System

Abstract

An object of a system according to an embodiment is to easily make a reservation for an overseas restaurant.SOLUTION: A system includes a reservation content reception unit, a telephone response unit, a negotiation unit, and a translation unit. The reservation content receiving unit receives reservation contents from the user. The telephone answering unit makes a telephone call to the restaurant based on the reservation details received by the reservation details receiving unit. The negotiation unit negotiates the reservation with the telephone called by the telephone answering unit. The translation unit automatically translates a state of the negotiation by the negotiation 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, making reservations at restaurants overseas can be difficult due to language barriers and the hassle of making international phone calls.

[0005] The system according to the embodiment aims to make it easy to make reservations at restaurants overseas. [Means for solving the problem]

[0006] The system according to the embodiment includes a reservation details receiving unit, a telephone answering unit, a negotiation unit, and a translation unit. The reservation details receiving unit receives reservation details from a user. The telephone answering unit makes a call to the restaurant based on the reservation details received by the reservation details receiving unit. The negotiation unit negotiates the reservation over the call made by the telephone answering unit. The translation unit automatically translates the negotiation process by the negotiation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily make reservations at restaurants overseas. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 (such as a prediction result) 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 reservation support system according to an embodiment of the present invention is a system in which a user simply provides the reservation details, and the generation AI automatically answers the phone and negotiates the reservation, while monitoring the negotiations with automatic translation. This allows the reservation support system to save the user the trouble of making international calls and allows for efficient reservations.

[0029] A reservation support system according to an embodiment includes a reservation details receiving unit, a telephone answering unit, a negotiation unit, and a translation unit. The reservation details receiving unit receives reservation details from a user. For example, the user inputs a prompt containing specific instructions, such as "I'd like to make a reservation for dinner for four people next Friday," into the generation AI. The telephone answering unit calls the restaurant based on the reservation details received by the reservation details receiving unit. For example, the generation AI makes an international call to a specified restaurant, uses speech recognition technology to understand what the restaurant staff is saying, and generates an appropriate response. The negotiation unit negotiates the reservation over the phone call made by the telephone answering unit. For example, the negotiation may be conducted in the form of, "Hello, I'd like to make a reservation for dinner for four people next Friday. Are you available?" The translation unit automatically translates the negotiation process conducted by the negotiation unit. For example, if the generation AI is negotiating in English, the user can view the content of the negotiation in Japanese. As a result, in the reservation support system according to an embodiment, the user only needs to provide the reservation details; the generation AI automatically answers the phone and negotiates the reservation, and the user can monitor the negotiation process using automatic translation.

[0030] The reservation details receiving unit can learn the user's past reservation history, predict the user's preferences and tendencies, and suggest optimal reservation details. For example, the reservation details receiving unit uses a generation AI to analyze the user's past reservation history and learn the user's preferences for cuisine and restaurant. For example, if a user has frequently made reservations for Italian food in the past, reservations at Italian restaurants will be suggested first. This improves user convenience by learning the user's past reservation history and suggesting optimal reservation details.

[0031] The reservation details receiving unit can link with the user's calendar and automatically suggest available dates and times. For example, the generation AI in the reservation details receiving unit links with the user's calendar app and automatically detects available dates and times. For example, it can sync with Google Calendar or Outlook Calendar and suggest available dates and times. This reduces the effort required to make a reservation by linking with the user's calendar and automatically suggesting available dates and times.

[0032] The reservation details receiving unit can analyze the user's voice input in real time and automatically convert the reservation details from the voice into text. For example, the reservation details receiving unit uses a generation AI to analyze the user's voice input in real time and automatically convert the reservation details from the voice into text. For example, a speech such as "I would like to make a reservation for dinner for four people next Friday" is converted into text. This reduces the effort required for input by analyzing the user's voice input in real time and automatically converting the reservation details into text.

[0033] The reservation details receiving unit can link with the user's social media account and suggest reservation details that take into account the schedules of friends and family. For example, the generation AI can link with the user's social media account and suggest reservation details that take into account the schedules of friends and family. For example, it can suggest the optimal reservation date and time based on event information on Facebook or Instagram. This allows for smooth group reservations by linking with the user's social media account and suggesting reservation details that take into account the schedules of friends and family.

[0034] The telephone response unit can learn the restaurant's past response patterns and automatically select the optimal negotiation strategy. For example, the telephone response unit uses a generation AI to learn the restaurant's past response patterns and automatically select the optimal negotiation strategy. For example, negotiations are conducted based on patterns with a high past reservation success rate. This improves the reservation success rate by learning the restaurant's past response patterns and automatically selecting the optimal negotiation strategy.

[0035] The telephone answering unit can check the congestion status of a restaurant in real time and suggest the optimal reservation time. For example, the generation AI in the telephone answering unit can check the congestion status of a restaurant in real time and suggest the optimal reservation time. For example, it can grasp the congestion status based on information from the restaurant's online reservation system or social media. This improves user convenience by checking the congestion status of a restaurant in real time and suggesting the optimal reservation time.

[0036] The telephone answering unit can call multiple restaurants simultaneously and select the restaurant with the best conditions. For example, the generation AI can call multiple restaurants simultaneously and select the restaurant with the best conditions. For example, it can check seat availability and whether special requests can be accommodated. This improves user convenience by calling multiple restaurants simultaneously and selecting the restaurant with the best conditions.

[0037] The telephone answering unit can obtain menu information of restaurants in advance and check whether special requests can be accommodated. For example, the generation AI can obtain menu information of restaurants in advance and check whether special requests can be accommodated. For example, it can check whether there are vegetarian or allergy-friendly menus. This improves user convenience by obtaining menu information of restaurants in advance and checking whether special requests can be accommodated.

[0038] The translation unit can analyze the progress of negotiations in real time and automatically suggest appropriate instructions to the user. For example, the generative AI in the translation unit can analyze the progress of negotiations in real time and automatically suggest appropriate instructions to the user. For example, it can suggest alternatives if negotiations are proving difficult. In this way, the success rate of negotiations is improved by analyzing the progress of negotiations in real time and automatically suggesting appropriate instructions to the user.

[0039] The translation unit can summarize the content of the negotiation and notify the user of only the important points. For example, the generation AI can summarize the content of the negotiation in real time and notify the user of only the important points. For example, it can notify the user of whether a reservation can be made or the status of special requests. This helps the user understand by summarizing the content of the negotiation and notifying the user of only the important points.

[0040] The translation unit reads out the contents of the negotiation aloud, making it suitable for visually impaired people. For example, the generation AI reads out the contents of the negotiation aloud in real time, making it suitable for visually impaired people. For example, it notifies the progress of the negotiation and important points by voice. This makes it possible to accommodate visually impaired people by reading out the contents of the negotiation aloud.

[0041] The translation unit can visualize the content of the negotiation so that the user can intuitively understand it. For example, the translation unit can use a generation AI to visualize the content of the negotiation so that the user can intuitively understand it. For example, the progress of the negotiation can be displayed in graphs or charts. In this way, the content of the negotiation can be visualized so that the user can intuitively understand it.

[0042] The translation unit can automatically detect the user's language setting and respond in the most appropriate language. For example, the generation AI in the translation unit automatically detects the language setting of the user's device and responds in the most appropriate language. For example, if the device's language setting is Japanese, the translation unit responds in Japanese. This improves user convenience by automatically detecting the user's language setting and responding in the most appropriate language.

[0043] The translation unit analyzes multiple languages ​​simultaneously and can smoothly accommodate cases where a user speaks multiple languages. For example, the translation unit can handle cases where the generation AI analyzes multiple languages ​​simultaneously and a user speaks multiple languages ​​simultaneously. For example, it can handle cases where a user alternates between English and Spanish. This improves user convenience by analyzing multiple languages ​​simultaneously and smoothly accommodating cases where a user speaks multiple languages ​​simultaneously.

[0044] The translation unit can select appropriate language and expressions, taking into account the cultural background of each region. For example, the generation AI can select appropriate language and expressions, taking into account the cultural background of each region. For example, it can use polite expressions that are in line with Japanese culture. This improves the user experience by taking into account the cultural background of each region and selecting appropriate language and expressions.

[0045] The translation unit can learn the user's language skills and respond in language at an appropriate level. For example, the generation AI in the translation unit learns the user's language skills and responds in language at an appropriate level. For example, if the user uses beginner-level English, it responds in simple English. This improves user convenience by learning the user's language skills and responding in language at an appropriate level.

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

[0047] The reservation details receiving unit can link with the user's health data and suggest optimal reservation details based on the user's health condition. For example, it can suggest restaurants that offer healthy meals based on data obtained from the user's fitness tracker or smartwatch. Also, if the user has a specific allergy, it can take that information into consideration and prioritize suggestions of restaurants that offer allergy-friendly menus. It can also suggest restaurants that offer menus tailored to the user's health goals (e.g., weight loss or muscle building). This supports the user's health management by suggesting optimal reservation details based on the user's health condition.

[0048] The reservation details receiving unit can learn the user's past reviews and ratings and prioritize suggesting restaurants that the user has given high ratings to. For example, it can analyze the reviews and ratings of restaurants the user has visited in the past and suggest restaurants that the user has given high satisfaction with. Also, if the user has given a high rating to a particular dish or service, it can suggest other restaurants that have those elements. Furthermore, it can suggest new restaurants based on the user's review history and taking into account the ratings of other users with similar preferences. This makes it possible to suggest reservation details that will provide a higher level of satisfaction by utilizing the user's past reviews and ratings.

[0049] The reservation details receiving unit can work with the user's travel plans to suggest optimal restaurant reservations at the travel destination. For example, it can obtain the user's travel schedule and suggest restaurants at the travel destination. It can also suggest restaurants that can be visited between sightseeing stops, taking into account tourist spots and event information at the travel destination. Furthermore, it can enrich the travel experience by suggesting restaurants where you can enjoy the local food culture and specialties of the travel destination. This improves the convenience and enjoyment of travel by working with the user's travel plans to suggest optimal restaurant reservations at the travel destination.

[0050] The reservation details receiving unit can suggest special events and themed dinners based on the user's hobbies and interests. For example, if the user is a music lover, a restaurant with live music performances can be suggested. If the user is a movie fan, a movie-themed dinner event can be suggested. Furthermore, if the user is interested in a particular sport, a restaurant with an event related to that sport can be suggested. In this way, the user's experience can be enriched by suggesting special events and themed dinners based on the user's hobbies and interests.

[0051] The reservation details receiving unit can learn the user's past travel history and suggest optimal restaurant reservations at the travel destination. For example, it can analyze the ratings and reviews of restaurants at the user's past travel destinations and suggest restaurants that the user found particularly satisfying. Also, if the user frequently visits a particular region or country, it can suggest new restaurants in that area. It can also suggest restaurants that suit the user's travel style (for example, business trip or family trip). In this way, the system can utilize the user's past travel history to suggest optimal restaurant reservations at the travel destination, improving the convenience and enjoyment of travel.

[0052] The reservation details receiving unit can utilize the user's geographical location information to suggest the restaurant closest to the user's current location. For example, it can obtain GPS data from the user's smartphone and suggest restaurants that are within walking distance or a short drive from the user's current location. If the user is in a specific area, it can also suggest popular restaurants in that area or recommended local spots. Furthermore, if the user is traveling, it can also suggest restaurants that the user can stop by on the way to their destination. This improves user convenience by utilizing the user's geographical location information to suggest the restaurant closest to the user's current location.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reservation details receiver receives the reservation details from the user. For example, the user inputs a prompt containing specific instructions, such as "I would like to make a reservation for dinner for four people next Friday," into the generation AI. Step 2: The telephone answering unit calls the restaurant based on the reservation details received by the reservation details receiving unit. For example, the generation AI makes an international call to the specified restaurant, uses voice recognition technology to understand what the restaurant staff is saying, and generates an appropriate response. Step 3: The Negotiation Department negotiates the reservation over the phone, which is placed by the Telephone Answering Department. For example, "Hello, I'd like to make a reservation for dinner for four next Friday. Are you available?" Step 4: The translation unit automatically translates the negotiations conducted by the negotiation unit. For example, if the generation AI is conducting a negotiation in English, the user can check the content of the negotiation in Japanese.

[0055] (Example 2) The reservation support system according to an embodiment of the present invention is a system in which a user simply provides the reservation details, and the generation AI automatically answers the phone and negotiates the reservation, while monitoring the negotiations with automatic translation. This allows the reservation support system to save the user the trouble of making international calls and allows for efficient reservations.

[0056] A reservation support system according to an embodiment includes a reservation details receiving unit, a telephone answering unit, a negotiation unit, and a translation unit. The reservation details receiving unit receives reservation details from a user. For example, the user inputs a prompt containing specific instructions, such as "I'd like to make a reservation for dinner for four people next Friday," into the generation AI. The telephone answering unit calls the restaurant based on the reservation details received by the reservation details receiving unit. For example, the generation AI makes an international call to a specified restaurant, uses speech recognition technology to understand what the restaurant staff is saying, and generates an appropriate response. The negotiation unit negotiates the reservation over the phone call made by the telephone answering unit. For example, the negotiation may be conducted in the form of, "Hello, I'd like to make a reservation for dinner for four people next Friday. Are you available?" The translation unit automatically translates the negotiation process conducted by the negotiation unit. For example, if the generation AI is negotiating in English, the user can view the content of the negotiation in Japanese. As a result, in the reservation support system according to an embodiment, the user only needs to provide the reservation details; the generation AI automatically answers the phone and negotiates the reservation, and the user can monitor the negotiation process using automatic translation.

[0057] The reservation details receiving unit can learn the user's past reservation history, predict the user's preferences and tendencies, and suggest optimal reservation details. For example, the reservation details receiving unit uses a generation AI to analyze the user's past reservation history and learn the user's preferences for cuisine and restaurant. For example, if a user has frequently made reservations for Italian food in the past, reservations at Italian restaurants will be suggested first. This improves user convenience by learning the user's past reservation history and suggesting optimal reservation details.

[0058] The reservation details receiving unit can link with the user's calendar and automatically suggest available dates and times. For example, the generation AI in the reservation details receiving unit links with the user's calendar app and automatically detects available dates and times. For example, it can sync with Google Calendar or Outlook Calendar and suggest available dates and times. This reduces the effort required to make a reservation by linking with the user's calendar and automatically suggesting available dates and times.

[0059] The reservation details receiving unit uses an emotion estimation function to analyze the emotions of the user when entering reservation details, and can make suggestions to help the user relax if the user is feeling stressed. For example, the reservation details receiving unit uses a generation AI to analyze the user's voice and facial expressions, and analyzes the emotions of the user when entering reservation details in real time. For example, stress is detected from the tone of voice and facial expression. This analyzes the user's emotions, and if the user is feeling stressed, suggestions to help them relax are made, thereby reducing the user's stress.

[0060] The reservation details receiving unit can analyze the user's voice input in real time and automatically convert the reservation details from the voice into text. For example, the reservation details receiving unit uses a generation AI to analyze the user's voice input in real time and automatically convert the reservation details from the voice into text. For example, a speech such as "I would like to make a reservation for dinner for four people next Friday" is converted into text. This reduces the effort required for input by analyzing the user's voice input in real time and automatically converting the reservation details into text.

[0061] The reservation details receiving unit can link with the user's social media account and suggest reservation details that take into account the schedules of friends and family. For example, the generation AI can link with the user's social media account and suggest reservation details that take into account the schedules of friends and family. For example, it can suggest the optimal reservation date and time based on event information on Facebook or Instagram. This allows for smooth group reservations by linking with the user's social media account and suggesting reservation details that take into account the schedules of friends and family.

[0062] The reservation details receiving unit can use the emotion estimation function to analyze the emotions of the user when entering reservation details and provide an interface for eliciting positive emotions. For example, the reservation details receiving unit uses the emotion estimation function to analyze the emotions of the user when entering reservation details in real time and provide an interface for eliciting positive emotions. For example, the reservation details receiving unit displays an animation that makes the user smile. This improves the user experience by analyzing the user's emotions and providing an interface for eliciting positive emotions.

[0063] The telephone response unit can learn the restaurant's past response patterns and automatically select the optimal negotiation strategy. For example, the telephone response unit uses a generation AI to learn the restaurant's past response patterns and automatically select the optimal negotiation strategy. For example, negotiations are conducted based on patterns with a high past reservation success rate. This improves the reservation success rate by learning the restaurant's past response patterns and automatically selecting the optimal negotiation strategy.

[0064] The telephone answering unit can check the congestion status of a restaurant in real time and suggest the optimal reservation time. For example, the generation AI in the telephone answering unit can check the congestion status of a restaurant in real time and suggest the optimal reservation time. For example, it can grasp the congestion status based on information from the restaurant's online reservation system or social media. This improves user convenience by checking the congestion status of a restaurant in real time and suggesting the optimal reservation time.

[0065] The telephone answering unit can use the emotion estimation function to analyze the emotions of the restaurant staff and take appropriate action to smoothly proceed with negotiations. The telephone answering unit, for example, uses the emotion estimation function to analyze the emotions of the restaurant staff in real time and take appropriate action to smoothly proceed with negotiations. For example, if the staff is feeling stressed, the telephone answering unit takes action to help them relax. In this way, by analyzing the emotions of the restaurant staff and taking appropriate action, negotiations can proceed smoothly.

[0066] The telephone answering unit can call multiple restaurants simultaneously and select the restaurant with the best conditions. For example, the generation AI can call multiple restaurants simultaneously and select the restaurant with the best conditions. For example, it can check seat availability and whether special requests can be accommodated. This improves user convenience by calling multiple restaurants simultaneously and selecting the restaurant with the best conditions.

[0067] The telephone answering unit can obtain menu information of restaurants in advance and check whether special requests can be accommodated. For example, the generation AI can obtain menu information of restaurants in advance and check whether special requests can be accommodated. For example, it can check whether there are vegetarian or allergy-friendly menus. This improves user convenience by obtaining menu information of restaurants in advance and checking whether special requests can be accommodated.

[0068] The telephone answering unit can use the emotion estimation function to analyze the emotions of the restaurant staff and conduct a conversation to elicit positive emotions. For example, the telephone answering unit uses the emotion estimation function to analyze the emotions of the restaurant staff in real time and conduct a conversation to elicit positive emotions. For example, the telephone answering unit uses compliments that will make the staff happy. In this way, by analyzing the emotions of the restaurant staff and conducting a conversation to elicit positive emotions, negotiations can proceed smoothly.

[0069] The translation unit can analyze the progress of negotiations in real time and automatically suggest appropriate instructions to the user. For example, the generative AI in the translation unit can analyze the progress of negotiations in real time and automatically suggest appropriate instructions to the user. For example, it can suggest alternatives if negotiations are proving difficult. In this way, the success rate of negotiations is improved by analyzing the progress of negotiations in real time and automatically suggesting appropriate instructions to the user.

[0070] The translation unit can summarize the content of the negotiation and notify the user of only the important points. For example, the generation AI can summarize the content of the negotiation in real time and notify the user of only the important points. For example, it can notify the user of whether a reservation can be made or the status of special requests. This helps the user understand by summarizing the content of the negotiation and notifying the user of only the important points.

[0071] The translation unit reads out the contents of the negotiation aloud, making it suitable for visually impaired people. For example, the generation AI reads out the contents of the negotiation aloud in real time, making it suitable for visually impaired people. For example, it notifies the progress of the negotiation and important points by voice. This makes it possible to accommodate visually impaired people by reading out the contents of the negotiation aloud.

[0072] The translation unit can visualize the content of the negotiation so that the user can intuitively understand it. For example, the translation unit can use a generation AI to visualize the content of the negotiation so that the user can intuitively understand it. For example, the progress of the negotiation can be displayed in graphs or charts. In this way, the content of the negotiation can be visualized so that the user can intuitively understand it.

[0073] The translation unit can use the emotion estimation function to analyze the emotions of the user as they watch the progress of the negotiation and provide feedback to elicit positive emotions. For example, the translation unit uses the emotion estimation function to analyze the emotions of the user as they watch the progress of the negotiation in real time and provide feedback to elicit positive emotions. For example, the translation unit displays an animation that makes the user smile. This improves the user experience by analyzing the user's emotions and providing feedback to elicit positive emotions.

[0074] The translation unit can automatically detect the user's language setting and respond in the most appropriate language. For example, the generation AI in the translation unit automatically detects the language setting of the user's device and responds in the most appropriate language. For example, if the device's language setting is Japanese, the translation unit responds in Japanese. This improves user convenience by automatically detecting the user's language setting and responding in the most appropriate language.

[0075] The translation unit analyzes multiple languages ​​simultaneously and can smoothly accommodate cases where a user speaks multiple languages. For example, the translation unit can handle cases where the generation AI analyzes multiple languages ​​simultaneously and a user speaks multiple languages ​​simultaneously. For example, it can handle cases where a user alternates between English and Spanish. This improves user convenience by analyzing multiple languages ​​simultaneously and smoothly accommodating cases where a user speaks multiple languages ​​simultaneously.

[0076] The translation unit can use the emotion estimation function to analyze the emotion of the user regarding the language used and respond in the language that is most relaxing. For example, the translation unit uses the emotion estimation function to analyze the emotion of the user regarding the language used in real time and respond in the language that is most relaxing. For example, if the user feels relaxed in Japanese, the translation unit responds in Japanese. In this way, the user's experience is improved by analyzing the user's emotion and responding in the language that is most relaxing.

[0077] The translation unit can select appropriate language and expressions, taking into account the cultural background of each region. For example, the generation AI can select appropriate language and expressions, taking into account the cultural background of each region. For example, it can use polite expressions that are in line with Japanese culture. This improves the user experience by taking into account the cultural background of each region and selecting appropriate language and expressions.

[0078] The translation unit can learn the user's language skills and respond in language at an appropriate level. For example, the generation AI in the translation unit learns the user's language skills and responds in language at an appropriate level. For example, if the user uses beginner-level English, it responds in simple English. This improves user convenience by learning the user's language skills and responding in language at an appropriate level.

[0079] The translation unit can use the emotion estimation function to analyze the emotion of the user toward the language used and select a language that will elicit positive emotions. For example, the translation unit uses the emotion estimation function to analyze the emotion of the user toward the language used in real time and select a language that will elicit positive emotions. For example, if the user has positive emotions in Japanese, the translation unit responds in Japanese. In this way, analyzing the user's emotions and selecting a language that will elicit positive emotions improves the user experience.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reservation details receiving unit can link with the user's health data and suggest optimal reservation details based on the user's health condition. For example, it can suggest restaurants that offer healthy meals based on data obtained from the user's fitness tracker or smartwatch. Also, if the user has a specific allergy, it can take that information into consideration and prioritize suggestions of restaurants that offer allergy-friendly menus. It can also suggest restaurants that offer menus tailored to the user's health goals (e.g., weight loss or muscle building). This supports the user's health management by suggesting optimal reservation details based on the user's health condition.

[0082] The reservation details receiving unit can learn the user's past reviews and ratings and prioritize suggesting restaurants that the user has given high ratings to. For example, it can analyze the reviews and ratings of restaurants the user has visited in the past and suggest restaurants that the user has given high satisfaction with. Also, if the user has given a high rating to a particular dish or service, it can suggest other restaurants that have those elements. Furthermore, it can suggest new restaurants based on the user's review history and taking into account the ratings of other users with similar preferences. This makes it possible to suggest reservation details that will provide a higher level of satisfaction by utilizing the user's past reviews and ratings.

[0083] The reservation details receiving unit can work with the user's travel plans to suggest optimal restaurant reservations at the travel destination. For example, it can obtain the user's travel schedule and suggest restaurants at the travel destination. It can also suggest restaurants that can be visited between sightseeing stops, taking into account tourist spots and event information at the travel destination. Furthermore, it can enrich the travel experience by suggesting restaurants where you can enjoy the local food culture and specialties of the travel destination. This improves the convenience and enjoyment of travel by working with the user's travel plans to suggest optimal restaurant reservations at the travel destination.

[0084] The reservation details receiving unit uses the emotion estimation function to analyze the emotions of the user when entering reservation details, and can make reassuring suggestions if the user feels anxious. For example, it can analyze the user's voice and facial expressions and carefully explain the reservation procedure if the user feels anxious. Also, if the user feels anxious about a particular restaurant, it can reassure the user by providing detailed information about the restaurant and past reviews. Furthermore, if the user feels anxious, it can provide an option to directly contact customer support. In this way, the user's emotions can be analyzed and reassuring suggestions made if the user feels anxious, reducing stress for the user.

[0085] The reservation details receiving unit can suggest special events and themed dinners based on the user's hobbies and interests. For example, if the user is a music lover, a restaurant with live music performances can be suggested. If the user is a movie fan, a movie-themed dinner event can be suggested. Furthermore, if the user is interested in a particular sport, a restaurant with an event related to that sport can be suggested. In this way, the user's experience can be enriched by suggesting special events and themed dinners based on the user's hobbies and interests.

[0086] The reservation details receiving unit uses the emotion estimation function to analyze the emotions of the user when entering reservation details, and if the user is excited, it can make suggestions to further enhance that emotion. For example, by analyzing the user's voice and facial expression, if the user is excited, it can suggest special services or events. Also, if the user is excited about a particular restaurant, it can heighten the user's expectations by introducing the restaurant's special menu or limited-time events. Furthermore, if the user is excited, it can provide a smooth reservation experience by quickly providing reservation confirmation and detailed information. In this way, the user's experience can be improved by analyzing the user's emotions and making suggestions to further enhance the user's emotions if the user is excited.

[0087] The reservation details receiving unit can learn the user's past travel history and suggest optimal restaurant reservations at the travel destination. For example, it can analyze the ratings and reviews of restaurants at the user's past travel destinations and suggest restaurants that the user found particularly satisfying. Also, if the user frequently visits a particular region or country, it can suggest new restaurants in that area. It can also suggest restaurants that suit the user's travel style (for example, business trip or family trip). In this way, the system can utilize the user's past travel history to suggest optimal restaurant reservations at the travel destination, improving the convenience and enjoyment of travel.

[0088] The reservation details receiving unit uses the emotion estimation function to analyze the emotions of the user when entering reservation details, and can make refreshing suggestions if the user is tired. For example, by analyzing the user's voice and facial expression, if the user is tired, it can suggest a relaxing restaurant or spa. Also, if the user feels tired at a particular restaurant, it can provide a sense of refreshment by recommending relaxing seats and a quiet environment at that restaurant. Furthermore, if the user is tired, it can also provide an option to simplify the reservation procedure. In this way, by analyzing the user's emotions and making refreshing suggestions if the user is tired, it can reduce the user's stress.

[0089] The reservation details receiving unit can utilize the user's geographical location information to suggest the restaurant closest to the user's current location. For example, it can obtain GPS data from the user's smartphone and suggest restaurants that are within walking distance or a short drive from the user's current location. If the user is in a specific area, it can also suggest popular restaurants in that area or recommended local spots. Furthermore, if the user is traveling, it can also suggest restaurants that the user can stop by on the way to their destination. This improves user convenience by utilizing the user's geographical location information to suggest the restaurant closest to the user's current location.

[0090] The reservation details receiving unit uses the emotion estimation function to analyze the emotions of the user when entering reservation details, and if the user is happy, it can make suggestions to further enhance that emotion. For example, by analyzing the user's voice and facial expression, if the user is happy, it can suggest special services or events. Also, if the user is happy with a particular restaurant, it can heighten the user's expectations by introducing the restaurant's special menu or limited-time events. Furthermore, if the user is happy, it can provide a smooth reservation experience by quickly providing reservation confirmation and detailed information. In this way, the user's experience can be improved by analyzing the user's emotions and, if the user is happy, making suggestions to further enhance that emotion.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reservation details receiver receives the reservation details from the user. For example, the user inputs a prompt containing specific instructions, such as "I would like to make a reservation for dinner for four people next Friday," into the generation AI. Step 2: The telephone answering unit calls the restaurant based on the reservation details received by the reservation details receiving unit. For example, the generation AI makes an international call to the specified restaurant, uses voice recognition technology to understand what the restaurant staff is saying, and generates an appropriate response. Step 3: The Negotiation Department negotiates the reservation over the phone, which is placed by the Telephone Answering Department. For example, "Hello, I'd like to make a reservation for dinner for four next Friday. Are you available?" Step 4: The translation unit automatically translates the negotiations conducted by the negotiation unit. For example, if the generation AI is conducting a negotiation in English, the user can check the content of the negotiation in Japanese.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 reservation content receiving unit that receives reservation content from a user; a telephone answering unit that makes a call to the restaurant based on the reservation details received by the reservation details receiving unit; a negotiation unit that negotiates reservations over the telephone made by the telephone answering unit; a translation unit that automatically translates the state of negotiation by the negotiation unit; A system characterized by:

2. The reservation content receiving unit The system learns the user's past reservation history, predicts the user's preferences and tendencies, and proposes optimal reservation contents.

2. The system of claim 1.

3. The reservation content receiving unit The user's voice input is analyzed in real time, and the reservation details are automatically converted from the voice into text.

2. The system of claim 1.

4. The telephone answering unit Learn the restaurant's past response patterns and automatically select the optimal negotiation strategy 2. The system of claim 1.

5. The translation unit Analyzing the progress of the negotiation in real time and automatically suggesting appropriate instructions to the user.

2. The system of claim 1.

6. The translation unit Automatically detect the user's language setting and respond in the most appropriate language 2. The system of claim 1.

7. The reservation content receiving unit Using an emotion estimation function, analyze the emotions of the user when entering reservation details, and make suggestions to help the user relax if they are feeling stressed.

2. The system of claim 1.

8. The telephone answering unit Using emotion estimation function, analyze the emotions of restaurant staff and respond appropriately to facilitate the negotiation.

2. The system of claim 1.

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