system

Abstract

The system according to this embodiment aims to suggest the most suitable restaurant based on the user's past preferences and behavior, and even to make reservations for it. [Solution] The system according to the embodiment comprises a learning unit, a reception unit, a suggestion unit, and a reservation unit. The learning unit learns the user's past preferences and behavior. The reception unit receives information such as area, date and time, number of people, purpose, and preferences as input. The suggestion unit suggests the most suitable restaurant candidates based on the data learned by the learning unit. The reservation unit performs the reservation procedure using web reservations or voice calls for the restaurant selected by the user from among the restaurants suggested by the suggestion unit.

Description

Technical Field

[0002]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to propose an optimal restaurant based on the user's past preferences and behaviors and even make a reservation, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal restaurant based on the user's past preferences and behaviors and even make a reservation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a reception unit, a suggestion unit, and a reservation unit. The learning unit learns the user's past preferences and behavior. The reception unit receives information such as area, date and time, number of people, purpose, and preferences as input. The suggestion unit suggests the most suitable restaurant candidates based on the data learned by the learning unit. The reservation unit performs the reservation procedure using web reservations or voice calls for the restaurant selected by the user from among the restaurants suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable restaurant based on the user's past preferences and behavior, and can even make reservations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The restaurant suggestion and reservation system according to an embodiment of the present invention is a system that learns the user's past preferences and behavior and suggests and reserves personalized restaurant candidates. In this restaurant suggestion and reservation system, the user inputs information such as area, date and time, number of people, purpose, and preferences, and the generating AI learns the user's past preferences and behavior data to suggest the most suitable restaurant candidates. Furthermore, the user selects a restaurant from the suggested restaurants and makes a reservation using web reservation or voice call. For example, the user inputs information such as area, date and time, number of people, purpose, and preferences. At this time, the user can input specific conditions. For example, the user inputs information such as "I want to have a drinking party with 4 people in Shinjuku at 8pm." This information is input to the generating AI. Next, the generating AI learns the user's past preferences and behavior data. Based on past search data, reservation data, and information specified by the user, the generating AI integrates information such as online restaurant ratings, seating information, menus, and prices to suggest the most suitable restaurant candidates. For example, it can suggest similar restaurants by considering the ratings of restaurants the user has visited in the past and their preferred cuisine. Furthermore, the system handles the reservation process for the restaurant selected by the user from the suggested restaurants, using web reservations or voice calls. The generating AI works in conjunction with an automated voice service to attempt reservations even for restaurants that do not support web reservations. For example, if the restaurant selected by the user does not support web reservations, the generating AI will attempt the reservation process via automated voice, and if the reservation is successful, it will notify the user. If the reservation fails, the user will be notified immediately. This reduces the effort and stress that users experience when searching for and reserving restaurants. For example, the organizer of a drinking party, the person in charge of making reservations for business entertainment venues, or someone planning a date can easily find the perfect restaurant and make a reservation smoothly. It also allows restaurants to reduce their reliance on promotions and create an environment where restaurants with high quality and ratings can gain popularity. As a result, the restaurant suggestion and reservation system learns the user's past preferences and behavior, and can suggest and reserve personalized restaurant options.

[0029] The restaurant suggestion and reservation system according to this embodiment comprises a learning unit, a reception unit, a suggestion unit, and a reservation unit. The learning unit learns the user's past preferences and behavior. For example, the learning unit learns the user's past restaurant selection history, order history, and rating history. The learning unit uses generative AI to analyze this data and understand the user's preferences and behavior patterns. For example, the learning unit analyzes the commonalities of restaurants that the user has given high ratings to in the past and learns data to suggest similar restaurants. The reception unit takes input information such as area, date and time, number of people, purpose, and preferences. For example, the reception unit allows the user to input information such as "I want to have a drinking party with 4 people in Shinjuku at 8 PM." The reception unit uses AI to analyze the user's input information and provides the necessary information to the generative AI. The suggestion unit suggests the most suitable restaurant candidates based on the data learned by the learning unit. The suggestion unit uses generative AI to integrate information such as online restaurant ratings, seating information, menus, and prices based on the user's preferences and behavior data, and suggests the most suitable restaurant candidates. For example, the suggestion unit can suggest similar restaurants based on the user's past restaurant reviews and preferred cuisine. The reservation unit then makes reservations for the restaurants selected by the user from those suggested by the suggestion unit, using web reservations or voice calls. The reservation unit uses AI to automatically make reservations for the restaurants selected by the user. For example, if the restaurant selected by the user does not support web reservations, the reservation unit will make the reservation in cooperation with an automated voice service. As a result, the restaurant suggestion and reservation system according to this embodiment can learn the user's past preferences and behavior, and suggest and reserve personalized restaurant candidates.

[0030] The learning unit learns the user's past preferences and behavior. Specifically, it meticulously collects data such as the user's past restaurant selection history, order history, and rating history, and analyzes this data. For example, if a user tends to prefer certain dishes or atmospheres, the learning unit extracts that pattern and uses it for future recommendations. The learning unit uses generative AI to analyze this data in an advanced way and understand the user's preferences and behavioral patterns. The generative AI utilizes natural language processing and machine learning algorithms to analyze the user's rating comments and review content, identifying the types of dishes and service characteristics that the user particularly likes. For example, it analyzes the commonalities of restaurants that the user has given high ratings to in the past and learns data to suggest similar restaurants. Furthermore, the learning unit also considers the user's visit frequency, time of day, and behavioral patterns related to specific events and seasons to build a more accurate preference model. As a result, the learning unit can comprehensively understand the user's diverse preferences and behavioral patterns and provide a foundation for making personalized recommendations.

[0031] The reception desk takes in information such as area, date and time, number of people, purpose, and preferences. Users can input specific information, such as, "I'd like to have a drinking party with four people in Shinjuku at 8 PM." The reception desk efficiently collects this information and analyzes it using AI. The AI ​​analyzes the user's input information and provides the necessary information to the generating AI. For example, based on the area information entered by the user, it generates a list of restaurants in that area and checks for availability based on the date, time, and number of people. It also takes into account the user's purpose and preferences and prioritizes listing restaurants with specific cuisine genres or atmospheres. Furthermore, the reception desk refers to the user's past input history and preference data to prepare suggestions that reflect the characteristics of restaurants the user prefers. As a result, the reception desk can quickly and accurately collect information that meets the user's needs and provide it to the suggestion and reservation departments.

[0032] The suggestion unit proposes optimal restaurant candidates based on data learned by the learning unit. Using generative AI, the suggestion unit integrates information such as online restaurant ratings, seating information, menus, and prices based on the user's preferences and behavioral data to propose optimal restaurant candidates. Specifically, the generative AI analyzes the user's past rating and selection history and selects restaurants considering the type of cuisine, price range, and atmosphere the user prefers. For example, it can suggest similar restaurants by considering the ratings and preferred cuisine of restaurants the user has visited in the past. Furthermore, the suggestion unit collects online restaurant information that is updated in real time and makes suggestions that reflect the latest ratings and availability information. The suggestion unit combines the user's input information and learning data to generate a list of optimal restaurants for the user and presents it to the user. This allows the suggestion unit to quickly and accurately propose personalized restaurant candidates that meet the user's preferences and needs.

[0033] The reservation department handles reservations for restaurants selected by the user from those suggested by the suggestion department, using web reservations or voice calls. The reservation department uses AI to automatically process reservations for the restaurants selected by the user. Specifically, if the restaurant selected by the user supports web reservations, the reservation department completes the reservation through the online reservation system. If the restaurant does not support web reservations, the reservation department works in conjunction with an automated voice service to process the reservation. The automated voice service calls the restaurant based on the user's reservation information and transmits the necessary reservation information. Furthermore, the reservation department can also automatically handle procedures such as reservation confirmation, modification, and cancellation. For example, if the user wants to change their reservation details, the reservation department will inform the restaurant of the changes and reconfirm the reservation. The reservation department also sends a reservation confirmation notification to the user, providing a link to check the reservation details and other information. In this way, the reservation department can process reservations for the restaurants selected by the user quickly and accurately, improving user convenience.

[0034] The suggestion support unit can suggest recommendations even when all the elements are unknown. For example, even if the user does not input information such as date, time, number of people, purpose, or preferences, the suggestion support unit will use generative AI to suggest the most suitable restaurant. Using generative AI, the suggestion support unit integrates information such as online restaurant ratings, seating information, menus, and prices based on the user's past preferences and behavioral data, and suggests the most suitable restaurant candidates. For example, the suggestion support unit can suggest similar restaurants by considering the ratings of restaurants the user has visited in the past and their preferred cuisine. This allows the system to suggest appropriate restaurants to the user even when all the elements are unknown.

[0035] The voice reservation system can attempt to make reservations even at restaurants that do not support online reservations, by working in conjunction with automated voice services. For example, if the restaurant selected by the user does not support online reservations, the voice reservation system will use the automated voice service to initiate the reservation process. The voice reservation system uses AI to automatically initiate the reservation process for the restaurant selected by the user. For example, the voice reservation system uses an IVR (Interactive Voice Response) system to input the user's reservation information and complete the reservation process. This allows it to attempt to make reservations even at restaurants that do not support online reservations.

[0036] The notification unit can notify the user if a reservation fails. For example, the notification unit will notify the user if the restaurant selected by the user is fully booked or if a communication error occurs. The notification unit uses AI to monitor the reservation status and immediately notifies the user if a reservation fails. For example, the notification unit will inform the user of the reservation failure using push notifications or email notifications. This allows the user to be notified if a reservation fails.

[0037] The suggestion department can integrate past search data, reservation data, online restaurant ratings, seating information, menus, prices, and other information to make recommendations. For example, the suggestion department can suggest the most suitable restaurants based on keywords the user has previously searched for, reservation dates and times, and information about the restaurants they have reserved. The suggestion department uses generative AI to analyze this data and understand the user's preferences and behavioral patterns. For example, the suggestion department can analyze the commonalities of restaurants that users have previously given high ratings to and suggest similar restaurants. This allows for improved accuracy of recommendations by integrating past data and online information.

[0038] The reservation department can make reservations for restaurants selected by the user from the suggested restaurants. For example, the reservation department can automatically make reservations for restaurants selected by the user. The reservation department uses AI to make reservations for restaurants selected by the user via web or voice call. For example, if the restaurant selected by the user does not support web reservations, the reservation department will work with an automated voice service to make the reservation. This allows the user to make reservations for restaurants of their choice.

[0039] The learning unit can learn patterns of rating fluctuations by analyzing users' past restaurant ratings in detail during the learning process. For example, the learning unit can analyze the common characteristics of restaurants that users have given high ratings to and learn similar restaurants. The learning unit uses generative AI to analyze users' past restaurant ratings in detail and learn patterns of rating fluctuations. For example, the learning unit can analyze the characteristics of restaurants that users have given low ratings to and learn restaurants to avoid. In this way, by analyzing users' past ratings, it is possible to learn patterns of rating fluctuations.

[0040] The learning unit can optimize its learning algorithm during training by considering the user's meal frequency and time patterns. For example, the learning unit learns restaurant data in accordance with the times when the user frequently eats. The learning unit uses generative AI to optimize the learning algorithm by considering the user's meal frequency and time patterns. For example, the learning unit prioritizes learning data from restaurants that the user visits on specific days of the week. By optimizing the learning algorithm to consider the user's meal frequency and time, it can provide more appropriate suggestions.

[0041] The learning unit can analyze users' social media activity during training and add relevant restaurant information to its training data. For example, the learning unit learns data on restaurants that users have checked into on social media. The learning unit uses generative AI to analyze users' social media activity and add relevant restaurant information to its training data. For example, the learning unit learns data on restaurants that users have given high ratings to on social media. This allows the learning unit to add relevant restaurant information to its training data by analyzing users' social media activity.

[0042] The learning unit can learn region-specific restaurant information by considering the user's geographical location during training. For example, the learning unit prioritizes learning restaurant information in areas the user frequently visits. The learning unit uses generative AI to learn region-specific restaurant information while considering the user's geographical location. For example, the learning unit learns restaurant information in areas the user has visited while traveling. This allows the learning unit to learn region-specific restaurant information by considering the user's geographical location.

[0043] The input system can provide input assistance by referring to the user's past input history during input. For example, the input system can automatically display areas and dates that the user has frequently entered in the past as suggestions. The input system uses AI to provide input assistance by referring to the user's past input history. For example, the input system prioritizes suggesting input methods that the user has used in the past (voice, text, etc.). In this way, input assistance can be provided by referring to the user's past input history.

[0044] The reception desk can dynamically change input fields according to the user's current situation during input. For example, if the user is on the move, the reception desk automatically acquires the user's current location and sets the area. The reception desk uses AI to dynamically change input fields according to the user's current situation. For example, if the user is in a hurry, the reception desk displays only the minimum necessary input fields. This allows for more appropriate input by dynamically changing input fields according to the user's current situation.

[0045] The reception unit can provide the optimal input method by considering the user's device information during input. For example, if the user is using a smartphone, the reception unit will provide an input method optimized for touch operation. The reception unit uses AI to provide the optimal input method by considering the user's device information. For example, if the user is using a tablet, the reception unit will provide an input method optimized for the large screen. In this way, the system can provide the optimal input method by considering the user's device information.

[0046] The reception desk can simplify input by analyzing the user's past behavior patterns during the input process. For example, the reception desk can automatically display areas and times that the user has frequently visited in the past as suggestions. The reception desk uses generative AI to analyze the user's past behavior patterns and simplify input. For example, the reception desk predicts and suggests areas and times that the user might use during a specific time period based on their past behavior patterns. In this way, input can be simplified by analyzing the user's past behavior patterns.

[0047] The suggestion function can improve the accuracy of its suggestions by considering the user's past restaurant ratings. For example, it can analyze the common characteristics of restaurants that users have given high ratings to and suggest similar restaurants. The suggestion function uses generative AI to improve the accuracy of its suggestions by considering the user's past restaurant ratings. For example, it can analyze the characteristics of restaurants that users have given low ratings to and suggest restaurants to avoid. In this way, the accuracy of suggestions can be improved by considering the user's past restaurant ratings.

[0048] The suggestion function can optimize its suggestion algorithm by considering the user's meal frequency and time patterns. For example, the suggestion function can suggest restaurants based on the times of day the user frequently eats. The suggestion function uses generative AI to optimize the suggestion algorithm by considering the user's meal frequency and time patterns. For example, the suggestion function can prioritize suggesting restaurants that the user visits on specific days of the week. By optimizing the suggestion algorithm by considering the user's meal frequency and time, it can provide more appropriate suggestions.

[0049] The suggestion department can analyze a user's social media activity and suggest relevant restaurants when making a suggestion. For example, the suggestion department can suggest restaurants that the user has checked into on social media. The suggestion department uses generative AI to analyze a user's social media activity and suggest relevant restaurants. For example, the suggestion department can suggest restaurants that the user has given high ratings to on social media. In this way, by analyzing a user's social media activity, it is possible to suggest relevant restaurants.

[0050] The suggestion function can propose restaurants specific to a user's region, taking into account the user's geographical location. For example, it can suggest restaurants in areas the user frequently visits. The suggestion function uses generative AI to propose restaurants specific to a user's region, taking into account the user's geographical location. For example, it can suggest restaurants in areas the user has visited while traveling. In this way, by considering the user's geographical location, it can propose restaurants specific to a user's region.

[0051] The reservation system can select the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation system may prioritize suggesting reservation methods that the user has frequently used in the past. The reservation system uses generative AI to select the optimal reservation method by referring to the user's past reservation history. For example, the reservation system may predict and suggest a reservation method to be used for a specific time slot based on the user's past reservation history. In this way, the optimal reservation method can be selected by referring to the user's past reservation history.

[0052] The reservation system can dynamically modify the reservation process based on the user's current situation. For example, if the user is on the move, the system will automatically acquire their current location and proceed with the reservation. The reservation system uses a generation AI to dynamically modify the reservation process based on the user's current situation. For example, if the user is in a hurry, the system will display only the minimum necessary reservation items. This allows for more appropriate reservations by dynamically modifying the reservation process according to the user's current situation.

[0053] The reservation system can provide the optimal reservation method by considering the user's device information at the time of reservation. For example, if the user is using a smartphone, the reservation system will provide a reservation method optimized for touch operation. The reservation system uses generational AI to provide the optimal reservation method by considering the user's device information. For example, if the user is using a tablet, the reservation system will provide a reservation method optimized for a large screen. In this way, the system can provide the optimal reservation method by considering the user's device information.

[0054] The reservation system can simplify the reservation process by analyzing the user's past behavior patterns. For example, the reservation system can automatically display restaurants that the user has frequently visited in the past as suggestions. The reservation system uses generative AI to analyze the user's past behavior patterns and simplify the reservation process. For example, the reservation system can predict and suggest reservation methods to use for specific time slots based on the user's past behavior patterns. In this way, the reservation process can be simplified by analyzing the user's past behavior patterns.

[0055] The recommendation support unit can improve the accuracy of recommendations by thoroughly analyzing the user's past preferences when making recommendations. For example, the recommendation support unit analyzes the common characteristics of restaurants that users have given high ratings to and recommends similar restaurants. The recommendation support unit uses generative AI to thoroughly analyze the user's past preferences and improve the accuracy of recommendations. For example, the recommendation support unit analyzes the characteristics of restaurants that users have given low ratings to and recommends restaurants to avoid. In this way, the accuracy of recommendations can be improved by thoroughly analyzing the user's past preferences.

[0056] The recommendation support unit can analyze the user's social media activity when making recommendations and suggest relevant restaurants. For example, the recommendation support unit can recommend restaurants that the user has checked into on social media. The recommendation support unit uses generative AI to analyze the user's social media activity and recommend relevant restaurants. For example, the recommendation support unit can recommend restaurants that the user has given high ratings to on social media. In this way, by analyzing the user's social media activity, it can recommend relevant restaurants.

[0057] The voice reservation unit can select the optimal voice reservation method by referring to the user's past reservation history when a voice reservation is made. For example, the voice reservation unit will prioritize suggesting voice reservation methods that the user has frequently used in the past. The voice reservation unit uses generation AI to select the optimal voice reservation method by referring to the user's past reservation history. For example, the voice reservation unit will predict and suggest a voice reservation method to be used during a specific time period based on the user's past reservation history. In this way, the optimal voice reservation method can be selected by referring to the user's past reservation history.

[0058] The voice reservation unit can dynamically change the voice reservation procedure according to the user's current situation when a reservation is made by voice. For example, if the user is on the move, the voice reservation unit will automatically acquire the user's current location and proceed with the voice reservation procedure. The voice reservation unit uses a generation AI to dynamically change the voice reservation procedure according to the user's current situation. For example, if the user is in a hurry, the voice reservation unit will display only the minimum necessary reservation items. In this way, by dynamically changing the voice reservation procedure according to the user's current situation, more appropriate reservations can be made.

[0059] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize suggesting notification methods that the user has frequently used in the past. The notification unit uses generative AI to select the optimal notification method by referring to the user's past notification history. For example, the notification unit predicts and suggests notification methods to be used during specific time periods based on the user's past notification history. In this way, the optimal notification method can be selected by referring to the user's past notification history.

[0060] The notification unit can analyze the user's past behavior patterns and simplify notifications when they are sent. For example, the notification unit can automatically display notification methods that the user has frequently used in the past as suggestions. The notification unit uses generative AI to analyze the user's past behavior patterns and simplify notifications. For example, the notification unit can predict and suggest notification methods to be used during specific time periods based on the user's past behavior patterns. In this way, notifications can be simplified by analyzing the user's past behavior patterns.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The recommendation team can suggest restaurants based on the user's current health status, in addition to their past preferences and behavioral data. For example, if a user is using a health management app, the system can suggest restaurants that consider calories and nutritional balance based on that data. Furthermore, if a user has specific allergies, the system can suggest allergy-friendly restaurants based on that information. Additionally, if a user is on a diet, the system can prioritize suggesting restaurants that offer low-calorie or healthy menu options.

[0063] The suggestion support unit can estimate whether a user is seeking a new experience based on their past preferences and behavioral data, and can also suggest new restaurants. For example, it can suggest restaurants in areas the user has never visited before. It can also suggest restaurants in culinary genres the user has never tried. Furthermore, if a user is interested in a particular event or festival, it can suggest restaurants related to that event.

[0064] The recommendation system can suggest restaurants that take into account not only the user's past preferences and behavioral data, but also the preferences of the user's friends and family. For example, if a user is dining with friends, the system can suggest a restaurant that everyone will enjoy by considering the friends' past preferences and behavioral data. Similarly, if a user is dining with family, the system can suggest family-friendly restaurants that take into account the preferences of all family members. Furthermore, if a user is dining with a specific group, the system can suggest a restaurant that the entire group can enjoy by considering the group's preferences.

[0065] The reservation system can process reservations considering the user's current schedule in addition to their past reservation history. For example, if a user is using a calendar app, the system can suggest the optimal reservation time based on that schedule. It can also process reservations considering a user's schedule if they plan to have a meal after a specific event or meeting. Furthermore, it can adjust the reservation process to accommodate sudden changes in the user's plans.

[0066] The recommendation team can suggest restaurants based on the user's past preferences and behavioral data, as well as considering the current weather and season. For example, during cold weather, they can suggest restaurants that serve hot food. On rainy days, they can suggest indoor restaurants. Furthermore, on hot summer days, they can suggest restaurants that serve cold drinks and desserts.

[0067] The recommendation system can suggest restaurants based on the user's past preferences and behavioral data, as well as their current budget. For example, if a user has set a budget, the system can suggest the best restaurant within that budget. It can also suggest restaurants where users can use specific discounts or coupons. Furthermore, if a user prefers a particular payment method, the system can suggest restaurants that accept that method.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The learning unit learns the user's past preferences and behavior. For example, it learns the user's past restaurant selection history, order history, and rating history, and analyzes this data using generative AI. This helps to understand the user's preferences and behavioral patterns and learn data to suggest similar restaurants. Step 2: The reception desk inputs information such as area, date and time, number of people, purpose, and preferences. For example, a user can input information such as "I want to have a drinking party with 4 people in Shinjuku at 8 PM." The reception desk uses AI to analyze the user's input information and provides the necessary information to the generating AI. Step 3: The suggestion unit proposes the most suitable restaurant candidates based on the data learned by the learning unit. Using generative AI, it integrates information such as online restaurant ratings, seating information, menus, and prices based on the user's preferences and behavioral data to propose the most suitable restaurant candidates. For example, it can suggest similar restaurants by considering the ratings of restaurants the user has visited in the past and their preferred cuisine. Step 4: The reservation department makes reservations for the restaurants selected by the user from those suggested by the suggestion department, using web reservations or voice calls. The reservation department uses AI to automatically make reservations for the restaurants selected by the user. For example, if the restaurant selected by the user does not support web reservations, the reservation department will make the reservation in conjunction with an automated voice service.

[0070] (Example of form 2) The restaurant suggestion and reservation system according to an embodiment of the present invention is a system that learns the user's past preferences and behavior and suggests and reserves personalized restaurant candidates. In this restaurant suggestion and reservation system, the user inputs information such as area, date and time, number of people, purpose, and preferences, and the generating AI learns the user's past preferences and behavior data to suggest the most suitable restaurant candidates. Furthermore, the user selects a restaurant from the suggested restaurants and makes a reservation using web reservation or voice call. For example, the user inputs information such as area, date and time, number of people, purpose, and preferences. At this time, the user can input specific conditions. For example, the user inputs information such as "I want to have a drinking party with 4 people in Shinjuku at 8pm." This information is input to the generating AI. Next, the generating AI learns the user's past preferences and behavior data. Based on past search data, reservation data, and information specified by the user, the generating AI integrates information such as online restaurant ratings, seating information, menus, and prices to suggest the most suitable restaurant candidates. For example, it can suggest similar restaurants by considering the ratings of restaurants the user has visited in the past and their preferred cuisine. Furthermore, the system handles the reservation process for the restaurant selected by the user from the suggested restaurants, using web reservations or voice calls. The generating AI works in conjunction with an automated voice service to attempt reservations even for restaurants that do not support web reservations. For example, if the restaurant selected by the user does not support web reservations, the generating AI will attempt the reservation process via automated voice, and if the reservation is successful, it will notify the user. If the reservation fails, the user will be notified immediately. This reduces the effort and stress that users experience when searching for and reserving restaurants. For example, the organizer of a drinking party, the person in charge of making reservations for business entertainment venues, or someone planning a date can easily find the perfect restaurant and make a reservation smoothly. It also allows restaurants to reduce their reliance on promotions and create an environment where restaurants with high quality and ratings can gain popularity. As a result, the restaurant suggestion and reservation system learns the user's past preferences and behavior, and can suggest and reserve personalized restaurant options.

[0071] The restaurant suggestion and reservation system according to this embodiment comprises a learning unit, a reception unit, a suggestion unit, and a reservation unit. The learning unit learns the user's past preferences and behavior. For example, the learning unit learns the user's past restaurant selection history, order history, and rating history. The learning unit uses generative AI to analyze this data and understand the user's preferences and behavior patterns. For example, the learning unit analyzes the commonalities of restaurants that the user has given high ratings to in the past and learns data to suggest similar restaurants. The reception unit takes input information such as area, date and time, number of people, purpose, and preferences. For example, the reception unit allows the user to input information such as "I want to have a drinking party with 4 people in Shinjuku at 8 PM." The reception unit uses AI to analyze the user's input information and provides the necessary information to the generative AI. The suggestion unit suggests the most suitable restaurant candidates based on the data learned by the learning unit. The suggestion unit uses generative AI to integrate information such as online restaurant ratings, seating information, menus, and prices based on the user's preferences and behavior data, and suggests the most suitable restaurant candidates. For example, the suggestion unit can suggest similar restaurants based on the user's past restaurant reviews and preferred cuisine. The reservation unit then makes reservations for the restaurants selected by the user from those suggested by the suggestion unit, using web reservations or voice calls. The reservation unit uses AI to automatically make reservations for the restaurants selected by the user. For example, if the restaurant selected by the user does not support web reservations, the reservation unit will make the reservation in cooperation with an automated voice service. As a result, the restaurant suggestion and reservation system according to this embodiment can learn the user's past preferences and behavior, and suggest and reserve personalized restaurant candidates.

[0072] The learning unit learns the user's past preferences and behavior. Specifically, it meticulously collects data such as the user's past restaurant selection history, order history, and rating history, and analyzes this data. For example, if a user tends to prefer certain dishes or atmospheres, the learning unit extracts that pattern and uses it for future recommendations. The learning unit uses generative AI to analyze this data in an advanced way and understand the user's preferences and behavioral patterns. The generative AI utilizes natural language processing and machine learning algorithms to analyze the user's rating comments and review content, identifying the types of dishes and service characteristics that the user particularly likes. For example, it analyzes the commonalities of restaurants that the user has given high ratings to in the past and learns data to suggest similar restaurants. Furthermore, the learning unit also considers the user's visit frequency, time of day, and behavioral patterns related to specific events and seasons to build a more accurate preference model. As a result, the learning unit can comprehensively understand the user's diverse preferences and behavioral patterns and provide a foundation for making personalized recommendations.

[0073] The reception desk takes in information such as area, date and time, number of people, purpose, and preferences. Users can input specific information, such as, "I'd like to have a drinking party with four people in Shinjuku at 8 PM." The reception desk efficiently collects this information and analyzes it using AI. The AI ​​analyzes the user's input information and provides the necessary information to the generating AI. For example, based on the area information entered by the user, it generates a list of restaurants in that area and checks for availability based on the date, time, and number of people. It also takes into account the user's purpose and preferences and prioritizes listing restaurants with specific cuisine genres or atmospheres. Furthermore, the reception desk refers to the user's past input history and preference data to prepare suggestions that reflect the characteristics of restaurants the user prefers. As a result, the reception desk can quickly and accurately collect information that meets the user's needs and provide it to the suggestion and reservation departments.

[0074] The suggestion unit proposes optimal restaurant candidates based on data learned by the learning unit. Using generative AI, the suggestion unit integrates information such as online restaurant ratings, seating information, menus, and prices based on the user's preferences and behavioral data to propose optimal restaurant candidates. Specifically, the generative AI analyzes the user's past rating and selection history and selects restaurants considering the type of cuisine, price range, and atmosphere the user prefers. For example, it can suggest similar restaurants by considering the ratings and preferred cuisine of restaurants the user has visited in the past. Furthermore, the suggestion unit collects online restaurant information that is updated in real time and makes suggestions that reflect the latest ratings and availability information. The suggestion unit combines the user's input information and learning data to generate a list of optimal restaurants for the user and presents it to the user. This allows the suggestion unit to quickly and accurately propose personalized restaurant candidates that meet the user's preferences and needs.

[0075] The reservation department handles reservations for restaurants selected by the user from those suggested by the suggestion department, using web reservations or voice calls. The reservation department uses AI to automatically process reservations for the restaurants selected by the user. Specifically, if the restaurant selected by the user supports web reservations, the reservation department completes the reservation through the online reservation system. If the restaurant does not support web reservations, the reservation department works in conjunction with an automated voice service to process the reservation. The automated voice service calls the restaurant based on the user's reservation information and transmits the necessary reservation information. Furthermore, the reservation department can also automatically handle procedures such as reservation confirmation, modification, and cancellation. For example, if the user wants to change their reservation details, the reservation department will inform the restaurant of the changes and reconfirm the reservation. The reservation department also sends a reservation confirmation notification to the user, providing a link to check the reservation details and other information. In this way, the reservation department can process reservations for the restaurants selected by the user quickly and accurately, improving user convenience.

[0076] The suggestion support unit can suggest recommendations even when all the elements are unknown. For example, even if the user does not input information such as date, time, number of people, purpose, or preferences, the suggestion support unit will use generative AI to suggest the most suitable restaurant. Using generative AI, the suggestion support unit integrates information such as online restaurant ratings, seating information, menus, and prices based on the user's past preferences and behavioral data, and suggests the most suitable restaurant candidates. For example, the suggestion support unit can suggest similar restaurants by considering the ratings of restaurants the user has visited in the past and their preferred cuisine. This allows the system to suggest appropriate restaurants to the user even when all the elements are unknown.

[0077] The voice reservation system can attempt to make reservations even at restaurants that do not support online reservations, by working in conjunction with automated voice services. For example, if the restaurant selected by the user does not support online reservations, the voice reservation system will use the automated voice service to initiate the reservation process. The voice reservation system uses AI to automatically initiate the reservation process for the restaurant selected by the user. For example, the voice reservation system uses an IVR (Interactive Voice Response) system to input the user's reservation information and complete the reservation process. This allows it to attempt to make reservations even at restaurants that do not support online reservations.

[0078] The notification unit can notify the user if a reservation fails. For example, the notification unit will notify the user if the restaurant selected by the user is fully booked or if a communication error occurs. The notification unit uses AI to monitor the reservation status and immediately notifies the user if a reservation fails. For example, the notification unit will inform the user of the reservation failure using push notifications or email notifications. This allows the user to be notified if a reservation fails.

[0079] The suggestion department can integrate past search data, reservation data, online restaurant ratings, seating information, menus, prices, and other information to make recommendations. For example, the suggestion department can suggest the most suitable restaurants based on keywords the user has previously searched for, reservation dates and times, and information about the restaurants they have reserved. The suggestion department uses generative AI to analyze this data and understand the user's preferences and behavioral patterns. For example, the suggestion department can analyze the commonalities of restaurants that users have previously given high ratings to and suggest similar restaurants. This allows for improved accuracy of recommendations by integrating past data and online information.

[0080] The reservation department can make reservations for restaurants selected by the user from the suggested restaurants. For example, the reservation department can automatically make reservations for restaurants selected by the user. The reservation department uses AI to make reservations for restaurants selected by the user via web or voice call. For example, if the restaurant selected by the user does not support web reservations, the reservation department will work with an automated voice service to make the reservation. This allows the user to make reservations for restaurants of their choice.

[0081] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is feeling stressed, the learning unit will prioritize learning data on relaxing restaurants. The learning unit uses generative AI to estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is having fun, the learning unit will prioritize learning data on highly entertaining restaurants. By selecting training data based on the user's emotions, more appropriate data can be learned. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The learning unit can learn patterns of rating fluctuations by analyzing users' past restaurant ratings in detail during the learning process. For example, the learning unit can analyze the common characteristics of restaurants that users have given high ratings to and learn similar restaurants. The learning unit uses generative AI to analyze users' past restaurant ratings in detail and learn patterns of rating fluctuations. For example, the learning unit can analyze the characteristics of restaurants that users have given low ratings to and learn restaurants to avoid. In this way, by analyzing users' past ratings, it is possible to learn patterns of rating fluctuations.

[0083] The learning unit can optimize its learning algorithm during training by considering the user's meal frequency and time patterns. For example, the learning unit learns restaurant data in accordance with the times when the user frequently eats. The learning unit uses generative AI to optimize the learning algorithm by considering the user's meal frequency and time patterns. For example, the learning unit prioritizes learning data from restaurants that the user visits on specific days of the week. By optimizing the learning algorithm to consider the user's meal frequency and time, it can provide more appropriate suggestions.

[0084] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit will reduce the learning frequency to alleviate the burden. The learning unit uses generative AI to estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit will increase the learning frequency to collect more detailed data. This reduces the user's burden by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The learning unit can analyze users' social media activity during training and add relevant restaurant information to its training data. For example, the learning unit learns data on restaurants that users have checked into on social media. The learning unit uses generative AI to analyze users' social media activity and add relevant restaurant information to its training data. For example, the learning unit learns data on restaurants that users have given high ratings to on social media. This allows the learning unit to add relevant restaurant information to its training data by analyzing users' social media activity.

[0086] The learning unit can learn region-specific restaurant information by considering the user's geographical location during training. For example, the learning unit prioritizes learning restaurant information in areas the user frequently visits. The learning unit uses generative AI to learn region-specific restaurant information while considering the user's geographical location. For example, the learning unit learns restaurant information in areas the user has visited while traveling. This allows the learning unit to learn region-specific restaurant information by considering the user's geographical location.

[0087] The reception desk can estimate the user's emotions and adjust the input interface based on those emotions. For example, if the user is stressed, the reception desk will provide a simple interface and minimize the input steps. The reception desk uses AI to estimate the user's emotions and adjust the interface based on those emotions. For example, if the user is relaxed, the reception desk will provide detailed input options and suggest customizable input methods. This reduces the user's burden by adjusting the interface based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The input system can provide input assistance by referring to the user's past input history during input. For example, the input system can automatically display areas and dates that the user has frequently entered in the past as suggestions. The input system uses AI to provide input assistance by referring to the user's past input history. For example, the input system prioritizes suggesting input methods that the user has used in the past (voice, text, etc.). In this way, input assistance can be provided by referring to the user's past input history.

[0089] The reception desk can dynamically change input fields according to the user's current situation during input. For example, if the user is on the move, the reception desk automatically acquires the user's current location and sets the area. The reception desk uses AI to dynamically change input fields according to the user's current situation. For example, if the user is in a hurry, the reception desk displays only the minimum necessary input fields. This allows for more appropriate input by dynamically changing input fields according to the user's current situation.

[0090] The reception desk can estimate the user's emotions and prioritize inputs based on those emotions. For example, if the user is stressed, the reception desk will prioritize displaying important input items. The reception desk uses AI to estimate the user's emotions and prioritize inputs based on those emotions. For example, if the user is relaxed, the reception desk will provide detailed input options. This reduces the user's burden by prioritizing inputs based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The reception unit can provide the optimal input method by considering the user's device information during input. For example, if the user is using a smartphone, the reception unit will provide an input method optimized for touch operation. The reception unit uses AI to provide the optimal input method by considering the user's device information. For example, if the user is using a tablet, the reception unit will provide an input method optimized for the large screen. In this way, the system can provide the optimal input method by considering the user's device information.

[0092] The reception desk can simplify input by analyzing the user's past behavior patterns during the input process. For example, the reception desk can automatically display areas and times that the user has frequently visited in the past as suggestions. The reception desk uses generative AI to analyze the user's past behavior patterns and simplify input. For example, the reception desk predicts and suggests areas and times that the user might use during a specific time period based on their past behavior patterns. In this way, input can be simplified by analyzing the user's past behavior patterns.

[0093] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present simple and easy-to-understand suggestions. The suggestion function uses generative AI to estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will present suggestions that include detailed information. By adjusting the way suggestions are presented based on the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The suggestion function can improve the accuracy of its suggestions by considering the user's past restaurant ratings. For example, it can analyze the common characteristics of restaurants that users have given high ratings to and suggest similar restaurants. The suggestion function uses generative AI to improve the accuracy of its suggestions by considering the user's past restaurant ratings. For example, it can analyze the characteristics of restaurants that users have given low ratings to and suggest restaurants to avoid. In this way, the accuracy of suggestions can be improved by considering the user's past restaurant ratings.

[0095] The suggestion function can optimize its suggestion algorithm by considering the user's meal frequency and time patterns. For example, the suggestion function can suggest restaurants based on the times of day the user frequently eats. The suggestion function uses generative AI to optimize the suggestion algorithm by considering the user's meal frequency and time patterns. For example, the suggestion function can prioritize suggesting restaurants that the user visits on specific days of the week. By optimizing the suggestion algorithm by considering the user's meal frequency and time, it can provide more appropriate suggestions.

[0096] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion function will prioritize suggesting relaxing restaurants. The suggestion function uses generative AI to estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is having fun, the suggestion function will prioritize suggesting restaurants with high entertainment value. This reduces the user's burden by prioritizing suggestions based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The suggestion department can analyze a user's social media activity and suggest relevant restaurants when making a suggestion. For example, the suggestion department can suggest restaurants that the user has checked into on social media. The suggestion department uses generative AI to analyze a user's social media activity and suggest relevant restaurants. For example, the suggestion department can suggest restaurants that the user has given high ratings to on social media. In this way, by analyzing a user's social media activity, it is possible to suggest relevant restaurants.

[0098] The suggestion function can propose restaurants specific to a user's region, taking into account the user's geographical location. For example, it can suggest restaurants in areas the user frequently visits. The suggestion function uses generative AI to propose restaurants specific to a user's region, taking into account the user's geographical location. For example, it can suggest restaurants in areas the user has visited while traveling. In this way, by considering the user's geographical location, it can propose restaurants specific to a user's region.

[0099] The reservation system can estimate the user's emotions and adjust the reservation process based on those emotions. For example, if the user is stressed, the reservation system will provide a simple and quick reservation process. The reservation system uses AI to estimate the user's emotions and adjust the reservation process based on those emotions. For example, if the user is relaxed, the reservation system will provide detailed reservation options and suggest a customizable reservation method. This reduces the user's burden by adjusting the reservation process based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The reservation system can select the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation system may prioritize suggesting reservation methods that the user has frequently used in the past. The reservation system uses generative AI to select the optimal reservation method by referring to the user's past reservation history. For example, the reservation system may predict and suggest a reservation method to be used for a specific time slot based on the user's past reservation history. In this way, the optimal reservation method can be selected by referring to the user's past reservation history.

[0101] The reservation system can dynamically modify the reservation process based on the user's current situation. For example, if the user is on the move, the system will automatically acquire their current location and proceed with the reservation. The reservation system uses a generation AI to dynamically modify the reservation process based on the user's current situation. For example, if the user is in a hurry, the system will display only the minimum necessary reservation items. This allows for more appropriate reservations by dynamically modifying the reservation process according to the user's current situation.

[0102] The reservation system can estimate the user's emotions and prioritize reservations based on those emotions. For example, if the user is feeling stressed, the reservation system will prioritize displaying important reservation items. The reservation system uses AI to estimate the user's emotions and prioritize reservations based on those emotions. For example, if the user is relaxed, the reservation system will provide detailed reservation options. This reduces the user's burden by prioritizing reservations based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The reservation system can provide the optimal reservation method by considering the user's device information at the time of reservation. For example, if the user is using a smartphone, the reservation system will provide a reservation method optimized for touch operation. The reservation system uses generational AI to provide the optimal reservation method by considering the user's device information. For example, if the user is using a tablet, the reservation system will provide a reservation method optimized for a large screen. In this way, the system can provide the optimal reservation method by considering the user's device information.

[0104] The reservation system can simplify the reservation process by analyzing the user's past behavior patterns. For example, the reservation system can automatically display restaurants that the user has frequently visited in the past as suggestions. The reservation system uses generative AI to analyze the user's past behavior patterns and simplify the reservation process. For example, the reservation system can predict and suggest reservation methods to use for specific time slots based on the user's past behavior patterns. In this way, the reservation process can be simplified by analyzing the user's past behavior patterns.

[0105] The suggestion assistance unit can estimate the user's emotions and adjust its recommendation methods based on those emotions. For example, if the user is feeling stressed, the suggestion assistance unit will provide simple and easy-to-understand recommendations. The suggestion assistance unit uses generative AI to estimate the user's emotions and adjust its recommendation methods based on those emotions. For example, if the user is relaxed, the suggestion assistance unit will provide recommendations that include detailed information. This reduces the user's burden by adjusting the recommendation methods based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The recommendation support unit can improve the accuracy of recommendations by thoroughly analyzing the user's past preferences when making recommendations. For example, the recommendation support unit analyzes the common characteristics of restaurants that users have given high ratings to and recommends similar restaurants. The recommendation support unit uses generative AI to thoroughly analyze the user's past preferences and improve the accuracy of recommendations. For example, the recommendation support unit analyzes the characteristics of restaurants that users have given low ratings to and recommends restaurants to avoid. In this way, the accuracy of recommendations can be improved by thoroughly analyzing the user's past preferences.

[0107] The suggestion assistance unit can estimate the user's emotions and determine recommendation priorities based on those emotions. For example, if the user is feeling stressed, the suggestion assistance unit will prioritize recommending relaxing restaurants. The suggestion assistance unit uses generative AI to estimate the user's emotions and determine recommendation priorities based on those emotions. For example, if the user is having fun, the suggestion assistance unit will prioritize recommending restaurants with high entertainment value. This reduces the user's burden by determining recommendation priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The recommendation support unit can analyze the user's social media activity when making recommendations and suggest relevant restaurants. For example, the recommendation support unit can recommend restaurants that the user has checked into on social media. The recommendation support unit uses generative AI to analyze the user's social media activity and recommend relevant restaurants. For example, the recommendation support unit can recommend restaurants that the user has given high ratings to on social media. In this way, by analyzing the user's social media activity, it can recommend relevant restaurants.

[0109] The voice reservation unit can estimate the user's emotions and adjust the voice reservation method based on the estimated emotions. For example, if the user is feeling stressed, the voice reservation unit provides a simple and quick voice reservation procedure. The voice reservation unit uses AI to estimate the user's emotions and adjust the voice reservation method based on the estimated emotions. For example, if the user is relaxed, the voice reservation unit provides detailed voice reservation options and suggests a customizable reservation method. This reduces the burden on the user by adjusting the voice reservation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The voice reservation unit can select the optimal voice reservation method by referring to the user's past reservation history when a voice reservation is made. For example, the voice reservation unit will prioritize suggesting voice reservation methods that the user has frequently used in the past. The voice reservation unit uses generation AI to select the optimal voice reservation method by referring to the user's past reservation history. For example, the voice reservation unit will predict and suggest a voice reservation method to be used during a specific time period based on the user's past reservation history. In this way, the optimal voice reservation method can be selected by referring to the user's past reservation history.

[0111] The voice reservation unit can dynamically change the voice reservation procedure according to the user's current situation when a reservation is made by voice. For example, if the user is on the move, the voice reservation unit will automatically acquire the user's current location and proceed with the voice reservation procedure. The voice reservation unit uses a generation AI to dynamically change the voice reservation procedure according to the user's current situation. For example, if the user is in a hurry, the voice reservation unit will display only the minimum necessary reservation items. In this way, by dynamically changing the voice reservation procedure according to the user's current situation, more appropriate reservations can be made.

[0112] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit provides a simple and quick notification method. The notification unit uses AI to estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is relaxed, the notification unit provides detailed notification options and suggests a customizable notification method. This reduces the user's burden by adjusting the notification method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize suggesting notification methods that the user has frequently used in the past. The notification unit uses generative AI to select the optimal notification method by referring to the user's past notification history. For example, the notification unit predicts and suggests notification methods to be used during specific time periods based on the user's past notification history. In this way, the optimal notification method can be selected by referring to the user's past notification history.

[0114] The notification unit can analyze the user's past behavior patterns and simplify notifications when they are sent. For example, the notification unit can automatically display notification methods that the user has frequently used in the past as suggestions. The notification unit uses generative AI to analyze the user's past behavior patterns and simplify notifications. For example, the notification unit can predict and suggest notification methods to be used during specific time periods based on the user's past behavior patterns. In this way, notifications can be simplified by analyzing the user's past behavior patterns.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The recommendation team can suggest restaurants based on the user's current health status, in addition to their past preferences and behavioral data. For example, if a user is using a health management app, the system can suggest restaurants that consider calories and nutritional balance based on that data. Furthermore, if a user has specific allergies, the system can suggest allergy-friendly restaurants based on that information. Additionally, if a user is on a diet, the system can prioritize suggesting restaurants that offer low-calorie or healthy menu options.

[0117] The suggestion support unit can estimate whether a user is seeking a new experience based on their past preferences and behavioral data, and can also suggest new restaurants. For example, it can suggest restaurants in areas the user has never visited before. It can also suggest restaurants in culinary genres the user has never tried. Furthermore, if a user is interested in a particular event or festival, it can suggest restaurants related to that event.

[0118] The voice reservation unit can estimate the user's emotions and adjust the tone and speed of the voice reservation based on those emotions. For example, if the user is stressed, it can provide a calm and slow voice reservation. If the user is in a hurry, it can provide a quick and concise voice reservation. Furthermore, if the user is relaxed, it can provide a friendly and detailed voice reservation.

[0119] The notification unit can estimate the user's emotions and adjust the content and timing of notifications based on those emotions. For example, if the user is stressed, it can send only simple and important information. If the user is relaxed, it can provide notifications with more detailed information and additional suggestions. Furthermore, if the user is busy, the notification timing can be adjusted to send notifications when the user is calm.

[0120] The recommendation system can suggest restaurants that take into account not only the user's past preferences and behavioral data, but also the preferences of the user's friends and family. For example, if a user is dining with friends, the system can suggest a restaurant that everyone will enjoy by considering the friends' past preferences and behavioral data. Similarly, if a user is dining with family, the system can suggest family-friendly restaurants that take into account the preferences of all family members. Furthermore, if a user is dining with a specific group, the system can suggest a restaurant that the entire group can enjoy by considering the group's preferences.

[0121] The reservation system can process reservations considering the user's current schedule in addition to their past reservation history. For example, if a user is using a calendar app, the system can suggest the optimal reservation time based on that schedule. It can also process reservations considering a user's schedule if they plan to have a meal after a specific event or meeting. Furthermore, it can adjust the reservation process to accommodate sudden changes in the user's plans.

[0122] The learning unit can estimate the user's emotions and adjust the priority of the training data based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize learning data on relaxing restaurants. If the user is having fun, it can prioritize learning data on entertaining restaurants. Furthermore, if the user is health-conscious, it can prioritize learning data on health-oriented restaurants.

[0123] The recommendation system can suggest restaurants based on the user's current mood, in addition to their past preferences and behavioral data. For example, if the user is tired, it can suggest a restaurant with a relaxing atmosphere. If the user is energetic, it can suggest a lively restaurant. Furthermore, if the user is in the mood for a specific dish, the system can prioritize suggesting restaurants that serve that dish.

[0124] The recommendation team can suggest restaurants based on the user's past preferences and behavioral data, as well as considering the current weather and season. For example, during cold weather, they can suggest restaurants that serve hot food. On rainy days, they can suggest indoor restaurants. Furthermore, on hot summer days, they can suggest restaurants that serve cold drinks and desserts.

[0125] The recommendation system can suggest restaurants based on the user's past preferences and behavioral data, as well as their current budget. For example, if a user has set a budget, the system can suggest the best restaurant within that budget. It can also suggest restaurants where users can use specific discounts or coupons. Furthermore, if a user prefers a particular payment method, the system can suggest restaurants that accept that method.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The learning unit learns the user's past preferences and behavior. For example, it learns the user's past restaurant selection history, order history, and rating history, and analyzes this data using generative AI. This helps to understand the user's preferences and behavioral patterns and learn data to suggest similar restaurants. Step 2: The reception desk inputs information such as area, date and time, number of people, purpose, and preferences. For example, a user can input information such as "I want to have a drinking party with 4 people in Shinjuku at 8 PM." The reception desk uses AI to analyze the user's input information and provides the necessary information to the generating AI. Step 3: The suggestion unit proposes the most suitable restaurant candidates based on the data learned by the learning unit. Using generative AI, it integrates information such as online restaurant ratings, seating information, menus, and prices based on the user's preferences and behavioral data to propose the most suitable restaurant candidates. For example, it can suggest similar restaurants by considering the ratings of restaurants the user has visited in the past and their preferred cuisine. Step 4: The reservation department makes reservations for the restaurants selected by the user from those suggested by the suggestion department, using web reservations or voice calls. The reservation department uses AI to automatically make reservations for the restaurants selected by the user. For example, if the restaurant selected by the user does not support web reservations, the reservation department will make the reservation in conjunction with an automated voice service.

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0131] Each of the multiple elements described above, including the learning unit, reception unit, suggestion unit, reservation unit, suggestion support unit, voice reservation unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's past preferences and behavior. The reception unit is implemented by the control unit 46A of the smart device 14 and inputs information such as area, date and time, number of people, purpose, and preferences. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the most suitable restaurant candidates. The reservation unit is implemented by the control unit 46A of the smart device 14 and handles reservation procedures using web reservations or voice calls. The suggestion support unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests recommendations even when each element is unknown. The voice reservation unit is implemented by the control unit 46A of the smart device 14 and attempts to make reservations even for stores that do not support web reservations in cooperation with an automated voice service. The notification unit is implemented by the specific processing unit 290 of the data processing device 12, and notifies the user if the reservation fails. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 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.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the learning unit, reception unit, suggestion unit, reservation unit, suggestion support unit, voice reservation unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's past preferences and behavior. The reception unit is implemented by the control unit 46A of the smart glasses 214 and inputs information such as area, date and time, number of people, purpose, and preferences. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the most suitable restaurant candidates. The reservation unit is implemented by the control unit 46A of the smart glasses 214 and handles reservation procedures using web reservations or voice calls. The suggestion support unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests recommendations even when each element is unknown. The voice reservation unit is implemented by the control unit 46A of the smart glasses 214 and attempts to make reservations even for stores that do not support web reservations in cooperation with an automated voice service. The notification unit is implemented by the specific processing unit 290 of the data processing device 12, and notifies the user if the reservation fails. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the learning unit, reception unit, suggestion unit, reservation unit, suggestion support unit, voice reservation unit, and notification unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's past preferences and behavior. The reception unit is implemented by the control unit 46A of the headset terminal 314 and inputs information such as area, date and time, number of people, purpose, and preferences. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the most suitable restaurant candidates. The reservation unit is implemented by the control unit 46A of the headset terminal 314 and handles reservation procedures using web reservations or voice calls. The suggestion support unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests recommendations even when each element is unknown. The voice reservation unit is implemented by the control unit 46A of the headset terminal 314 and attempts to make reservations even for stores that do not support web reservations in cooperation with an automated voice service. The notification unit is implemented by the specific processing unit 290 of the data processing device 12, and notifies the user if the reservation fails. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the learning unit, reception unit, suggestion unit, reservation unit, suggestion assistance unit, voice reservation unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's past preferences and behavior. The reception unit is implemented by the control unit 46A of the robot 414 and inputs information such as area, date and time, number of people, purpose, and preferences. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the most suitable restaurant candidates. The reservation unit is implemented by the control unit 46A of the robot 414 and handles reservation procedures using web reservations or voice telephones. The suggestion assistance unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests recommendations even when each element is unknown. The voice reservation unit is implemented by the control unit 46A of the robot 414 and attempts to make reservations even for stores that do not support web reservations in cooperation with an automated voice service. The notification unit is implemented by the specific processing unit 290 of the data processing device 12, and notifies the user if the reservation fails. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0181] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) A learning unit that learns the user's past preferences and behavior, The reception area is where you input information such as area, date and time, number of people, purpose, and preferences. A proposal unit that suggests the most suitable restaurant candidates based on the data learned by the aforementioned learning unit, The reservation department handles reservations for restaurants selected by the user from among those proposed by the aforementioned proposal department, using web reservations or voice calls. Equipped with A system characterized by the following features. (Note 2) It includes a suggestion support function that offers recommendations even when each element is unknown. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an automated voice service that allows it to attempt to make reservations even at stores that do not support online reservations. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a notification unit that notifies the user if the reservation fails. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We integrate past search data, reservation data, online restaurant ratings, seating information, menus, prices, and other information to provide suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reservation section is, The user makes a reservation at a restaurant of their choice from the suggested restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, During training, the system analyzes users' past restaurant reviews in detail and learns patterns of rating fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, During training, the learning algorithm is optimized by considering the user's meal frequency and time patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During training, the system analyzes users' social media activity and adds relevant restaurant information to the training data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, During training, the system learns region-specific restaurant information while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is During input, the system provides input assistance by referring to the user's past input history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is The input fields are dynamically changed based on the user's current status during input. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is During input, the system provides the optimal input method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reception unit is During input, the system analyzes the user's past behavioral patterns and simplifies the input process. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, we improve the accuracy of the suggestions by taking into account the user's past restaurant reviews. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, the suggestion algorithm is optimized by considering the user's meal frequency and time patterns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, we take the user's geographical location into consideration and propose restaurants specific to that area. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reservation section is, The system estimates the user's emotions and adjusts the booking process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reservation section is, When a reservation is made, the system will refer to the user's past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, The booking process is dynamically modified based on the user's current status during the booking process. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation section is, When making a reservation, we provide the optimal reservation method by taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation section is, When a reservation is made, the system analyzes the user's past behavior patterns to simplify the reservation process. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposed auxiliary unit is, It estimates the user's emotions and adjusts the recommendation method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned proposed auxiliary unit is, When making recommendations, we analyze the user's past preferences in detail to improve the accuracy of the suggestions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned proposed auxiliary unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned proposed auxiliary unit is, When making recommendations, the system analyzes the user's social media activity and suggests relevant restaurants. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned voice reservation unit is It estimates the user's emotions and adjusts the voice reservation method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned voice reservation unit is When making a voice reservation, the system will refer to the user's past reservation history to select the most suitable voice reservation method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned voice reservation unit is When making a voice reservation, the reservation procedure is dynamically changed according to the user's current status. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned notification unit, When sending notifications, the system analyzes the user's past behavior patterns to simplify the notifications. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning unit that learns the user's past preferences and behavior, The reception area is where you input information such as area, date and time, number of people, purpose, and preferences. A proposal unit that suggests the most suitable restaurant candidates based on the data learned by the aforementioned learning unit, The reservation department handles reservations for restaurants selected by the user from among those proposed by the aforementioned proposal department, using web reservations or voice calls. Equipped with A system characterized by the following features.

2. It includes a suggestion support function that offers recommendations even when each element is unknown. The system according to feature 1.

3. It features an automated voice service that allows it to attempt to make reservations even at stores that do not support online reservations. The system according to feature 1.

4. It includes a notification unit that notifies the user if the reservation fails. The system according to feature 1.

5. The aforementioned proposal section is, We integrate past search data, reservation data, online restaurant ratings, seating information, menus, prices, and other information to provide suggestions. The system according to feature 1.

6. The aforementioned reservation section is, The user makes a reservation at a restaurant of their choice from the suggested restaurants. The system according to feature 1.

7. The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system according to feature 1.

8. The aforementioned learning unit, During training, the system analyzes users' past restaurant reviews in detail and learns patterns of rating fluctuations. The system according to feature 1.

9. The aforementioned learning unit, During training, the learning algorithm is optimized by considering the user's meal frequency and time patterns. The system according to feature 1.

10. The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system according to feature 1.

11. The aforementioned learning unit, During training, the system analyzes users' social media activity and adds relevant restaurant information to the training data. The system according to feature 1.

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