System

Abstract

An object of a system according to an embodiment is to efficiently perform schedule adjustment, shop determination, and reservation in meal setting.SOLUTION: A system includes a schedule adjustment part, a store determination part, and a reservation part. The schedule adjustment unit adjusts an optimal schedule based on the schedule information provided by the user. The restaurant deciding part selects an optimum restaurant on the basis of conditions such as the budget, location, hobbies and tastes, atmosphere, and smoking / non-smoking of the user. The reservation unit makes a reservation at the selected restaurant.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, meal planning involves complicated tasks such as scheduling, choosing a restaurant, and making reservations, making it difficult to complete efficiently.

[0005] The system according to the embodiment aims to efficiently arrange dates, select restaurants, and make reservations when setting up a meal. [Means for solving the problem]

[0006] The system according to the embodiment includes a schedule adjustment unit, a restaurant selection unit, and a reservation unit. The schedule adjustment unit adjusts the optimal schedule based on schedule information provided by the user. The restaurant selection unit selects the optimal restaurant based on the user's budget, location, hobbies, preferences, atmosphere, smoking / non-smoking status, and other conditions. The reservation unit makes a reservation at the selected restaurant. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently arrange dates, select restaurants, and make reservations when setting up a meal. [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 (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) An AI system according to an embodiment of the present invention automates the setup of a meal for a small group of four or less people. In this system, a generation AI performs a series of tasks, such as scheduling, choosing a restaurant, and making reservations. This allows the AI ​​system to significantly reduce the effort required for the user to set up a meal, and actually realize the opportunity to have a meal.

[0029] The AI ​​system according to the embodiment includes a schedule adjustment unit, a restaurant selection unit, and a reservation unit. The schedule adjustment unit adjusts the optimal schedule based on schedule information provided by the user. For example, if a user inputs a vague request such as "a weekday evening next week," the generation AI analyzes each user's schedule and proposes a date and time that is convenient for everyone. The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI adjusts the optimal schedule based on the prompts. The restaurant selection unit selects the optimal restaurant based on the user's budget, location, hobbies, preferences, atmosphere, and non-smoking / smoking preferences. For example, if the user inputs criteria such as "budget of 5,000 yen per person, within a 5-minute walk from the station, preference for Japanese food, and a restaurant with non-smoking seats," the generation AI searches for restaurants that meet these criteria and proposes several candidates. The reservation unit makes a reservation at the selected restaurant. For example, the generation AI may make a reservation in the form of "Please make a reservation for 4 people at 7:00 PM on XX / XX." Once the reservation is complete, the generation AI sends a confirmation message to the user. This allows the AI ​​system to automate the user's meal preparation, significantly reducing the amount of work required.

[0030] The schedule adjustment unit can learn the user's past schedule history and predict the most suitable schedule. For example, the generation AI analyzes the user's schedule history for the past year and finds patterns where appointments are concentrated on certain days of the week or time periods. For example, if the user has many appointments every Tuesday night, the unit will prioritize suggesting dates other than Tuesday. This makes it possible to predict the optimal schedule based on the user's past schedule history.

[0031] The schedule adjustment unit can propose the optimal schedule taking into account the user's health data. For example, the schedule adjustment unit analyzes sleep data acquired from the user's wearable device by the generation AI and proposes a schedule that will allow the user to participate in the most refreshed state. For example, it prioritizes proposing plans for the day after the user has had enough sleep. This makes it possible to propose the optimal schedule taking into account the user's health data.

[0032] The schedule adjustment unit can also consider the schedules of the user's family and friends and propose the optimal date that everyone can participate. For example, the generation AI shares the schedules of the user's family and friends and proposes the optimal date that everyone can participate. For example, it synchronizes everyone's calendars to find common free time. This makes it possible to propose the optimal date that everyone can participate, taking into consideration the schedules of the user's family and friends.

[0033] The schedule adjustment unit works in conjunction with the user's work schedule to suggest a schedule that will reduce the burden on the workforce. For example, the generation AI analyzes the user's work schedule and suggests a schedule that will reduce the burden on the workforce. For example, it prioritizes suggestions for days with fewer meetings or projects. This makes it possible to suggest a schedule that will reduce the burden on the workforce in conjunction with the user's work schedule.

[0034] The restaurant selection unit learns from users' past reviews and ratings and can suggest restaurants that provide the highest level of satisfaction. For example, the generation AI analyzes users' past reviews and ratings and suggests restaurants that provide the highest level of satisfaction. For example, it prioritizes restaurants that users have given high ratings. This makes it possible to suggest restaurants that provide the highest level of satisfaction based on users' past reviews and ratings.

[0035] The restaurant selection unit can take into account real-time congestion conditions and suggest restaurants with short waiting times. For example, the generation AI analyzes real-time congestion conditions and suggests restaurants with short waiting times. For example, it prioritizes suggesting restaurants with less waiting time based on the current congestion situation. This makes it possible to suggest restaurants with short waiting times taking into account real-time congestion conditions.

[0036] The restaurant selection unit can suggest safe restaurants by taking into account the user's dietary restrictions and allergy information. For example, the generation AI analyzes the user's dietary restrictions and allergy information and suggests safe restaurants. For example, it prioritizes gluten-free and vegan restaurants. This allows the system to suggest safe restaurants by taking into account the user's dietary restrictions and allergy information.

[0037] The restaurant selection unit can suggest new restaurants based on the user's past visit history. For example, the generation AI analyzes the user's past visit history and suggests new restaurants. For example, it prioritizes suggesting restaurants that the user has not visited. This makes it possible to suggest new restaurants based on the user's past visit history.

[0038] The reservation part works in conjunction with the user's calendar to prevent overlapping reservations. For example, the reservation part will build a system where the generation AI works in conjunction with the user's calendar to prevent overlapping reservations. For example, it will prevent reservations from being made during times when there are already scheduled appointments. This will prevent overlapping reservations by working in conjunction with the user's calendar.

[0039] The reservation section can take into account the restaurant's cancellation policy and suggest the optimal reservation timing. For example, the generation AI analyzes the restaurant's cancellation policy and suggests the optimal reservation timing. For example, it prioritizes reservations within periods when no cancellation fees are incurred. This allows the reservation section to suggest the optimal reservation timing taking into account the restaurant's cancellation policy.

[0040] The reservation unit can suggest the smoothest reservation method based on the user's past reservation history. For example, the generation AI analyzes the user's past reservation history and suggests the smoothest reservation method. For example, it prioritizes suggesting reservation methods that the user has used in the past. This makes it possible to suggest the smoothest reservation method based on the user's past reservation history.

[0041] The reservation unit can suggest the best time to make a reservation by taking into account the restaurant's special events and promotional information. For example, the generation AI analyzes the restaurant's special event information and suggests the best time to make a reservation. For example, the reservation is made on the day when the special event is held. This allows the reservation unit to suggest the best time to make a reservation by taking into account the restaurant's special events and promotional information.

[0042] The confirmation and reminder section works in conjunction with the user's calendar to optimize the timing of reminders. For example, the generation AI works in conjunction with the user's calendar to optimize the timing of reminders. For example, it sends a reminder one day and one hour before the appointment. This allows the timing of reminders to be optimized in conjunction with the user's calendar.

[0043] The confirmation and reminder section can learn the user's past reminder history and suggest the most effective reminder method. For example, the confirmation and reminder section uses a generation AI to analyze the user's past reminder history and suggest the most effective reminder method. For example, it will prioritize suggesting reminder methods that the user has responded to in the past. This makes it possible to suggest the most effective reminder method based on the user's past reminder history.

[0044] The confirmation and reminder section can also send reminders to the user's family and friends to ensure that no one forgets. For example, the generation AI can share the contact information of the user's family and friends and send reminders to everyone. For example, it can send the same reminder to everyone. This allows reminders to be sent to the user's family and friends to ensure that no one forgets.

[0045] The confirmation and reminder section can customize the content of the reminder and send a message that matches the user's preferences. For example, the generation AI can send a reminder message that matches the user's preferences. For example, it can send a message that includes words or phrases that the user likes. This allows the reminder message to be sent according to the user's preferences.

[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 schedule adjustment unit can also propose optimal dates taking into account the user's hobbies and interests. For example, if the user has plans to attend a specific sporting event or concert, the schedule adjustment unit can suggest dates that avoid those events. Also, if the user wishes to attend an event related to a specific hobby, the schedule adjustment unit can suggest dates that coincide with that event. In this way, the schedule adjustment unit can propose optimal dates taking into account the user's hobbies and interests.

[0048] The restaurant selection unit can also suggest restaurants that offer the newest dishes and menus based on the user's past dining history. For example, it can analyze the menus of restaurants the user has visited in the past and suggest restaurants that offer new dishes that the user has not yet tried. It can also prioritize suggesting restaurants that offer seasonal menus or new menu items. This makes it possible to suggest restaurants that offer new dishes and menus based on the user's dining history.

[0049] The reservation unit can also suggest the smoothest reservation method based on the user's past reservation history. For example, it can analyze the reservation methods the user has used in the past and prioritize the method that allowed for the smoothest reservation completion. It can also suggest the easiest reservation method based on the reservation sites and apps the user has used in the past. This makes it possible to suggest the smoothest reservation method based on the user's past reservation history.

[0050] The confirmation and reminder section can also learn the user's past reminder history and suggest the most effective reminder method. For example, it can analyze reminder methods that the user has responded to in the past and prioritize the most effective method. It can also send more effective reminders by avoiding reminder methods that the user has ignored in the past. This allows it to suggest the most effective reminder method based on the user's past reminder history.

[0051] The restaurant selection unit can also suggest safe restaurants by taking into account the user's dietary restrictions and allergy information. For example, the generation AI can analyze the user's dietary restrictions and allergy information and suggest safe restaurants. For example, it can prioritize suggestions for gluten-free and vegan restaurants. This allows it to suggest safe restaurants by taking into account the user's dietary restrictions and allergy information.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The schedule adjustment unit adjusts the optimal schedule based on the schedule information provided by the user. For example, if a user inputs a vague request such as "a weekday evening next week," the generation AI analyzes each user's schedule and proposes a date and time that is convenient for everyone. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI adjusts the optimal schedule based on that prompt. Step 2: The restaurant selection unit selects the optimal restaurant based on the user's budget, location, hobbies, preferences, atmosphere, smoking / non-smoking status, etc. For example, if the user inputs criteria such as "budget of 5,000 yen per person, within a 5-minute walk from the station, I like Japanese food, and I want a restaurant with non-smoking seats," the generation AI will search for restaurants that meet these criteria and suggest several candidates. Step 3: The reservation unit makes a reservation at the selected restaurant. For example, the generation AI might make a reservation in the form of "Please make a reservation for 4 people at 7pm on XX / XX." Once the reservation is complete, the generation AI sends a confirmation message to the user.

[0054] (Example 2) An AI system according to an embodiment of the present invention automates the setup of a meal for a small group of four or less people. In this system, a generation AI performs a series of tasks, such as scheduling, choosing a restaurant, and making reservations. This allows the AI ​​system to significantly reduce the effort required for the user to set up a meal, and actually realize the opportunity to have a meal.

[0055] The AI ​​system according to the embodiment includes a schedule adjustment unit, a restaurant selection unit, and a reservation unit. The schedule adjustment unit adjusts the optimal schedule based on schedule information provided by the user. For example, if a user inputs a vague request such as "a weekday evening next week," the generation AI analyzes each user's schedule and proposes a date and time that is convenient for everyone. The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI adjusts the optimal schedule based on the prompts. The restaurant selection unit selects the optimal restaurant based on the user's budget, location, hobbies, preferences, atmosphere, and non-smoking / smoking preferences. For example, if the user inputs criteria such as "budget of 5,000 yen per person, within a 5-minute walk from the station, preference for Japanese food, and a restaurant with non-smoking seats," the generation AI searches for restaurants that meet these criteria and proposes several candidates. The reservation unit makes a reservation at the selected restaurant. For example, the generation AI may make a reservation in the form of "Please make a reservation for 4 people at 7:00 PM on XX / XX." Once the reservation is complete, the generation AI sends a confirmation message to the user. This allows the AI ​​system to automate the user's meal preparation, significantly reducing the amount of work required.

[0056] The schedule adjustment unit can learn the user's past schedule history and predict the most suitable schedule. For example, the generation AI analyzes the user's schedule history for the past year and finds patterns where appointments are concentrated on certain days of the week or time periods. For example, if the user has many appointments every Tuesday night, the unit will prioritize suggesting dates other than Tuesday. This makes it possible to predict the optimal schedule based on the user's past schedule history.

[0057] The schedule adjustment unit can propose the optimal schedule taking into account the user's health data. For example, the schedule adjustment unit analyzes sleep data acquired from the user's wearable device by the generation AI and proposes a schedule that will allow the user to participate in the most refreshed state. For example, it prioritizes proposing plans for the day after the user has had enough sleep. This makes it possible to propose the optimal schedule taking into account the user's health data.

[0058] The schedule adjustment unit can use the emotion estimation function to analyze the user's emotional state and suggest the most relaxing schedule. For example, the schedule adjustment unit uses a generation AI to analyze the user's emotional state in real time and suggest a relaxing schedule. For example, it can prioritize suggestions for days when the user is not feeling stressed. This allows the system to analyze the user's emotional state and suggest the most relaxing schedule.

[0059] The schedule adjustment unit can also consider the schedules of the user's family and friends and propose the optimal date that everyone can participate. For example, the generation AI shares the schedules of the user's family and friends and proposes the optimal date that everyone can participate. For example, it synchronizes everyone's calendars to find common free time. This makes it possible to propose the optimal date that everyone can participate, taking into consideration the schedules of the user's family and friends.

[0060] The schedule adjustment unit works in conjunction with the user's work schedule to suggest a schedule that will reduce the burden on the workforce. For example, the generation AI analyzes the user's work schedule and suggests a schedule that will reduce the burden on the workforce. For example, it prioritizes suggestions for days with fewer meetings or projects. This makes it possible to suggest a schedule that will reduce the burden on the workforce in conjunction with the user's work schedule.

[0061] The schedule adjustment unit can use the emotion estimation function to suggest dates that do not overlap with the event the user is most looking forward to. For example, the schedule adjustment unit uses a generation AI to analyze the user's emotion data and suggest dates that do not overlap with the event the user is most looking forward to. For example, the schedule can be made to avoid the day of a concert the user is looking forward to. This makes it possible to suggest dates that do not overlap with the event the user is looking forward to.

[0062] The restaurant selection unit learns from users' past reviews and ratings and can suggest restaurants that provide the highest level of satisfaction. For example, the generation AI analyzes users' past reviews and ratings and suggests restaurants that provide the highest level of satisfaction. For example, it prioritizes restaurants that users have given high ratings. This makes it possible to suggest restaurants that provide the highest level of satisfaction based on users' past reviews and ratings.

[0063] The restaurant selection unit can take into account real-time congestion conditions and suggest restaurants with short waiting times. For example, the generation AI analyzes real-time congestion conditions and suggests restaurants with short waiting times. For example, it prioritizes suggesting restaurants with less waiting time based on the current congestion situation. This makes it possible to suggest restaurants with short waiting times taking into account real-time congestion conditions.

[0064] The restaurant selection unit can use the emotion estimation function to suggest the best restaurant for the user's current mood. For example, the generation AI analyzes the user's current mood and suggests the best restaurant. For example, if the user feels like relaxing, it will suggest a quiet restaurant. This makes it possible to suggest the best restaurant for the user's current mood.

[0065] The restaurant selection unit can suggest safe restaurants by taking into account the user's dietary restrictions and allergy information. For example, the generation AI analyzes the user's dietary restrictions and allergy information and suggests safe restaurants. For example, it prioritizes gluten-free and vegan restaurants. This allows the system to suggest safe restaurants by taking into account the user's dietary restrictions and allergy information.

[0066] The restaurant selection unit can suggest new restaurants based on the user's past visit history. For example, the generation AI analyzes the user's past visit history and suggests new restaurants. For example, it prioritizes suggesting restaurants that the user has not visited. This makes it possible to suggest new restaurants based on the user's past visit history.

[0067] The restaurant selection unit can use the emotion estimation function to suggest restaurants with the most relaxing atmosphere for the user. For example, the generation AI analyzes the user's emotion data and suggests restaurants with the most relaxing atmosphere. For example, it prioritizes suggesting restaurants with a quiet and calm atmosphere. This allows it to suggest restaurants with the most relaxing atmosphere for the user.

[0068] The reservation part works in conjunction with the user's calendar to prevent overlapping reservations. For example, the reservation part will build a system where the generation AI works in conjunction with the user's calendar to prevent overlapping reservations. For example, it will prevent reservations from being made during times when there are already scheduled appointments. This will prevent overlapping reservations by working in conjunction with the user's calendar.

[0069] The reservation section can take into account the restaurant's cancellation policy and suggest the optimal reservation timing. For example, the generation AI analyzes the restaurant's cancellation policy and suggests the optimal reservation timing. For example, it prioritizes reservations within periods when no cancellation fees are incurred. This allows the reservation section to suggest the optimal reservation timing taking into account the restaurant's cancellation policy.

[0070] The reservation unit can use the emotion estimation function to select the reservation method that causes the least stress to the user. For example, the reservation unit uses a generation AI to analyze the user's emotion data and select the reservation method that causes the least stress. For example, if the user dislikes making a reservation by phone, online reservations will be prioritized. This allows the user to select the reservation method that causes the least stress.

[0071] The reservation unit can suggest the smoothest reservation method based on the user's past reservation history. For example, the generation AI analyzes the user's past reservation history and suggests the smoothest reservation method. For example, it prioritizes suggesting reservation methods that the user has used in the past. This makes it possible to suggest the smoothest reservation method based on the user's past reservation history.

[0072] The reservation unit can suggest the best time to make a reservation by taking into account the restaurant's special events and promotional information. For example, the generation AI analyzes the restaurant's special event information and suggests the best time to make a reservation. For example, the reservation is made on the day when the special event is held. This allows the reservation unit to suggest the best time to make a reservation by taking into account the restaurant's special events and promotional information.

[0073] The reservation unit can use the emotion estimation function to make reservations that do not overlap with the event the user is most looking forward to. For example, the reservation unit uses a generation AI to analyze the user's emotion data and make reservations that do not overlap with the event the user is most looking forward to. For example, the reservation is made on a day that avoids a concert the user is looking forward to. This allows reservations to be made that do not overlap with the event the user is looking forward to.

[0074] The confirmation and reminder section works in conjunction with the user's calendar to optimize the timing of reminders. For example, the generation AI works in conjunction with the user's calendar to optimize the timing of reminders. For example, it sends a reminder one day and one hour before the appointment. This allows the timing of reminders to be optimized in conjunction with the user's calendar.

[0075] The confirmation and reminder section can learn the user's past reminder history and suggest the most effective reminder method. For example, the confirmation and reminder section uses a generation AI to analyze the user's past reminder history and suggest the most effective reminder method. For example, it will prioritize suggesting reminder methods that the user has responded to in the past. This makes it possible to suggest the most effective reminder method based on the user's past reminder history.

[0076] The confirmation and reminder section can use the emotion estimation function to send reminders at the timing when the user is most relaxed. For example, the generation AI analyzes the user's emotion data and sends reminders at the timing when the user is most relaxed. For example, the reminder is sent during the time period when the user is most relaxed. This allows the reminder to be sent at the timing when the user is most relaxed.

[0077] The confirmation and reminder section can also send reminders to the user's family and friends to ensure that no one forgets. For example, the generation AI can share the contact information of the user's family and friends and send reminders to everyone. For example, it can send the same reminder to everyone. This allows reminders to be sent to the user's family and friends to ensure that no one forgets.

[0078] The confirmation and reminder section can customize the content of the reminder and send a message that matches the user's preferences. For example, the generation AI can send a reminder message that matches the user's preferences. For example, it can send a message that includes words or phrases that the user likes. This allows the reminder message to be sent according to the user's preferences.

[0079] The confirmation and reminder section can use the emotion estimation function to adjust reminders so that they do not overlap with the events the user is most looking forward to. For example, the generation AI analyzes the user's emotion data and adjusts reminders so that they do not overlap with the events the user is most looking forward to. For example, the confirmation and reminder section can send reminders before or after the events the user is looking forward to. This allows reminders to be adjusted so that they do not overlap with the events the user is looking forward to.

[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 schedule adjustment unit can also propose optimal dates taking into account the user's hobbies and interests. For example, if the user has plans to attend a specific sporting event or concert, the schedule adjustment unit can suggest dates that avoid those events. Also, if the user wishes to attend an event related to a specific hobby, the schedule adjustment unit can suggest dates that coincide with that event. In this way, the schedule adjustment unit can propose optimal dates taking into account the user's hobbies and interests.

[0082] The restaurant selection unit can also suggest restaurants that offer the newest dishes and menus based on the user's past dining history. For example, it can analyze the menus of restaurants the user has visited in the past and suggest restaurants that offer new dishes that the user has not yet tried. It can also prioritize suggesting restaurants that offer seasonal menus or new menu items. This makes it possible to suggest restaurants that offer new dishes and menus based on the user's dining history.

[0083] The reservation unit can also suggest the smoothest reservation method based on the user's past reservation history. For example, it can analyze the reservation methods the user has used in the past and prioritize the method that allowed for the smoothest reservation completion. It can also suggest the easiest reservation method based on the reservation sites and apps the user has used in the past. This makes it possible to suggest the smoothest reservation method based on the user's past reservation history.

[0084] The confirmation and reminder section can also learn the user's past reminder history and suggest the most effective reminder method. For example, it can analyze reminder methods that the user has responded to in the past and prioritize the most effective method. It can also send more effective reminders by avoiding reminder methods that the user has ignored in the past. This allows it to suggest the most effective reminder method based on the user's past reminder history.

[0085] The schedule adjustment unit can also use the emotion estimation function to analyze the user's stress level and suggest the most relaxing schedule. For example, it can avoid days when the user is feeling stressed and prioritize suggestions for relaxing dates. It can also suggest optimal dates taking into account the environment and circumstances in which the user can relax. This allows the system to analyze the user's stress level and suggest the most relaxing schedule.

[0086] The restaurant selection unit can also use the emotion estimation function to suggest a restaurant that best suits the user's current mood. For example, if the user feels like relaxing, it can suggest a quiet restaurant. If the user prefers lively places, it can also suggest a lively restaurant. In this way, it is possible to suggest a restaurant that best suits the user's current mood.

[0087] The reservation unit can also use the emotion estimation function to select the reservation method that causes the least stress to the user. For example, if the user dislikes making a reservation by phone, it can prioritize online reservations. Also, if the user prefers face-to-face reservations, it can suggest making a reservation directly at the store. This allows the user to select the reservation method that causes the least stress.

[0088] The confirmation and reminder unit can also use the emotion estimation function to send reminders at times when the user is most relaxed. For example, it can send reminders when the user is relaxed. It can also send reminders at times when the user is most relaxed, avoiding busy times. This allows reminders to be sent at times when the user is most relaxed.

[0089] The schedule adjustment unit can also use the emotion estimation function to suggest a date that does not overlap with the event the user is most looking forward to. For example, the schedule can be made to avoid the day of a concert the user is looking forward to. Also, if the user is looking forward to a specific event, the schedule can be adjusted so that it does not overlap with that event. In this way, it is possible to suggest a date that does not overlap with the event the user is looking forward to.

[0090] The restaurant selection unit can also suggest safe restaurants by taking into account the user's dietary restrictions and allergy information. For example, the generation AI can analyze the user's dietary restrictions and allergy information and suggest safe restaurants. For example, it can prioritize suggestions for gluten-free and vegan restaurants. This allows it to suggest safe restaurants by taking into account the user's dietary restrictions and allergy information.

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

[0092] Step 1: The schedule adjustment unit adjusts the optimal schedule based on the schedule information provided by the user. For example, if a user inputs a vague request such as "a weekday evening next week," the generation AI analyzes each user's schedule and proposes a date and time that is convenient for everyone. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI adjusts the optimal schedule based on that prompt. Step 2: The restaurant selection unit selects the optimal restaurant based on the user's budget, location, hobbies, preferences, atmosphere, smoking / non-smoking status, etc. For example, if the user inputs criteria such as "budget of 5,000 yen per person, within a 5-minute walk from the station, I like Japanese food, and I want a restaurant with non-smoking seats," the generation AI will search for restaurants that meet these criteria and suggest several candidates. Step 3: The reservation unit makes a reservation at the selected restaurant. For example, the generation AI might make a reservation in the form of "Please make a reservation for 4 people at 7pm on XX / XX." Once the reservation is complete, the generation AI sends a confirmation message to the user.

[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 the 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 type 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 system equipped with a generative AI, The schedule adjustment department Based on the schedule information provided by the user, we will adjust the optimal schedule. The store selection section is The optimal restaurant is selected based on the user's budget, location, tastes, atmosphere, smoking / non-smoking status, etc. The reservation department is Make a reservation at the selected restaurant A system characterized by:

2. The schedule adjustment unit Taking into account the user's health data, the system proposes the optimal schedule 2. The system of claim 1.

3. The schedule adjustment unit Linking with the user's work schedule to suggest dates that minimize the burden on the work 2. The system of claim 1.

4. The store determination unit Considering real-time congestion status, suggest restaurants with short waiting times 2. The system of claim 1.

5. The store determination unit Suggesting new restaurants based on the user's past visit history 2. The system of claim 1.

6. The reservation unit Consider the restaurant's cancellation policy and suggest the best time to make a reservation 2. The system of claim 1.

7. The reservation unit Considering the restaurant's special events and promotions, we suggest the best time to make a reservation.

2. The system of claim 1.

8. The confirmation and reminder section Sending reminders at times when the user is most relaxed 2. The system of claim 1.

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