[0065] The hearing unit allows the generation AI to translate in real time when a user answers a question, making it possible to accommodate users of different languages. For example, the hearing unit allows the generation AI to translate the user's answer in real time, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in Japanese in English. Furthermore, the hearing unit allows the generation AI to translate in real time when a user answers a question in English, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in English in Spanish. Furthermore, the hearing unit allows the generation AI to translate in real time, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in French in German. This makes it possible to accommodate users of different languages.

[0066] The hearing unit uses the emotion estimation function to analyze the emotions of the user when answering a question in real time and make suggestions that will elicit positive emotions. For example, the hearing unit uses the emotion estimation function to analyze the emotions of the user when answering a question in real time and make suggestions that will elicit positive emotions. For example, it asks questions that will help the user relax. Furthermore, the hearing unit uses the emotion estimation function to analyze the emotions of the user when answering a question in real time and make suggestions that will elicit positive emotions. For example, it can ask questions that will make the user feel happy. Furthermore, the hearing unit uses the emotion estimation function to analyze the emotions of the user when answering a question in real time and make suggestions that will elicit positive emotions. For example, it can ask questions that will make the user feel at ease. This makes it possible to make suggestions that will elicit positive emotions from the user.

[0067] The confirmation unit can refer to the user's past reservation history and suggest the optimal option. In the confirmation unit, for example, the generation AI refers to the user's past reservation history and suggests the optimal option. For example, similar suggestions are made based on data on restaurants visited in the past. In addition, the confirmation unit refers to the user's past reservation history, so the generation AI analyzes the data and suggests the optimal option. For example, it can suggest a date, time, and location based on the past reservation history. In addition, the confirmation unit can refer to the user's past reservation history and suggest the optimal option. For example, it can re-suggest restaurants that were well-received in the past. This makes it possible to suggest the optimal option based on the user's past reservation history.

[0068] The confirmation unit can add a function to automatically set a reminder after a reservation is completed and notify the user. For example, the generation AI can send a reminder the day before a drinking party. The confirmation unit can also add a function to automatically set a reminder after a reservation is completed and notify the user. For example, a reminder can be sent on the day of the drinking party. The confirmation unit can also add a function to automatically set a reminder after a reservation is completed and notify the user. For example, a reminder can be sent one hour before a drinking party. This allows the user to be automatically notified of the reminder.

[0069] The confirmation unit can use the emotion estimation function to analyze the user's emotions at the time of final confirmation and make suggestions that elicit positive emotions. The confirmation unit, for example, uses the emotion estimation function to analyze the user's emotions at the time of final confirmation and make suggestions that elicit positive emotions. For example, it can make suggestions that make the user feel happy. Furthermore, the confirmation unit uses the emotion estimation function of the generation AI to analyze the user's emotions at the time of final confirmation and make suggestions that elicit positive emotions. For example, it can make suggestions that make the user feel at ease. Furthermore, the confirmation unit uses the emotion estimation function to analyze the user's emotions at the time of final confirmation and make suggestions that elicit positive emotions. For example, it can make suggestions that make the user feel relaxed. This makes it possible to make suggestions that elicit positive emotions from the user.

[0070] The confirmation unit can also link the final confirmation and completion of the reservation with other reservation platforms. For example, the generation AI in the confirmation unit can link the final confirmation and completion of the reservation with other reservation platforms (e.g., OpenTable or Gurunavi). For example, it can select the optimal reservation from multiple platforms. In addition, to link with other reservation platforms, the generation AI in the confirmation unit can obtain data using an API and perform the final confirmation and completion of the reservation. For example, it can use OpenTable data. In addition, the generation AI in the confirmation unit can link the final confirmation and completion of the reservation with other reservation platforms. For example, it can make a reservation based on Gurunavi data. This allows reservations to be completed in collaboration with multiple reservation platforms.

[0071] The confirmation unit can add a function to automatically reserve transportation or provide a map after a reservation is completed. For example, the confirmation unit adds a function to automatically reserve transportation after the generation AI has completed a reservation. For example, it can automatically reserve a taxi. The confirmation unit also adds a function to automatically provide a map after a reservation has been completed. For example, it can display directions to a restaurant on a map. The confirmation unit also adds a function to automatically reserve transportation or provide a map after the generation AI has completed a reservation. For example, it can provide a public transportation timetable. This allows transportation to be reserved or a map to be provided to the user automatically.

[0072] The confirmation unit can use the emotion estimation function to analyze the user's emotions after the reservation is completed and make suggestions that elicit positive emotions. For example, the confirmation unit can use the emotion estimation function to analyze the user's emotions after the reservation is completed and make suggestions that elicit positive emotions. For example, it can make suggestions that make the user feel happy. Furthermore, the generation AI in the confirmation unit can use the emotion estimation function to analyze the user's emotions after the reservation is completed and make suggestions that elicit positive emotions. For example, it can make suggestions that make the user feel at ease. Furthermore, the confirmation unit can use the emotion estimation function to analyze the user's emotions after the reservation is completed and make suggestions that elicit positive emotions. For example, it can make suggestions that make the user feel relaxed. This makes it possible to make suggestions that elicit positive emotions from the user.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The conversation history analysis unit can learn a user's past drinking party history or preferences and make personalized suggestions. For example, it can record restaurants visited in the past and menu items ordered, and reflect these in suggestions for the next time. In addition, to learn the user's preferences, the generation AI analyzes past conversation history and extracts preferences for specific restaurants and cuisines. For example, it can suggest sushi restaurants based on a statement such as "I like sushi." The conversation history analysis unit can also suggest restaurants that are popular on specific days of the week or at specific times of the day based on the user's past drinking party history. For example, it can make similar suggestions based on data on restaurants visited on Friday nights. This makes it possible to make suggestions tailored to the user's preferences.

[0075] The conversation history analysis unit uses natural language processing technology to analyze the tone or nuance of the conversation and select an appropriate restaurant. For example, if the conversation has a relaxed tone, it will suggest a cafe. In addition, the conversation history analysis unit analyzes the nuances of the conversation, so the generation AI understands the context and selects an appropriate restaurant. For example, it can suggest a high-end restaurant based on a statement such as "Today is a special day." In addition, the conversation history analysis unit analyzes the tone and nuance of the conversation and selects a restaurant that matches the user's mood. For example, it can suggest a lively izakaya if the conversation has a lively tone. This makes it possible to select a restaurant that matches the user's mood.

[0076] The conversation history analysis unit uses the emotion estimation function to analyze the user's emotions during a conversation and prioritizes the selection of dates, times, and places where positive emotions are prevalent. For example, it can suggest places that have fond memories from the past. The conversation history analysis unit also uses the generation AI to analyze emotions during a conversation in real time and prioritize the selection of dates, times, and places where positive emotions are prevalent. For example, it can suggest places that make the user feel happy. The conversation history analysis unit also uses the emotion estimation function to analyze the user's emotions during a conversation and prioritizes the selection of dates, times, and places where positive emotions are prevalent. For example, it can make suggestions based on dates and times when positive emotions were prevalent in the past. This makes it possible to select dates, times, and places that will elicit positive emotions from the user.

[0077] The conversation history analysis unit can also analyze conversation histories from other messaging apps to understand the necessary requirements. For example, the generation AI can analyze conversation histories from messaging apps other than LINE (registered trademark) (e.g., WhatsApp and Facebook (registered trademark) Messenger) to understand the necessary requirements. For example, information from multiple apps can be integrated. Furthermore, to analyze conversation histories from other messaging apps, the generation AI acquires data using an API to understand the requirements for a drinking party. For example, the conversation history analysis unit can analyze WhatsApp conversation history. Furthermore, the conversation history analysis unit supports multiple messaging apps, analyzes conversation histories, and understands the necessary requirements. For example, the generation AI can determine the date and time of a drinking party based on the conversation history from Facebook (registered trademark) Messenger. This allows the generation AI to support multiple messaging apps and understand the necessary requirements.

[0078] The conversation history analysis unit can link the analysis results of the conversation history with the user's calendar app and automatically adjust the schedule. For example, the generation AI can link the analysis results of the conversation history with the user's calendar app and automatically adjust the schedule. For example, the date and time of a drinking party can be automatically added to the calendar. Furthermore, based on the analysis results of the conversation history, the generation AI can link with the user's calendar app and adjust the schedule. For example, the date and time can be changed to avoid overlapping appointments. Furthermore, the conversation history analysis unit can link the analysis results of the conversation history with the calendar app and automatically adjust the schedule. For example, the date and time of a drinking party can be added to the calendar and a reminder can be set. This makes it possible to automatically adjust the user's schedule.

[0079] The conversation history analysis unit uses the emotion estimation function to analyze the user's emotions during a conversation in real time and make suggestions that will elicit positive emotions. For example, it can suggest restaurants that will bring joy to the user. The conversation history analysis unit also uses the generation AI to analyze the emotions during a conversation in real time and make suggestions that will elicit positive emotions. For example, it can suggest places where the user can relax. The conversation history analysis unit also uses the emotion estimation function to analyze the user's emotions during a conversation in real time and make suggestions that will elicit positive emotions. For example, it can suggest places that have fond memories from the past. This makes it possible to make suggestions that will elicit positive emotions in the user.

[0080] The hearing unit presents options to the user to make it easier for them to respond, and can provide an interface that allows them to easily enter missing information. For example, the generation AI presents options to the user and provides an interface that allows them to easily enter missing information. For example, options such as "Please select a date and time" can be displayed. The hearing unit also presents options to the user to make it easier for them to respond, and provides an interface. For example, options such as "Please select an area" can be displayed. The hearing unit also presents options to the user to make it easier for them to enter missing information. For example, options such as "Please select the number of participants" can be displayed. This allows the user to easily enter missing information.

[0081] The hearing unit can add a function that learns the user's past response history and automatically completes missing information. For example, the generation AI can learn the user's past response history and add a function that automatically completes missing information. For example, it can automatically suggest a date, time, and location based on past responses. In addition, the hearing unit analyzes the data to learn the user's past response history and automatically completes missing information. For example, it can predict the number of participants based on past responses. In addition, the hearing unit adds a function that learns the user's past response history and automatically completes missing information. For example, it can automatically suggest types of restaurants based on past responses. This makes it possible to automatically complete missing information based on the user's past response history.

[0082] The hearing unit can use the emotion estimation function to analyze the emotions the user feels when answering questions and adjust the order or content of questions so that the user does not feel stressed. For example, the emotion estimation function can be used to analyze the emotions the user feels when answering questions and adjust the order or content of questions so that the user does not feel stressed. For example, questions can be arranged so that the user can answer in a relaxed state. The hearing unit can also use the emotion estimation function to analyze the emotions the user feels when answering questions and adjust the order or content of questions so that the user does not feel stressed. For example, questions that elicit positive emotions can be prioritized. This allows the user to answer without feeling stressed.

[0083] The hearing unit can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can ask, "Please tell me the date and time" by voice. The hearing unit also provides an interface that supports voice input so that the user can respond by voice. For example, the generation AI can ask, "Please tell me the area" by voice. The generation AI can also support voice input when hearing the missing information, allowing the user to respond by voice. For example, the generation AI can ask, "Please tell me the number of participants" by voice. This allows the user to respond by voice to the missing information.

[0084] When a user answers a question in a hearing, the generation AI translates in real time, making it possible to accommodate users of different languages. For example, the generation AI translates the user's answer in real time, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in Japanese in English. Furthermore, when a user answers a question in a hearing, the generation AI translates in real time, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in English in Spanish. Furthermore, the hearing unit translates in real time, making it possible to accommodate users of different languages. For example, it can accommodate users who answer questions in French in German. This makes it possible to accommodate users of different languages.

[0085] The hearing unit uses the emotion estimation function to analyze the emotions of the user when answering a question in real time and make suggestions that will elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions of the user when answering a question in real time and make suggestions that will elicit positive emotions. For example, it can ask questions that will help the user relax. Furthermore, the hearing unit uses the emotion estimation function to analyze the emotions of the user when answering a question in real time and make suggestions that will elicit positive emotions. For example, it can ask questions that will make the user feel happy. Furthermore, the hearing unit uses the emotion estimation function to analyze the emotions of the user when answering a question in real time and make suggestions that will elicit positive emotions. For example, it can ask questions that will make the user feel at ease. This makes it possible to make suggestions that will elicit positive emotions from the user.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The conversation history analysis unit analyzes the conversation history in the LINE (registered trademark) group chat. For example, it analyzes text messages to extract the date, time, and location of the drinking party. It can also analyze voice messages to understand the requirements for the drinking party. It can also analyze messages with images to understand the theme and atmosphere of the drinking party. This is done using natural language processing technology, voice recognition technology, and image analysis technology. Step 2: The hearing unit hears the missing information from the user based on the information analyzed by the conversation history analysis unit. For example, if the date and time of the drinking party have not been decided, the hearing unit can ask the user, "When would you like to hold the drinking party?". If the location of the drinking party has not been decided, the hearing unit can also ask the user, "In what area would you like to hold the drinking party?". Furthermore, if the number of participants has not been decided, the hearing unit can also ask the user, "How many participants will there be?". The hearing unit presents the user with options and provides an interface that allows them to easily enter the missing information. Step 3: The confirmation section performs final confirmation based on the information obtained by the hearing section. For example, the confirmation section asks the user, "Is it okay to proceed with making a reservation at a yakiniku restaurant in Shinjuku at 7pm on Friday night?". The confirmation section can also ask the user for confirmation, such as, "Is it okay for there to be five participants?" or "Is it okay for the theme of the drinking party to be yakiniku?". The confirmation section may also refer to the user's past reservation history and suggest the best option. Step 4: The reservation unit completes the reservation based on the information confirmed by the confirmation unit. For example, it accesses the restaurant's reservation system and completes the reservation. Once the reservation is complete, it sends a notification of the reservation completion to the user. Furthermore, after the reservation is completed, it sets a reminder and notifies the user the day before the drinking party.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0155] 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 conversation history analysis unit that analyzes the conversation history in the group chat; a hearing unit that hears from a user about missing information based on the information analyzed by the conversation history analysis unit; a confirmation unit that performs a final confirmation based on the information obtained by the hearing unit; a reservation unit that completes the reservation based on the information confirmed by the confirmation unit. A system characterized by:

2. The conversation history analysis unit Learn about the user's past drinking party history or preferences to make personalized suggestions 2. The system of claim 1.

3. The conversation history analysis unit Using natural language processing technology to analyze the tone and nuance of a conversation and select the appropriate restaurant 2. The system of claim 1.

4. The conversation history analysis unit Analyzing the user's emotions during the conversation and prioritizing the selection of a date, time, or place where positive emotions predominate 2. The system of claim 1.

5. The conversation history analysis unit Analyze conversation history from other messaging apps to understand requirements 2. The system of claim 1.

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