System

Abstract

An object of a system according to an embodiment is to propose a spot that suits a user's preference during a traffic jam.SOLUTION: A system according to an embodiment includes an action data learning unit, a congestion information acquisition unit, and a spot proposal unit. The action data learning unit learns action data of the user. The congestion information acquisition unit acquires congestion information. The spot proposal unit proposes a spot on the basis of the preference of the user learned by the action data learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide adequate suggestions for avoiding time wasted due to traffic jams, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest spots that suit the user's preferences when traffic jams occur. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavioral data learning unit, a traffic congestion information acquisition unit, and a spot suggestion unit. The behavioral data learning unit learns user behavioral data. The traffic congestion information acquisition unit acquires traffic congestion information. The spot suggestion unit suggests spots based on the user's preferences learned by the behavioral data learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest spots that suit the user's preferences when traffic jams occur. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The car navigation system + generative AI system according to an embodiment of the present invention is a system that suggests personalized tourist spots and short rest spots based on the user's behavioral data and provides a plan for a smooth return home after traffic congestion has cleared. This allows the user to effectively utilize the time spent waiting in traffic jams and return home comfortably.

[0029] The car navigation system and generation AI system according to the embodiment includes a behavioral data learning unit, a traffic congestion information acquisition unit, and a spot suggestion unit. The behavioral data learning unit learns user behavioral data. For example, the behavioral data learning unit analyzes the user's search history and reservation history to learn the user's preferences. The behavioral data learning unit can also learn the user's consumption patterns based on the user's payment information. The behavioral data learning unit can also learn the user's movement patterns based on the user's location information. The traffic congestion information acquisition unit acquires traffic congestion information. For example, the traffic congestion information acquisition unit acquires current traffic conditions using traffic sensors and GPS data. The traffic congestion information acquisition unit can also predict the time it takes for traffic congestion to clear based on past traffic data. The traffic congestion information acquisition unit can also acquire real-time traffic congestion information using a traffic information service. The spot suggestion unit suggests spots based on the user's preferences learned by the behavioral data learning unit. For example, the spot suggestion unit suggests nearby tourist spots based on the user's past visit history. The spot suggestion unit can also suggest short rest spots such as restaurants and cafes based on the user's preferences. The spot suggestion unit can also suggest optimal spots based on the user's current location and the waiting time in traffic jams. This allows the car navigation system + generation AI system according to the embodiment to effectively utilize the time the user spends waiting in traffic jams and return home comfortably. For example, the user can effectively utilize the time spent waiting in traffic jams by enjoying sightseeing at the suggested tourist spots or eating at the suggested restaurants. Furthermore, the user can relax at the suggested spots, resulting in a less stressful return home.

[0030] Furthermore, the car navigation system + generative AI system is equipped with a behavioral data learning unit that collects real-time biometric information in addition to the user's behavioral data, allowing the generative AI to make suggestions based on the user's physical condition. The behavioral data learning unit collects real-time biometric information in addition to the user's behavioral data. For example, the behavioral data learning unit collects real-time heart rate and stress level data from the user's smartwatch or fitness tracker and inputs that data into the generative AI. Based on this biometric information, the generative AI suggests rest spots and relaxation facilities that are optimal for the user's physical condition. For example, if the user's heart rate is high, the generative AI will suggest relaxation spots. Also, if the user's stress level is high, the generative AI can suggest spots that will help reduce stress. The generative AI can also suggest appropriate rest times based on the user's physical condition. This allows the system to suggest optimal spots based on the user's physical condition.

[0031] Furthermore, the car navigation system + generative AI system is equipped with a behavioral data learning unit that uses user behavioral data to learn the long-term evolution of a user's hobbies and interests and predict future changes in preferences. The behavioral data learning unit allows the generative AI to learn the long-term evolution of a user's hobbies and interests based on the user's behavioral data. For example, the behavioral data learning unit analyzes the user's past search history and reservation history, allowing the generative AI to learn the evolution of the user's hobbies and interests. The generative AI predicts changes in the user's preferences based on data from the past few years and suggests spots that the user may be interested in in the future. For example, the generative AI predicts spots the user may want to visit in the future based on data from spots the user has visited in the past. The generative AI can also suggest new spots based on the evolution of the user's hobbies and interests. The generative AI can also predict changes in the user's preferences and suggest events and activities that the user may be interested in in the future. This allows the system to predict future changes in the user's preferences and suggest optimal spots.

[0032] Furthermore, the car navigation system + generative AI system is equipped with a behavioral data learning unit that integrates the user's behavioral data with that of family and friends to make suggestions based on the preferences of the entire group. The behavioral data learning unit collects the behavioral data of family and friends in addition to the user's behavioral data. For example, the behavioral data learning unit collects the behavioral data of family and friends in addition to the user's behavioral data, and the generative AI makes suggestions based on the preferences of the entire group. The generative AI suggests tourist spots and restaurants that the whole family can enjoy. For example, the generative AI suggests spots that interest all family members. The generative AI can also suggest activities and events based on the preferences of the entire friend group. The generative AI can also suggest optimal routes based on the preferences of the entire group. This allows the system to suggest optimal spots based on the preferences of the entire group.

[0033] Furthermore, the car navigation + generative AI system is equipped with a behavioral data learning unit that uses the user's behavioral data to suggest music and podcasts that match the user's preferences, providing entertainment while traveling. The behavioral data learning unit uses the user's behavioral data to suggest music and podcasts that match the user's preferences. For example, the behavioral data learning unit analyzes the user's past music playback history and podcast listening history, and the generative AI suggests music and podcasts that match the user's preferences. The generative AI provides relaxing music and interesting podcasts while traveling. For example, the generative AI suggests similar music based on music the user has liked to listen to in the past. The generative AI can also suggest new podcasts based on the user's interests. The generative AI can also suggest optimal entertainment that matches the user's mood while traveling. This allows the system to provide entertainment that matches the user's preferences.

[0034] Furthermore, the car navigation system + generating AI system is equipped with a traffic congestion information acquisition unit that integrates weather information and event information in addition to traffic congestion information to make more accurate traffic congestion predictions. The traffic congestion information acquisition unit integrates weather information and event information in addition to traffic congestion information. For example, the traffic congestion information acquisition unit integrates weather information in addition to traffic congestion information, allowing the generating AI to make more accurate traffic congestion predictions. The generating AI makes traffic congestion predictions taking into account traffic conditions during rainy weather. For example, the generating AI predicts the time when traffic congestion will clear based on weather information. The generating AI can also make traffic congestion predictions based on event information. For example, the generating AI makes traffic congestion predictions taking into account traffic conditions when an event is held. The generating AI can also suggest optimal routes based on weather information and event information. This enables more accurate traffic congestion predictions that take weather and event information into account.

[0035] Furthermore, the car navigation system + generating AI system includes a traffic congestion information acquisition unit that predicts the congestion status of spots the user is likely to visit based on traffic congestion information and makes suggestions to avoid the congestion. The traffic congestion information acquisition unit predicts the congestion status of spots the user is likely to visit based on traffic congestion information. For example, the traffic congestion information acquisition unit predicts the congestion status of spots the user is likely to visit based on traffic congestion information, and the generating AI makes suggestions to avoid the congestion. The generating AI proposes a route that avoids spots that are expected to be crowded. For example, the generating AI proposes an alternative route to avoid spots that are expected to be crowded. The generating AI can also suggest adjusting the visit time to avoid times when congestion is expected. The generating AI can also suggest the optimal spots based on the congestion status. This makes it possible to make suggestions to avoid congestion.

[0036] Furthermore, the car navigation system + generating AI system includes a traffic congestion information acquisition unit that provides the operation status of public transportation that the user may use based on traffic congestion information and suggests alternative means. The traffic congestion information acquisition unit provides the operation status of public transportation that the user may use based on traffic congestion information. For example, the traffic congestion information acquisition unit provides the operation status of public transportation that the user may use based on traffic congestion information, and the generating AI suggests alternative means. The generating AI suggests the optimal alternative means based on the operation status of trains and buses. For example, the generating AI suggests the optimal alternative means based on the operation status of trains and buses. The generating AI can also suggest the optimal route based on the operation status of public transportation. The generating AI can also suggest adjusting the visit time based on the operation status of public transportation. This makes it possible to suggest alternative means that take into account the operation status of public transportation.

[0037] Furthermore, the car navigation system + generative AI system is equipped with a spot suggestion unit that takes into account the user's past reviews and ratings for the suggested spots to make more accurate personalized suggestions. The spot suggestion unit takes into account the user's past reviews and ratings for the suggested spots. For example, the spot suggestion unit analyzes the user's past reviews and ratings to improve the accuracy of the spots suggested by the generative AI. The generative AI suggests similar spots based on spots that the user has given a high rating. For example, the generative AI suggests similar spots based on spots that the user has given a high rating in the past. The generative AI can also suggest new spots based on the user's reviews and ratings. The generative AI can also suggest optimal spots based on the user's reviews and ratings. This enables highly accurate suggestions that take into account the user's past reviews and ratings.

[0038] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit in which the generation AI automatically generates detailed information about suggested spots and provides it to the user. In the spot suggestion unit, the generation AI automatically generates detailed information about suggested spots. For example, in the spot suggestion unit, the generation AI automatically generates detailed information about suggested spots and provides it to the user. The generation AI automatically generates restaurant menus and service details and provides them to the user. For example, the generation AI automatically generates restaurant menus and service details and provides them to the user. The generation AI can also automatically generate detailed information about tourist spots and provide it to the user. The generation AI can also automatically generate special offer information for suggested spots and provide it to the user. In this way, detailed information about spots can be automatically generated and provided to the user.

[0039] Furthermore, the car navigation system + generating AI system includes a spot suggestion unit that suggests spots that the whole family can enjoy, including information about facilities for the user's pets and children. The spot suggestion unit includes information about facilities for the user's pets and children in the suggested spots. For example, the spot suggestion unit includes information about facilities for the user's pets and children in the suggested spots, and the generating AI suggests spots that the whole family can enjoy. The generating AI suggests restaurants that allow pets and playgrounds for children. For example, the generating AI suggests restaurants that allow pets and playgrounds for children. The generating AI can also suggest activities that the whole family can enjoy. The generating AI can also suggest events that the whole family can enjoy. This makes it possible to suggest things that the whole family can enjoy.

[0040] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that includes healthy meals and relaxation facilities according to the user's health condition in the suggested spots. The spot suggestion unit includes healthy meals and relaxation facilities according to the user's health condition in the suggested spots. For example, the spot suggestion unit includes healthy meals according to the user's health condition in the suggested spots, and the generation AI makes suggestions. The generation AI suggests restaurants that offer low-calorie and low-carbohydrate menus. For example, the generation AI suggests restaurants that offer low-calorie and low-carbohydrate menus. The generation AI can also suggest facilities that have a relaxation effect. The generation AI can also suggest fitness plans according to the user's health condition. This makes it possible to make suggestions according to the user's health condition.

[0041] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that provides a customized activity plan for spending time at a suggested spot based on the user's past behavioral data. The spot suggestion unit provides a customized activity plan for spending time at a suggested spot based on the user's past behavioral data. For example, the spot suggestion unit provides a customized activity plan for spending time at a suggested spot based on the user's past behavioral data. The generation AI suggests similar activities based on activities at spots visited in the past. For example, the generation AI suggests similar activities based on activities the user enjoyed in the past. The generation AI can also suggest new activities based on the user's preferences. The generation AI can also suggest optimal activity plans based on the user's past behavioral data. This makes it possible to provide customized activity plans based on the user's past behavioral data.

[0042] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that includes guided tours and event information automatically generated by the generation AI in ways to spend time at suggested spots. The spot suggestion unit includes guided tours and event information automatically generated by the generation AI in ways to spend time at suggested spots. For example, the spot suggestion unit includes guided tours automatically generated by the generation AI in ways to spend time at suggested spots. The generation AI suggests guided tours that take in the highlights of tourist spots. For example, the generation AI suggests guided tours that take in the highlights of tourist spots. The generation AI can also suggest events that are optimal for the user based on event information. The generation AI can also suggest optimal guided tours based on the user's preferences. In this way, by including guided tours and event information automatically generated by the generation AI, the user's time can be enriched.

[0043] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that provides local specialty products and souvenir information that match the user's preferences when spending time at a suggested spot. The spot suggestion unit provides local specialty product and souvenir information that match the user's preferences when spending time at a suggested spot. For example, the spot suggestion unit provides local specialty product information that match the user's preferences when spending time at a suggested spot. The generation AI introduces local specialties and specialty products. For example, the generation AI introduces local specialties and specialty products. The generation AI can also provide souvenir information based on the user's preferences. The generation AI can also provide optimal souvenir information based on the user's past behavioral data. This makes it possible to provide local specialty product and souvenir information that match the user's preferences.

[0044] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that includes fitness plans and relaxation plans tailored to the user's health condition in ways to spend time at suggested spots. The spot suggestion unit includes fitness plans and relaxation plans tailored to the user's health condition in ways to spend time at suggested spots. For example, the spot suggestion unit includes fitness plans tailored to the user's health condition in ways to spend time at suggested spots. The generation AI suggests walking courses and jogging courses. For example, the generation AI suggests walking courses and jogging courses. The generation AI can also suggest plans that have a relaxation effect. The generation AI can also suggest relaxation plans tailored to the user's health condition. This makes it possible to provide fitness plans and relaxation plans tailored to the user's health condition.

[0045] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that introduces targeted advertising based on user behavior data when displaying ads for suggested spots, thereby maximizing advertising effectiveness. The spot suggestion unit introduces targeted advertising based on user behavior data when displaying ads for suggested spots. For example, the spot suggestion unit introduces targeted advertising based on user behavior data when displaying ads for suggested spots. The generation AI displays relevant ads based on spots the user has visited in the past and their search history. For example, the generation AI displays relevant ads based on spots the user has visited in the past and their search history. The generation AI can also display optimal ads based on user behavior data. The generation AI can also adjust the display order of ads based on user behavior data. This allows the introduction of targeted advertising based on user behavior data to maximize advertising effectiveness.

[0046] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that uses advertising copy and visuals automatically generated by the generation AI to display advertisements for suggested spots, thereby enhancing the appeal of the advertisements. The spot suggestion unit uses advertising copy and visuals automatically generated by the generation AI to display advertisements for suggested spots. For example, the spot suggestion unit uses advertising copy automatically generated by the generation AI to display advertisements for suggested spots. The generation AI generates a catchy slogan that matches the user's preferences, thereby enhancing the appeal of the advertisement. For example, the generation AI generates a catchy slogan that matches the user's preferences, thereby enhancing the appeal of the advertisement. The generation AI can also automatically generate advertisement visuals and provide them to the user. The generation AI can also automatically generate advertisement content and provide them to the user. In this way, the appeal of the advertisement can be enhanced by using advertising copy and visuals automatically generated by the generation AI.

[0047] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that introduces personalized advertisements based on the user's health condition and lifestyle into the advertisement display for the suggested spots. The spot suggestion unit introduces personalized advertisements based on the user's health condition and lifestyle into the advertisement display for the suggested spots. For example, the spot suggestion unit introduces personalized advertisements based on the user's health condition into the advertisement display for the suggested spots. The generation AI displays advertisements for health-conscious restaurants and fitness facilities. For example, the generation AI displays advertisements for health-conscious restaurants and fitness facilities. The generation AI can also display optimal advertisements based on the user's lifestyle. The generation AI can also adjust the display order of advertisements based on the user's health condition and lifestyle. This maximizes the effectiveness of advertisements by introducing personalized advertisements based on the user's health condition and lifestyle.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The car navigation + generative AI system also includes a behavioral data learning unit that uses user behavioral data to suggest music and podcasts that match the user's preferences, providing entertainment while traveling. The behavioral data learning unit uses user behavioral data to suggest music and podcasts that match the user's preferences. For example, the behavioral data learning unit analyzes the user's past music playback history and podcast listening history, allowing the generative AI to suggest music and podcasts that match the user's preferences. The generative AI provides relaxing music and interesting podcasts while traveling. For example, the generative AI suggests similar music based on music the user has liked listening to in the past. The generative AI can also suggest new podcasts based on the user's interests. The generative AI can also suggest the optimal entertainment to match the user's mood while traveling. This allows the system to provide entertainment that matches the user's preferences.

[0050] The car navigation system + generative AI system further includes a spot suggestion unit where the generative AI provides information on local specialties and souvenirs that match the user's preferences based on the user's behavioral data. The spot suggestion unit provides information on local specialties that match the user's preferences when spending time at the suggested spot. The generative AI introduces local specialties and products. For example, the generative AI introduces local specialties and products. The generative AI can also provide souvenir information based on the user's preferences. The generative AI can also provide optimal souvenir information based on the user's past behavioral data. This makes it possible to provide information on local specialties and souvenirs that match the user's preferences.

[0051] The car navigation system + generative AI system further includes a spot suggestion unit in which the generative AI includes healthy meals and relaxation facilities according to the user's health condition based on the user's behavioral data. The spot suggestion unit includes healthy meals according to the user's health condition in the suggested spots, and the generative AI makes suggestions. The generative AI suggests restaurants that offer low-calorie and low-carb menus. For example, the generative AI suggests restaurants that offer low-calorie and low-carb menus. The generative AI can also suggest facilities that have a relaxation effect. The generative AI can also suggest fitness plans according to the user's health condition. This makes it possible to make suggestions according to the user's health condition.

[0052] The car navigation system + generating AI system further includes a spot suggestion unit in which the generating AI proposes fitness and relaxation plans that suit the user's preferences based on the user's behavioral data. The spot suggestion unit includes fitness plans that suit the user's health condition in how to spend time at the suggested spots. The generating AI suggests walking and jogging courses. For example, the generating AI suggests walking and jogging courses. The generating AI can also propose plans that have a relaxation effect. The generating AI can also propose relaxation plans that suit the user's health condition. This makes it possible to provide fitness and relaxation plans that suit the user's health condition.

[0053] The car navigation + generative AI system also includes a spot suggestion unit in which the generative AI provides guided tours and event information that match the user's preferences based on the user's behavioral data. The spot suggestion unit includes guided tours automatically generated by the generative AI in ways to spend time at the suggested spots. The generative AI suggests guided tours that take in the highlights of tourist destinations. For example, the generative AI suggests guided tours that take in the highlights of tourist destinations. The generative AI can also suggest events that are optimal for the user based on event information. The generative AI can also suggest optimal guided tours based on the user's preferences. In this way, by including guided tours and event information automatically generated by the generative AI, the user's time can be enriched.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The behavioral data learning unit learns the user's behavioral data. For example, the behavioral data learning unit analyzes the user's search history and reservation history to learn the user's preferences. The behavioral data learning unit can also learn the user's consumption patterns based on the user's payment information. Furthermore, the behavioral data learning unit can also learn the user's movement patterns based on the user's location information. Step 2: The traffic congestion information acquisition unit acquires traffic congestion information. For example, the traffic congestion information acquisition unit acquires current traffic conditions using traffic sensors and GPS data. The traffic congestion information acquisition unit can also predict the time it will take for the traffic congestion to clear based on past traffic data. Furthermore, the traffic congestion information acquisition unit can also acquire real-time traffic congestion information using a traffic information service. Step 3: The spot suggestion unit suggests spots based on the user's preferences learned by the behavioral data learning unit. For example, the spot suggestion unit suggests nearby tourist spots based on the user's past visit history. The spot suggestion unit can also suggest short rest spots such as restaurants and cafes based on the user's preferences. Furthermore, the spot suggestion unit can also suggest optimal spots based on the user's current location and waiting time in traffic jams.

[0056] (Example 2) The car navigation system + generative AI system according to an embodiment of the present invention is a system that suggests personalized tourist spots and short rest spots based on the user's behavioral data and provides a plan for a smooth return home after traffic congestion has cleared. This allows the user to effectively utilize the time spent waiting in traffic jams and return home comfortably.

[0057] The car navigation system and generation AI system according to the embodiment includes a behavioral data learning unit, a traffic congestion information acquisition unit, and a spot suggestion unit. The behavioral data learning unit learns user behavioral data. For example, the behavioral data learning unit analyzes the user's search history and reservation history to learn the user's preferences. The behavioral data learning unit can also learn the user's consumption patterns based on the user's payment information. The behavioral data learning unit can also learn the user's movement patterns based on the user's location information. The traffic congestion information acquisition unit acquires traffic congestion information. For example, the traffic congestion information acquisition unit acquires current traffic conditions using traffic sensors and GPS data. The traffic congestion information acquisition unit can also predict the time it takes for traffic congestion to clear based on past traffic data. The traffic congestion information acquisition unit can also acquire real-time traffic congestion information using a traffic information service. The spot suggestion unit suggests spots based on the user's preferences learned by the behavioral data learning unit. For example, the spot suggestion unit suggests nearby tourist spots based on the user's past visit history. The spot suggestion unit can also suggest short rest spots such as restaurants and cafes based on the user's preferences. The spot suggestion unit can also suggest optimal spots based on the user's current location and the waiting time in traffic jams. This allows the car navigation system + generation AI system according to the embodiment to effectively utilize the time the user spends waiting in traffic jams and return home comfortably. For example, the user can effectively utilize the time spent waiting in traffic jams by enjoying sightseeing at the suggested tourist spots or eating at the suggested restaurants. Furthermore, the user can relax at the suggested spots, resulting in a less stressful return home.

[0058] Furthermore, the car navigation system + generative AI system is equipped with a behavioral data learning unit that collects real-time biometric information in addition to the user's behavioral data, allowing the generative AI to make suggestions based on the user's physical condition. The behavioral data learning unit collects real-time biometric information in addition to the user's behavioral data. For example, the behavioral data learning unit collects real-time heart rate and stress level data from the user's smartwatch or fitness tracker and inputs that data into the generative AI. Based on this biometric information, the generative AI suggests rest spots and relaxation facilities that are optimal for the user's physical condition. For example, if the user's heart rate is high, the generative AI will suggest relaxation spots. Also, if the user's stress level is high, the generative AI can suggest spots that will help reduce stress. The generative AI can also suggest appropriate rest times based on the user's physical condition. This allows the system to suggest optimal spots based on the user's physical condition.

[0059] Furthermore, the car navigation system + generative AI system is equipped with a behavioral data learning unit that uses user behavioral data to learn the long-term evolution of a user's hobbies and interests and predict future changes in preferences. The behavioral data learning unit allows the generative AI to learn the long-term evolution of a user's hobbies and interests based on the user's behavioral data. For example, the behavioral data learning unit analyzes the user's past search history and reservation history, allowing the generative AI to learn the evolution of the user's hobbies and interests. The generative AI predicts changes in the user's preferences based on data from the past few years and suggests spots that the user may be interested in in the future. For example, the generative AI predicts spots the user may want to visit in the future based on data from spots the user has visited in the past. The generative AI can also suggest new spots based on the evolution of the user's hobbies and interests. The generative AI can also predict changes in the user's preferences and suggest events and activities that the user may be interested in in the future. This allows the system to predict future changes in the user's preferences and suggest optimal spots.

[0060] Furthermore, the car navigation system + generative AI system is equipped with a behavioral data learning unit that uses an emotion estimation function to estimate changes in emotions from the user's past behavioral data and suggests spots that elicit positive emotions. The behavioral data learning unit uses the emotion estimation function to estimate changes in emotions from the user's past behavioral data. For example, the behavioral data learning unit performs emotion estimation on the user's past behavioral data and identifies spots that elicit positive emotions. The generative AI suggests spots that elicit similar emotions based on the emotion scores of spots visited in the past. For example, the generative AI suggests similar spots based on spots where the user felt positive emotions in the past. The generative AI can also suggest new spots that elicit positive emotions based on changes in the user's emotions. The generative AI can also suggest activities that elicit positive emotions based on changes in the user's emotions. This makes it possible to suggest spots that elicit positive emotions in the user.

[0061] Furthermore, the car navigation system + generative AI system is equipped with a behavioral data learning unit that integrates the user's behavioral data with that of family and friends to make suggestions based on the preferences of the entire group. The behavioral data learning unit collects the behavioral data of family and friends in addition to the user's behavioral data. For example, the behavioral data learning unit collects the behavioral data of family and friends in addition to the user's behavioral data, and the generative AI makes suggestions based on the preferences of the entire group. The generative AI suggests tourist spots and restaurants that the whole family can enjoy. For example, the generative AI suggests spots that interest all family members. The generative AI can also suggest activities and events based on the preferences of the entire friend group. The generative AI can also suggest optimal routes based on the preferences of the entire group. This allows the system to suggest optimal spots based on the preferences of the entire group.

[0062] Furthermore, the car navigation + generative AI system is equipped with a behavioral data learning unit that uses the user's behavioral data to suggest music and podcasts that match the user's preferences, providing entertainment while traveling. The behavioral data learning unit uses the user's behavioral data to suggest music and podcasts that match the user's preferences. For example, the behavioral data learning unit analyzes the user's past music playback history and podcast listening history, and the generative AI suggests music and podcasts that match the user's preferences. The generative AI provides relaxing music and interesting podcasts while traveling. For example, the generative AI suggests similar music based on music the user has liked to listen to in the past. The generative AI can also suggest new podcasts based on the user's interests. The generative AI can also suggest optimal entertainment that matches the user's mood while traveling. This allows the system to provide entertainment that matches the user's preferences.

[0063] Furthermore, the car navigation system + generative AI system is equipped with a behavioral data learning unit that uses an emotion estimation function to monitor the emotions a user feels toward specific spots in real time and dynamically adjusts the content of suggestions. The behavioral data learning unit uses the emotion estimation function to monitor the emotions a user feels toward specific spots in real time. For example, the behavioral data learning unit monitors the emotions a user feels toward specific spots in real time, and the generative AI dynamically adjusts the content of suggestions. The generative AI prioritizes suggestions for spots that the user feels positive about. For example, the generative AI suggests similar spots based on spots that the user feels positive about in real time. The generative AI can also dynamically adjust the content of suggestions according to the user's emotions. The generative AI can also suggest optimal spots based on changes in the user's emotions. This allows the content of suggestions to be dynamically adjusted according to the user's emotions.

[0064] Furthermore, the car navigation system + generating AI system is equipped with a traffic congestion information acquisition unit that integrates weather information and event information in addition to traffic congestion information to make more accurate traffic congestion predictions. The traffic congestion information acquisition unit integrates weather information and event information in addition to traffic congestion information. For example, the traffic congestion information acquisition unit integrates weather information in addition to traffic congestion information, allowing the generating AI to make more accurate traffic congestion predictions. The generating AI makes traffic congestion predictions taking into account traffic conditions during rainy weather. For example, the generating AI predicts the time when traffic congestion will clear based on weather information. The generating AI can also make traffic congestion predictions based on event information. For example, the generating AI makes traffic congestion predictions taking into account traffic conditions when an event is held. The generating AI can also suggest optimal routes based on weather information and event information. This enables more accurate traffic congestion predictions that take weather and event information into account.

[0065] Furthermore, the car navigation system + generating AI system includes a traffic congestion information acquisition unit that uses an emotion estimation function to predict the user's stress level due to traffic congestion and makes suggestions for stress reduction. The traffic congestion information acquisition unit uses the emotion estimation function to predict the user's stress level due to traffic congestion. For example, the traffic congestion information acquisition unit uses the emotion estimation function to predict the user's stress level due to traffic congestion, and the generating AI makes suggestions for stress reduction. The generating AI suggests spots that have a relaxing effect. For example, if the user is feeling stressed, the generating AI suggests spots that have a relaxing effect. The generating AI can also suggest appropriate rest times based on the user's stress level. The generating AI can also suggest optimal routes based on the user's stress level. This makes it possible to predict the user's stress level and make suggestions for stress reduction.

[0066] Furthermore, the car navigation system + generating AI system includes a traffic congestion information acquisition unit that predicts the congestion status of spots the user is likely to visit based on traffic congestion information and makes suggestions to avoid the congestion. The traffic congestion information acquisition unit predicts the congestion status of spots the user is likely to visit based on traffic congestion information. For example, the traffic congestion information acquisition unit predicts the congestion status of spots the user is likely to visit based on traffic congestion information, and the generating AI makes suggestions to avoid the congestion. The generating AI proposes a route that avoids spots that are expected to be crowded. For example, the generating AI proposes an alternative route to avoid spots that are expected to be crowded. The generating AI can also suggest adjusting the visit time to avoid times when congestion is expected. The generating AI can also suggest the optimal spots based on the congestion status. This makes it possible to make suggestions to avoid congestion.

[0067] Furthermore, the car navigation system + generating AI system includes a traffic congestion information acquisition unit that provides the operation status of public transportation that the user may use based on traffic congestion information and suggests alternative means. The traffic congestion information acquisition unit provides the operation status of public transportation that the user may use based on traffic congestion information. For example, the traffic congestion information acquisition unit provides the operation status of public transportation that the user may use based on traffic congestion information, and the generating AI suggests alternative means. The generating AI suggests the optimal alternative means based on the operation status of trains and buses. For example, the generating AI suggests the optimal alternative means based on the operation status of trains and buses. The generating AI can also suggest the optimal route based on the operation status of public transportation. The generating AI can also suggest adjusting the visit time based on the operation status of public transportation. This makes it possible to suggest alternative means that take into account the operation status of public transportation.

[0068] Furthermore, the car navigation system + generative AI system includes a traffic congestion information acquisition unit that uses an emotion estimation function to monitor the user's emotional response to traffic congestion information in real time and dynamically proposes the optimal route. The traffic congestion information acquisition unit uses the emotion estimation function to monitor the user's emotional response to traffic congestion information in real time. For example, the traffic congestion information acquisition unit uses the emotion estimation function to monitor the user's emotional response to traffic congestion information in real time, and the generative AI dynamically proposes the optimal route. If the user is feeling stressed, the generative AI proposes a route that has a relaxing effect. For example, if the user is feeling stressed, the generative AI proposes a route that has a relaxing effect. The generative AI can also dynamically adjust the optimal route according to the user's emotions. The generative AI can also propose the optimal route based on changes in the user's emotions. This makes it possible to propose the optimal route according to the user's emotions.

[0069] Furthermore, the car navigation system + generative AI system is equipped with a spot suggestion unit that takes into account the user's past reviews and ratings for the suggested spots to make more accurate personalized suggestions. The spot suggestion unit takes into account the user's past reviews and ratings for the suggested spots. For example, the spot suggestion unit analyzes the user's past reviews and ratings to improve the accuracy of the spots suggested by the generative AI. The generative AI suggests similar spots based on spots that the user has given a high rating. For example, the generative AI suggests similar spots based on spots that the user has given a high rating in the past. The generative AI can also suggest new spots based on the user's reviews and ratings. The generative AI can also suggest optimal spots based on the user's reviews and ratings. This enables highly accurate suggestions that take into account the user's past reviews and ratings.

[0070] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit in which the generation AI automatically generates detailed information about suggested spots and provides it to the user. In the spot suggestion unit, the generation AI automatically generates detailed information about suggested spots. For example, in the spot suggestion unit, the generation AI automatically generates detailed information about suggested spots and provides it to the user. The generation AI automatically generates restaurant menus and service details and provides them to the user. For example, the generation AI automatically generates restaurant menus and service details and provides them to the user. The generation AI can also automatically generate detailed information about tourist spots and provide it to the user. The generation AI can also automatically generate special offer information for suggested spots and provide it to the user. In this way, detailed information about spots can be automatically generated and provided to the user.

[0071] Furthermore, the car navigation system + generative AI system includes a spot suggestion unit that uses an emotion estimation function to predict the emotions the user will have toward the suggested spots and makes suggestions that elicit positive emotions. The spot suggestion unit uses the emotion estimation function to predict the emotions the user will have toward the suggested spots. For example, the spot suggestion unit uses the emotion estimation function to predict the emotions the user will have toward the suggested spots, and the generative AI makes suggestions that elicit positive emotions. The generative AI makes suggestions based on spots that the user has previously felt positive about. For example, the generative AI suggests similar spots based on spots that the user has previously felt positive about. The generative AI can also suggest new spots that elicit positive emotions based on changes in the user's emotions. The generative AI can also suggest activities that elicit positive emotions based on changes in the user's emotions. This makes it possible to make suggestions that elicit positive emotions from the user.

[0072] Furthermore, the car navigation system + generating AI system includes a spot suggestion unit that suggests spots that the whole family can enjoy, including information about facilities for the user's pets and children. The spot suggestion unit includes information about facilities for the user's pets and children in the suggested spots. For example, the spot suggestion unit includes information about facilities for the user's pets and children in the suggested spots, and the generating AI suggests spots that the whole family can enjoy. The generating AI suggests restaurants that allow pets and playgrounds for children. For example, the generating AI suggests restaurants that allow pets and playgrounds for children. The generating AI can also suggest activities that the whole family can enjoy. The generating AI can also suggest events that the whole family can enjoy. This makes it possible to suggest things that the whole family can enjoy.

[0073] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that includes healthy meals and relaxation facilities according to the user's health condition in the suggested spots. The spot suggestion unit includes healthy meals and relaxation facilities according to the user's health condition in the suggested spots. For example, the spot suggestion unit includes healthy meals according to the user's health condition in the suggested spots, and the generation AI makes suggestions. The generation AI suggests restaurants that offer low-calorie and low-carbohydrate menus. For example, the generation AI suggests restaurants that offer low-calorie and low-carbohydrate menus. The generation AI can also suggest facilities that have a relaxation effect. The generation AI can also suggest fitness plans according to the user's health condition. This makes it possible to make suggestions according to the user's health condition.

[0074] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that provides a customized activity plan for spending time at a suggested spot based on the user's past behavioral data. The spot suggestion unit provides a customized activity plan for spending time at a suggested spot based on the user's past behavioral data. For example, the spot suggestion unit provides a customized activity plan for spending time at a suggested spot based on the user's past behavioral data. The generation AI suggests similar activities based on activities at spots visited in the past. For example, the generation AI suggests similar activities based on activities the user enjoyed in the past. The generation AI can also suggest new activities based on the user's preferences. The generation AI can also suggest optimal activity plans based on the user's past behavioral data. This makes it possible to provide customized activity plans based on the user's past behavioral data.

[0075] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that includes guided tours and event information automatically generated by the generation AI in ways to spend time at suggested spots. The spot suggestion unit includes guided tours and event information automatically generated by the generation AI in ways to spend time at suggested spots. For example, the spot suggestion unit includes guided tours automatically generated by the generation AI in ways to spend time at suggested spots. The generation AI suggests guided tours that take in the highlights of tourist spots. For example, the generation AI suggests guided tours that take in the highlights of tourist spots. The generation AI can also suggest events that are optimal for the user based on event information. The generation AI can also suggest optimal guided tours based on the user's preferences. In this way, by including guided tours and event information automatically generated by the generation AI, the user's time can be enriched.

[0076] Furthermore, the car navigation system + generative AI system includes a spot suggestion unit that uses an emotion estimation function to monitor the emotions felt by the user during their experience at a spot in real time and makes suggestions to promote a positive experience. The spot suggestion unit uses the emotion estimation function to monitor the emotions felt by the user during their experience at a spot in real time. For example, the spot suggestion unit uses the emotion estimation function to monitor the emotions felt by the user during their experience at a spot in real time, and the generative AI makes suggestions to promote a positive experience. The generative AI prioritizes suggesting activities that evoke positive emotions in the user. For example, the generative AI suggests similar activities based on activities that evoke positive emotions in the user in real time. The generative AI can also dynamically adjust the content of suggestions based on the user's emotions. The generative AI can also suggest optimal activities based on changes in the user's emotions. This enables suggestions that promote a positive experience for the user.

[0077] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that provides local specialty products and souvenir information that match the user's preferences when spending time at a suggested spot. The spot suggestion unit provides local specialty product and souvenir information that match the user's preferences when spending time at a suggested spot. For example, the spot suggestion unit provides local specialty product information that match the user's preferences when spending time at a suggested spot. The generation AI introduces local specialties and specialty products. For example, the generation AI introduces local specialties and specialty products. The generation AI can also provide souvenir information based on the user's preferences. The generation AI can also provide optimal souvenir information based on the user's past behavioral data. This makes it possible to provide local specialty product and souvenir information that match the user's preferences.

[0078] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that includes fitness plans and relaxation plans tailored to the user's health condition in ways to spend time at suggested spots. The spot suggestion unit includes fitness plans and relaxation plans tailored to the user's health condition in ways to spend time at suggested spots. For example, the spot suggestion unit includes fitness plans tailored to the user's health condition in ways to spend time at suggested spots. The generation AI suggests walking courses and jogging courses. For example, the generation AI suggests walking courses and jogging courses. The generation AI can also suggest plans that have a relaxation effect. The generation AI can also suggest relaxation plans tailored to the user's health condition. This makes it possible to provide fitness plans and relaxation plans tailored to the user's health condition.

[0079] Furthermore, the car navigation system + generative AI system includes a spot suggestion unit that uses an emotion estimation function to monitor the emotions felt by the user in real time while experiencing a spot and dynamically adjusts the experience content. The spot suggestion unit uses the emotion estimation function to monitor the emotions felt by the user in real time while experiencing a spot. For example, the spot suggestion unit uses the emotion estimation function to monitor the emotions felt by the user in real time while experiencing a spot, and the generative AI dynamically adjusts the experience content. The generative AI preferentially suggests activities that evoke positive emotions in the user. For example, the generative AI suggests similar activities based on activities that evoke positive emotions in the user in real time. The generative AI can also dynamically adjust the experience content according to the user's emotions. The generative AI can also suggest optimal activities based on changes in the user's emotions. This makes it possible to dynamically adjust the experience content according to the user's emotions.

[0080] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that introduces targeted advertising based on user behavior data when displaying ads for suggested spots, thereby maximizing advertising effectiveness. The spot suggestion unit introduces targeted advertising based on user behavior data when displaying ads for suggested spots. For example, the spot suggestion unit introduces targeted advertising based on user behavior data when displaying ads for suggested spots. The generation AI displays relevant ads based on spots the user has visited in the past and their search history. For example, the generation AI displays relevant ads based on spots the user has visited in the past and their search history. The generation AI can also display optimal ads based on user behavior data. The generation AI can also adjust the display order of ads based on user behavior data. This allows the introduction of targeted advertising based on user behavior data to maximize advertising effectiveness.

[0081] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that uses advertising copy and visuals automatically generated by the generation AI to display advertisements for suggested spots, thereby enhancing the appeal of the advertisements. The spot suggestion unit uses advertising copy and visuals automatically generated by the generation AI to display advertisements for suggested spots. For example, the spot suggestion unit uses advertising copy automatically generated by the generation AI to display advertisements for suggested spots. The generation AI generates a catchy slogan that matches the user's preferences, thereby enhancing the appeal of the advertisement. For example, the generation AI generates a catchy slogan that matches the user's preferences, thereby enhancing the appeal of the advertisement. The generation AI can also automatically generate advertisement visuals and provide them to the user. The generation AI can also automatically generate advertisement content and provide them to the user. In this way, the appeal of the advertisement can be enhanced by using advertising copy and visuals automatically generated by the generation AI.

[0082] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that uses an emotion estimation function to monitor users' emotions toward advertisements in real time and dynamically adjusts advertisement content. The spot suggestion unit uses the emotion estimation function to monitor users' emotions toward advertisements in real time. For example, the spot suggestion unit uses the emotion estimation function to monitor users' emotions toward advertisements in real time, and the generation AI dynamically adjusts advertisement content. The generation AI preferentially displays advertisements that evoke positive emotions in the user. For example, the generation AI displays similar advertisements based on advertisements that evoke positive emotions in the user in real time. The generation AI can also dynamically adjust advertisement content according to the user's emotions. The generation AI can also display optimal advertisements based on changes in the user's emotions. This enables dynamic adjustment of advertisement content according to the user's emotions.

[0083] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that introduces personalized advertisements based on the user's health condition and lifestyle into the advertisement display for the suggested spots. The spot suggestion unit introduces personalized advertisements based on the user's health condition and lifestyle into the advertisement display for the suggested spots. For example, the spot suggestion unit introduces personalized advertisements based on the user's health condition into the advertisement display for the suggested spots. The generation AI displays advertisements for health-conscious restaurants and fitness facilities. For example, the generation AI displays advertisements for health-conscious restaurants and fitness facilities. The generation AI can also display optimal advertisements based on the user's lifestyle. The generation AI can also adjust the display order of advertisements based on the user's health condition and lifestyle. This maximizes the effectiveness of advertisements by introducing personalized advertisements based on the user's health condition and lifestyle.

[0084] Furthermore, the car navigation system + generation AI system includes a spot suggestion unit that uses an emotion estimation function to monitor users' emotions toward advertisements in real time and dynamically adjusts advertisement content. The spot suggestion unit uses the emotion estimation function to monitor users' emotions toward advertisements in real time. For example, the spot suggestion unit uses the emotion estimation function to monitor users' emotions toward advertisements in real time, and the generation AI dynamically adjusts advertisement content. The generation AI preferentially displays advertisements that evoke positive emotions in the user. For example, the generation AI displays similar advertisements based on advertisements that evoke positive emotions in the user in real time. The generation AI can also dynamically adjust advertisement content according to the user's emotions. The generation AI can also display optimal advertisements based on changes in the user's emotions. This enables dynamic adjustment of advertisement content according to the user's emotions.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The car navigation + generative AI system also includes a behavioral data learning unit that uses user behavioral data to suggest music and podcasts that match the user's preferences, providing entertainment while traveling. The behavioral data learning unit uses user behavioral data to suggest music and podcasts that match the user's preferences. For example, the behavioral data learning unit analyzes the user's past music playback history and podcast listening history, allowing the generative AI to suggest music and podcasts that match the user's preferences. The generative AI provides relaxing music and interesting podcasts while traveling. For example, the generative AI suggests similar music based on music the user has liked listening to in the past. The generative AI can also suggest new podcasts based on the user's interests. The generative AI can also suggest the optimal entertainment to match the user's mood while traveling. This allows the system to provide entertainment that matches the user's preferences.

[0087] The car navigation system + generative AI system further includes a spot suggestion unit where the generative AI provides information on local specialties and souvenirs that match the user's preferences based on the user's behavioral data. The spot suggestion unit provides information on local specialties that match the user's preferences when spending time at the suggested spot. The generative AI introduces local specialties and products. For example, the generative AI introduces local specialties and products. The generative AI can also provide souvenir information based on the user's preferences. The generative AI can also provide optimal souvenir information based on the user's past behavioral data. This makes it possible to provide information on local specialties and souvenirs that match the user's preferences.

[0088] The car navigation system + generative AI system further includes a spot suggestion unit in which the generative AI includes healthy meals and relaxation facilities according to the user's health condition based on the user's behavioral data. The spot suggestion unit includes healthy meals according to the user's health condition in the suggested spots, and the generative AI makes suggestions. The generative AI suggests restaurants that offer low-calorie and low-carb menus. For example, the generative AI suggests restaurants that offer low-calorie and low-carb menus. The generative AI can also suggest facilities that have a relaxation effect. The generative AI can also suggest fitness plans according to the user's health condition. This makes it possible to make suggestions according to the user's health condition.

[0089] The car navigation system + generating AI system further includes a spot suggestion unit in which the generating AI proposes fitness and relaxation plans that suit the user's preferences based on the user's behavioral data. The spot suggestion unit includes fitness plans that suit the user's health condition in how to spend time at the suggested spots. The generating AI suggests walking and jogging courses. For example, the generating AI suggests walking and jogging courses. The generating AI can also propose plans that have a relaxation effect. The generating AI can also propose relaxation plans that suit the user's health condition. This makes it possible to provide fitness and relaxation plans that suit the user's health condition.

[0090] The car navigation + generative AI system also includes a spot suggestion unit in which the generative AI provides guided tours and event information that match the user's preferences based on the user's behavioral data. The spot suggestion unit includes guided tours automatically generated by the generative AI in ways to spend time at the suggested spots. The generative AI suggests guided tours that take in the highlights of tourist destinations. For example, the generative AI suggests guided tours that take in the highlights of tourist destinations. The generative AI can also suggest events that are optimal for the user based on event information. The generative AI can also suggest optimal guided tours based on the user's preferences. In this way, by including guided tours and event information automatically generated by the generative AI, the user's time can be enriched.

[0091] The car navigation system + generative AI system is equipped with a behavioral data learning unit that uses an emotion estimation function to monitor the emotions a user has toward specific spots in real time and dynamically adjusts the content of suggestions. The behavioral data learning unit uses the emotion estimation function to monitor the emotions a user has toward specific spots in real time. For example, the behavioral data learning unit monitors the emotions a user has toward specific spots in real time, and the generative AI dynamically adjusts the content of suggestions. The generative AI prioritizes suggestions for spots that the user has positive emotions about. For example, the generative AI suggests similar spots based on spots that the user has positive emotions about in real time. The generative AI can also dynamically adjust the content of suggestions according to the user's emotions. The generative AI can also suggest optimal spots based on changes in the user's emotions. This allows the content of suggestions to be dynamically adjusted according to the user's emotions.

[0092] The car navigation system + generative AI system includes a spot suggestion unit that uses an emotion estimation function to monitor the emotions felt by the user during their experience at a spot in real time and makes suggestions to promote a positive experience. The spot suggestion unit uses the emotion estimation function to monitor the emotions felt by the user during their experience at a spot in real time. For example, the spot suggestion unit uses the emotion estimation function to monitor the emotions felt by the user during their experience at a spot in real time, and the generative AI makes suggestions to promote a positive experience. The generative AI prioritizes suggesting activities that evoke positive emotions in the user. For example, the generative AI suggests similar activities based on activities that evoke positive emotions in the user in real time. The generative AI can also dynamically adjust the content of suggestions based on the user's emotions. The generative AI can also suggest optimal activities based on changes in the user's emotions. This enables suggestions that promote a positive experience for the user.

[0093] The car navigation system + generative AI system includes a spot suggestion unit that uses an emotion estimation function to predict the emotions the user will have toward the suggested spots and makes suggestions that elicit positive emotions. The spot suggestion unit uses the emotion estimation function to predict the emotions the user will have toward the suggested spots. For example, the spot suggestion unit uses the emotion estimation function to predict the emotions the user will have toward the suggested spots, and the generative AI makes suggestions that elicit positive emotions. The generative AI makes suggestions based on spots that the user has felt positive about in the past. For example, the generative AI suggests similar spots based on spots that the user has felt positive about in the past. The generative AI can also suggest new spots that elicit positive emotions based on changes in the user's emotions. The generative AI can also suggest activities that elicit positive emotions based on changes in the user's emotions. This makes it possible to make suggestions that elicit positive emotions from the user.

[0094] The car navigation system + generation AI system includes a spot suggestion unit that uses an emotion estimation function to monitor users' emotions toward advertisements in real time and dynamically adjusts advertisement content. The spot suggestion unit uses the emotion estimation function to monitor users' emotions toward advertisements in real time. For example, the spot suggestion unit uses the emotion estimation function to monitor users' emotions toward advertisements in real time, and the generation AI dynamically adjusts advertisement content. The generation AI preferentially displays advertisements that evoke positive emotions in the user. For example, the generation AI displays similar advertisements based on advertisements that evoke positive emotions in the user in real time. The generation AI can also dynamically adjust advertisement content according to the user's emotions. The generation AI can also display optimal advertisements based on changes in the user's emotions. This enables dynamic adjustment of advertisement content according to the user's emotions.

[0095] The car navigation system + generative AI system is equipped with a traffic congestion information acquisition unit that uses an emotion estimation function to suggest spots with a relaxing effect when the user feels stressed due to traffic congestion. The traffic congestion information acquisition unit uses the emotion estimation function to allow the generative AI to suggest spots with a relaxing effect when the user feels stressed due to traffic congestion. The generative AI suggests spots with a relaxing effect when the user is feeling stressed. For example, the generative AI suggests spots with a relaxing effect when the user is feeling stressed. The generative AI can also suggest appropriate rest times based on the user's stress level. The generative AI can also suggest optimal routes based on the user's stress level. This makes it possible to predict the user's stress level and make suggestions to reduce stress.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The behavioral data learning unit learns the user's behavioral data. For example, the behavioral data learning unit analyzes the user's search history and reservation history to learn the user's preferences. The behavioral data learning unit can also learn the user's consumption patterns based on the user's payment information. Furthermore, the behavioral data learning unit can also learn the user's movement patterns based on the user's location information. Step 2: The traffic congestion information acquisition unit acquires traffic congestion information. For example, the traffic congestion information acquisition unit acquires current traffic conditions using traffic sensors and GPS data. The traffic congestion information acquisition unit can also predict the time it will take for the traffic congestion to clear based on past traffic data. Furthermore, the traffic congestion information acquisition unit can also acquire real-time traffic congestion information using a traffic information service. Step 3: The spot suggestion unit suggests spots based on the user's preferences learned by the behavioral data learning unit. For example, the spot suggestion unit suggests nearby tourist spots based on the user's past visit history. The spot suggestion unit can also suggest short rest spots such as restaurants and cafes based on the user's preferences. Furthermore, the spot suggestion unit can also suggest optimal spots based on the user's current location and waiting time in traffic jams.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a behavioral data learning unit that learns behavioral data of a user; a congestion information acquisition unit that acquires congestion information; a spot suggestion unit that suggests spots based on the user's preferences learned by the behavior data learning unit. A system characterized by:

2. The behavioral data learning unit Predicts changes in emotions from users' past behavioral data and suggests spots that elicit positive emotions 2. The system of claim 1.

3. The traffic congestion information acquisition unit In addition to traffic congestion information, weather information and event information will be integrated to make more accurate traffic congestion predictions.

2. The system of claim 1.

4. The spot suggestion unit The system takes into account users' past reviews and ratings of the recommended spots to provide more accurate personalized suggestions.

2. The system of claim 1.

5. The spot suggestion unit The system monitors users' emotions about the experience at the suggested spots in real time and dynamically adjusts the suggestions.

2. The system of claim 1.

6. The traffic congestion information acquisition unit Predicts the user's stress level due to traffic congestion and makes suggestions to reduce stress 2. The system of claim 1.

7. The spot suggestion unit Monitor users' emotions in real time while they are experiencing the spot and make suggestions to promote a positive experience.

2. The system of claim 1.

8. The spot suggestion unit Monitor users' emotions towards ads in real time and dynamically adjust ad content 2. The system of claim 1.

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