System

Abstract

An object of a system according to an embodiment is to propose an optimum schedule before a travel and to cope with changes in weather and traffic conditions during the travel.SOLUTION: A system according to an embodiment includes a schedule proposal unit, a prediction unit, and a communication unit. The schedule proposal unit proposes an optimal schedule before travel. The prediction unit predicts weather and traffic conditions during travel. The communication unit communicates with the driver.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 technology has had the problem that it is difficult to plan an optimal schedule before traveling, and it is difficult to respond to changes in weather and traffic conditions during traveling.

[0005] The system according to the embodiment aims to propose an optimal schedule before a trip and to respond to changes in weather and traffic conditions during the trip. [Means for solving the problem]

[0006] The system according to the embodiment includes a schedule proposal unit, a prediction unit, and a communication unit. The schedule proposal unit proposes an optimal schedule before a trip. The prediction unit predicts weather and traffic conditions during the trip. The communication unit communicates with the driver. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal schedule before a trip and can respond to changes in weather and traffic conditions during the trip. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The travel schedule proposal system according to an embodiment of the present invention proposes optimal travel schedules by linking a generation AI with a Google app in order to solve the problem of traffic congestion that occurs when driving to tourist spots or traveling by car. As a result, the travel schedule proposal system can solve the problem of traffic congestion that occurs when driving to tourist spots or traveling by car, thereby enabling a comfortable and safe trip.

[0029] A travel schedule proposal system according to an embodiment includes a schedule proposal unit, a prediction unit, and a communication unit. The schedule proposal unit proposes an optimal schedule before a trip. For example, the generation AI proposes an optimal travel schedule based on information entered by a user, such as the travel destination, departure date and time, and destination. The generation AI analyzes past traffic data and weather forecasts to propose optimal departure times and routes to avoid traffic jams. The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI generates a schedule based on the prompts. The prediction unit predicts weather and traffic conditions during the trip. For example, the generation AI analyzes current traffic conditions and weather forecasts to propose new routes and rest stops to avoid traffic jams and bad weather. The generation AI predicts weather and traffic conditions in real time and suggests schedule changes as needed. The communication unit communicates with the driver. For example, the generation AI monitors the driver's condition by linking with an in-car camera and communicates as needed. The generation AI analyzes the driver's facial expression and eye movements and issues a warning if it detects signs of drowsy driving. The generation AI also manages the driver's physical condition and suggests appropriate rest times. As a result, the travel schedule proposal system according to the embodiment can solve the traffic congestion problem that occurs when driving to tourist spots or traveling by car, enabling comfortable and safe travel.

[0030] The schedule suggestion unit can analyze past traffic data and weather forecasts to suggest optimal departure times and routes to avoid congestion. In the schedule suggestion unit, for example, the generation AI analyzes past traffic data to suggest optimal departure times to avoid congestion. For example, based on past traffic data, it suggests departure times that avoid routes that are prone to congestion during specific time periods. In addition, the schedule suggestion unit can analyze weather forecasts to suggest optimal routes to avoid bad weather. For example, based on the weather forecast, it suggests routes that avoid bad weather such as rain and snow. This makes it possible to suggest optimal departure times and routes to avoid congestion.

[0031] The prediction unit can analyze current traffic conditions and weather forecasts and suggest new routes and rest stops to avoid traffic jams and bad weather. In the prediction unit, for example, the generation AI analyzes current traffic conditions and suggests new routes to avoid traffic jams. For example, based on real-time traffic data, it suggests new routes to avoid routes where traffic jams are occurring. In addition, the prediction unit can analyze weather forecasts and suggest new rest stops to avoid bad weather. For example, based on the weather forecast, it suggests rest stops to avoid bad weather such as rain and snow. This makes it possible to suggest new routes and rest stops to avoid traffic jams and bad weather.

[0032] The communication unit analyzes the driver's facial color and eye movements, and can issue a warning if it detects signs of drowsy driving. For example, the generation AI in the communication unit uses an in-car camera to analyze the driver's facial color and detect signs of drowsy driving. For example, it determines whether the driver is drowsy driving based on changes in facial color. The communication unit also analyzes the driver's eye movements and detects signs of drowsy driving. For example, it determines whether the driver is drowsy driving based on the frequency of eye opening and closing and gaze movements. This makes it possible to detect signs of drowsy driving and issue a warning.

[0033] The schedule suggestion unit can learn the user's past travel history and preferences and propose individually customized schedules. For example, the generation AI analyzes the user's past travel history and proposes travel schedules that suit the user's preferences based on data such as places visited, length of stay, and preferred activities. For example, the generation AI can suggest similar tourist spots based on ratings and reviews of tourist spots visited in the past. The schedule suggestion unit can also learn preferences for specific seasons and events from the user's past travel history and propose a schedule that includes the optimal travel time and events based on that. For example, if the user has previously preferred traveling during cherry blossom season, the generation AI can propose a schedule that includes famous cherry blossom spots. This makes it possible to propose customized schedules based on the user's past travel history and preferences.

[0034] The schedule suggestion unit can analyze the plans of other travelers in real time and suggest the optimal departure time to avoid crowded times. For example, the generation AI in the schedule suggestion unit collects the plans of other travelers in real time and suggests the optimal departure time to avoid times when congestion is expected. For example, the generation AI analyzes social media posts and reservation information from other travelers to predict times when congestion will occur. In addition, the generation AI in the schedule suggestion unit collects movement data of other travelers in real time and suggests the optimal departure time to avoid routes and times when congestion is expected. For example, the generation AI analyzes data from transportation apps and car navigation systems to calculate departure times to avoid congestion. This makes it possible to suggest the optimal departure time to avoid congestion.

[0035] The schedule suggestion unit can incorporate information on the congestion status of tourist spots and events to provide more comprehensive travel plans. For example, the generation AI in the schedule suggestion unit collects information on the congestion status of tourist spots in real time and suggests the optimal time to visit to avoid crowds. For example, it analyzes live camera footage and social media posts at tourist spots to understand the congestion status. In addition, the generation AI in the schedule suggestion unit collects event information and incorporates events that match the user's interests into the schedule. For example, it suggests events that the user can enjoy based on information on local festivals and special exhibitions. This makes it possible to provide more comprehensive travel plans by incorporating information on the congestion status of tourist spots and events.

[0036] The schedule suggestion unit can include rest points that take into account the user's health condition and physical condition. In the schedule suggestion unit, for example, the generation AI takes into account the user's health condition and physical condition and incorporates appropriate rest points into the schedule. For example, to avoid long periods of driving, the generation AI suggests appropriate rest points based on the user's health data. For example, the generation AI analyzes the user's heart rate and fatigue level and suggests appropriate rest timings. This makes it possible to include rest points that take into account the user's health condition and physical condition.

[0037] The prediction unit collects driving data of other drivers in real time and can propose the optimal route. For example, the generation AI in the prediction unit collects driving data of other drivers in real time and proposes the optimal route. For example, it analyzes the speed and route selection of other drivers and proposes a route that avoids traffic jams. The prediction unit also analyzes traffic conditions in real time based on the driving data of other drivers and proposes the optimal route. For example, it calculates the optimal route taking into account traffic accidents and construction information. This makes it possible to collect driving data of other drivers in real time and propose the optimal route.

[0038] The prediction unit collects driving data of other drivers in real time and can propose the optimal route. For example, the generation AI in the prediction unit collects driving data of other drivers in real time and proposes the optimal route. For example, it analyzes the speed and route selection of other drivers and proposes a route that avoids traffic jams. The prediction unit also analyzes traffic conditions in real time based on the driving data of other drivers and proposes the optimal route. For example, it calculates the optimal route taking into account traffic accidents and construction information. This makes it possible to collect driving data of other drivers in real time and propose the optimal route.

[0039] The prediction unit combines past weather data with current weather information to make more accurate weather forecasts. For example, the generation AI in the prediction unit combines past weather data with current weather information to make more accurate weather forecasts. For example, it learns past weather patterns and compares them with current weather information to make predictions. The prediction unit also analyzes weather patterns for specific regions and seasons based on past weather data and combines them with current weather information to make predictions. For example, it predicts weather fluctuations in specific regions based on past data. This allows for more accurate weather forecasts to be made by combining past weather data with current weather information.

[0040] The prediction unit can reflect the real-time congestion situation at tourist spots in the proposed route. For example, the generation AI collects real-time congestion information at tourist spots and proposes routes that avoid crowds. For example, the prediction unit analyzes live camera footage and social media posts at tourist spots to understand the congestion situation. The prediction unit also analyzes the congestion situation at tourist spots in real time and adjusts routes to allow users to tour efficiently. For example, it proposes routes that allow users to visit tourist spots while avoiding busy times. This allows the proposed route to reflect the real-time congestion situation at tourist spots.

[0041] The prediction unit can incorporate tourist spots and restaurants according to the user's preferences. For example, the generation AI of the prediction unit suggests tourist spots and restaurants according to the user's preferences. For example, the prediction unit incorporates the user's favorite tourist spots and restaurants into the schedule based on the user's past travel history and ratings. The prediction unit also suggests tourist spots and restaurants based on the user's interests. For example, the prediction unit incorporates places that serve the user's favorite activities and cuisine into the schedule. This makes it possible to incorporate tourist spots and restaurants according to the user's preferences.

[0042] The communication unit can analyze the driver's facial expressions and body movements to detect signs of fatigue and stress. For example, the generative AI in the communication unit uses an in-car camera to analyze the driver's facial expressions to detect signs of fatigue and stress. For example, it analyzes the frequency of eye opening and closing and the degree of facial tension to evaluate the level of fatigue. The communication unit also analyzes the driver's body movements to detect signs of fatigue and stress. For example, it analyzes changes in the driver's posture and movements to evaluate the level of fatigue and stress. This makes it possible to analyze the driver's facial expressions and body movements to detect signs of fatigue and stress.

[0043] The communication unit can analyze the driver's tone of voice and speaking style to understand their emotional state. For example, the generation AI in the communication unit analyzes the driver's tone of voice to understand their emotional state. For example, it analyzes the pitch and strength of the voice to evaluate the driver's emotional state. The communication unit also analyzes the driver's speaking style to understand their emotional state. For example, it analyzes the speed and rhythm of speech to evaluate the driver's emotional state. This makes it possible to analyze the driver's tone of voice and speaking style to understand their emotional state.

[0044] The communication unit can also monitor the condition of passengers using an in-car camera and manage the physical condition of everyone. For example, the communication unit can use an in-car camera to analyze the facial expressions and body movements of passengers and manage the physical condition of everyone. For example, it can evaluate the fatigue and stress levels of passengers and suggest appropriate times to take a break. The communication unit can also monitor the condition of passengers in real time and build a system to manage the physical condition of everyone. For example, it can analyze the facial color and eye movements of passengers to evaluate their physical condition. This makes it possible to use an in-car camera to monitor the condition of passengers and manage the physical condition of everyone.

[0045] The communication unit can use an in-vehicle camera to analyze the driver's posture and provide advice to maintain appropriate posture. The communication unit can, for example, use an in-vehicle camera to analyze the driver's posture and provide advice to maintain appropriate posture. For example, the communication unit can analyze the driver's spine curvature and shoulder position to provide advice to improve posture. The communication unit can also build a system that analyzes the driver's posture in real time and provides advice to maintain appropriate posture. For example, if the driver's posture is poor, the communication unit can provide advice to correct the posture. This makes it possible to use an in-vehicle camera to analyze the driver's posture and provide advice to maintain appropriate posture.

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

[0047] The schedule suggestion unit can learn the user's past travel history and preferences and propose individually customized schedules. For example, the generation AI analyzes the user's past travel history and proposes travel schedules that suit the user's preferences based on data such as places visited, length of stay, and preferred activities. For example, it can suggest similar tourist spots based on ratings and reviews of tourist spots visited in the past. The schedule suggestion unit can also learn preferences for specific seasons and events from the user's past travel history and propose a schedule that includes the optimal travel time and events based on that. For example, if the user has previously preferred traveling during cherry blossom season, it can propose a schedule that includes famous cherry blossom spots. This makes it possible to propose customized schedules based on the user's past travel history and preferences.

[0048] The schedule suggestion unit can analyze the plans of other travelers in real time and suggest the optimal departure time to avoid congestion. For example, the generation AI collects the plans of other travelers in real time and suggests the optimal departure time to avoid times when congestion is expected. For example, it analyzes social media posts and reservation information from other travelers to predict times when congestion will occur. In addition, the schedule suggestion unit uses the generation AI to collect travel data of other travelers in real time and suggest the optimal departure time to avoid routes and times when congestion is expected. For example, it analyzes data from transportation apps and car navigation systems to calculate departure times to avoid congestion. This makes it possible to suggest the optimal departure time to avoid congestion.

[0049] The schedule suggestion unit incorporates information on the congestion status of tourist spots and events, allowing it to provide more comprehensive travel plans. For example, the generation AI collects information on the congestion status of tourist spots in real time and suggests the optimal time to visit to avoid crowds. For example, it analyzes live camera footage and social media posts at tourist spots to understand the congestion status. In addition, the schedule suggestion unit uses the generation AI to collect event information and incorporate events that match the user's interests into the schedule. For example, it suggests events that the user can enjoy based on information on local festivals and special exhibitions. This allows it to provide more comprehensive travel plans by incorporating information on the congestion status of tourist spots and events.

[0050] The schedule suggestion unit can include rest points that take into account the user's health condition and physical condition. For example, the generation AI can incorporate appropriate rest points into the schedule by taking into account the user's health condition and physical condition. For example, it can suggest appropriate breaks to avoid long periods of driving. The schedule suggestion unit can also suggest rest points that match the user's physical condition based on the user's health data. For example, it can analyze the user's heart rate and fatigue level and suggest appropriate break times. This makes it possible to include rest points that take into account the user's health condition and physical condition.

[0051] The prediction unit collects driving data of other drivers in real time and can propose the optimal route. For example, the generation AI collects driving data of other drivers in real time and proposes the optimal route. For example, it analyzes the speed and route choices of other drivers and proposes a route that avoids traffic jams. The prediction unit also analyzes traffic conditions in real time based on the driving data of other drivers and proposes the optimal route. For example, it takes into account traffic accidents and construction information to calculate the optimal route. This makes it possible to collect driving data of other drivers in real time and propose the optimal route.

[0052] The prediction unit combines past weather data with current weather information to make more accurate weather forecasts. For example, the generation AI combines past weather data with current weather information to make more accurate weather forecasts. For example, it learns past weather patterns and compares them with current weather information to make predictions. The prediction unit also analyzes weather patterns for specific regions and seasons based on past weather data and combines them with current weather information to make predictions. For example, it predicts weather fluctuations in specific regions based on past data. This allows for more accurate weather forecasts to be made by combining past weather data with current weather information.

[0053] The prediction unit can reflect the real-time congestion situation at tourist spots in the proposed route. For example, the generation AI collects real-time congestion information at tourist spots and proposes routes that avoid crowds. For example, it analyzes live camera footage and social media posts at tourist spots to understand the congestion situation. The prediction unit also analyzes the congestion situation at tourist spots in real time and adjusts routes to allow users to tour efficiently. For example, it proposes routes that allow users to visit tourist spots while avoiding busy times. This makes it possible to reflect the real-time congestion situation at tourist spots in the proposed route.

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

[0055] Step 1: The schedule suggestion unit proposes the optimal schedule before the trip. For example, the generation AI proposes the optimal travel schedule based on information entered by the user, such as the travel destination, departure date and time, and destination. The generation AI analyzes past traffic data and weather forecasts to propose the optimal departure time and route to avoid traffic jams. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a schedule based on that prompt. Step 2: The forecasting part predicts weather and traffic conditions during the trip. For example, the generation AI analyzes current traffic conditions and weather forecasts and suggests new routes and rest stops to avoid traffic jams and bad weather. The generation AI predicts weather and traffic conditions in real time and suggests schedule changes if necessary. Step 3: The communication unit communicates with the driver. For example, the generation AI can monitor the driver's condition by linking with the in-car camera and communicate as needed. The generation AI analyzes the driver's facial expression and eye movements, and issues a warning if it detects signs of drowsiness at the wheel. The generation AI also manages the driver's physical condition and suggests appropriate times for breaks.

[0056] (Example 2) The travel schedule proposal system according to an embodiment of the present invention proposes optimal travel schedules by linking a generation AI with a Google app in order to solve the problem of traffic congestion that occurs when driving to tourist spots or traveling by car. As a result, the travel schedule proposal system can solve the problem of traffic congestion that occurs when driving to tourist spots or traveling by car, thereby enabling a comfortable and safe trip.

[0057] A travel schedule proposal system according to an embodiment includes a schedule proposal unit, a prediction unit, and a communication unit. The schedule proposal unit proposes an optimal schedule before a trip. For example, the generation AI proposes an optimal travel schedule based on information entered by a user, such as the travel destination, departure date and time, and destination. The generation AI analyzes past traffic data and weather forecasts to propose optimal departure times and routes to avoid traffic jams. The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI generates a schedule based on the prompts. The prediction unit predicts weather and traffic conditions during the trip. For example, the generation AI analyzes current traffic conditions and weather forecasts to propose new routes and rest stops to avoid traffic jams and bad weather. The generation AI predicts weather and traffic conditions in real time and suggests schedule changes as needed. The communication unit communicates with the driver. For example, the generation AI monitors the driver's condition by linking with an in-car camera and communicates as needed. The generation AI analyzes the driver's facial expression and eye movements and issues a warning if it detects signs of drowsy driving. The generation AI also manages the driver's physical condition and suggests appropriate rest times. As a result, the travel schedule proposal system according to the embodiment can solve the traffic congestion problem that occurs when driving to tourist spots or traveling by car, enabling comfortable and safe travel.

[0058] The schedule suggestion unit can analyze past traffic data and weather forecasts to suggest optimal departure times and routes to avoid congestion. In the schedule suggestion unit, for example, the generation AI analyzes past traffic data to suggest optimal departure times to avoid congestion. For example, based on past traffic data, it suggests departure times that avoid routes that are prone to congestion during specific time periods. In addition, the schedule suggestion unit can analyze weather forecasts to suggest optimal routes to avoid bad weather. For example, based on the weather forecast, it suggests routes that avoid bad weather such as rain and snow. This makes it possible to suggest optimal departure times and routes to avoid congestion.

[0059] The prediction unit can analyze current traffic conditions and weather forecasts and suggest new routes and rest stops to avoid traffic jams and bad weather. In the prediction unit, for example, the generation AI analyzes current traffic conditions and suggests new routes to avoid traffic jams. For example, based on real-time traffic data, it suggests new routes to avoid routes where traffic jams are occurring. In addition, the prediction unit can analyze weather forecasts and suggest new rest stops to avoid bad weather. For example, based on the weather forecast, it suggests rest stops to avoid bad weather such as rain and snow. This makes it possible to suggest new routes and rest stops to avoid traffic jams and bad weather.

[0060] The communication unit analyzes the driver's facial color and eye movements, and can issue a warning if it detects signs of drowsy driving. For example, the generation AI in the communication unit uses an in-car camera to analyze the driver's facial color and detect signs of drowsy driving. For example, it determines whether the driver is drowsy driving based on changes in facial color. The communication unit also analyzes the driver's eye movements and detects signs of drowsy driving. For example, it determines whether the driver is drowsy driving based on the frequency of eye opening and closing and gaze movements. This makes it possible to detect signs of drowsy driving and issue a warning.

[0061] The schedule suggestion unit can learn the user's past travel history and preferences and propose individually customized schedules. For example, the generation AI analyzes the user's past travel history and proposes travel schedules that suit the user's preferences based on data such as places visited, length of stay, and preferred activities. For example, the generation AI can suggest similar tourist spots based on ratings and reviews of tourist spots visited in the past. The schedule suggestion unit can also learn preferences for specific seasons and events from the user's past travel history and propose a schedule that includes the optimal travel time and events based on that. For example, if the user has previously preferred traveling during cherry blossom season, the generation AI can propose a schedule that includes famous cherry blossom spots. This makes it possible to propose customized schedules based on the user's past travel history and preferences.

[0062] The schedule suggestion unit can analyze the plans of other travelers in real time and suggest the optimal departure time to avoid crowded times. For example, the generation AI in the schedule suggestion unit collects the plans of other travelers in real time and suggests the optimal departure time to avoid times when congestion is expected. For example, the generation AI analyzes social media posts and reservation information from other travelers to predict times when congestion will occur. In addition, the generation AI in the schedule suggestion unit collects movement data of other travelers in real time and suggests the optimal departure time to avoid routes and times when congestion is expected. For example, the generation AI analyzes data from transportation apps and car navigation systems to calculate departure times to avoid congestion. This makes it possible to suggest the optimal departure time to avoid congestion.

[0063] The schedule suggestion unit can use the emotion estimation function to analyze the user's expectations and anxieties about the trip and propose an optimal schedule based on the results. The schedule suggestion unit, for example, uses the emotion estimation function to analyze the user's expectations and anxieties about the trip and propose an optimal schedule based on the results. For example, if the user feels like relaxing, the schedule suggestion unit proposes a schedule that includes relaxation spots. The schedule suggestion unit also analyzes the user's emotions about the trip in real time and proposes a schedule that elicits positive emotions. For example, it prioritizes the inclusion of activities that the user is looking forward to. This makes it possible to analyze the user's expectations and anxieties about the trip and propose an optimal schedule based on the results.

[0064] The schedule suggestion unit can incorporate information on the congestion status of tourist spots and events to provide more comprehensive travel plans. For example, the generation AI in the schedule suggestion unit collects information on the congestion status of tourist spots in real time and suggests the optimal time to visit to avoid crowds. For example, it analyzes live camera footage and social media posts at tourist spots to understand the congestion status. In addition, the generation AI in the schedule suggestion unit collects event information and incorporates events that match the user's interests into the schedule. For example, it suggests events that the user can enjoy based on information on local festivals and special exhibitions. This makes it possible to provide more comprehensive travel plans by incorporating information on the congestion status of tourist spots and events.

[0065] The schedule suggestion unit can include rest points that take into account the user's health condition and physical condition. In the schedule suggestion unit, for example, the generation AI takes into account the user's health condition and physical condition and incorporates appropriate rest points into the schedule. For example, to avoid long periods of driving, the generation AI suggests appropriate rest points based on the user's health data. For example, the generation AI analyzes the user's heart rate and fatigue level and suggests appropriate rest timings. This makes it possible to include rest points that take into account the user's health condition and physical condition.

[0066] The schedule suggestion unit can use the emotion estimation function to suggest a relaxation plan to reduce stress felt by the user before traveling. The schedule suggestion unit, for example, uses the emotion estimation function to analyze the stress felt by the user before traveling and suggest a relaxation plan to reduce that stress. For example, relaxation spots and spas are incorporated into the schedule. The schedule suggestion unit also analyzes the user's emotional state in real time and suggests activities to reduce stress. For example, places and activities where the user can relax are incorporated into the schedule. This makes it possible to suggest a relaxation plan to reduce stress felt by the user before traveling.

[0067] The prediction unit collects driving data of other drivers in real time and can propose the optimal route. For example, the generation AI in the prediction unit collects driving data of other drivers in real time and proposes the optimal route. For example, it analyzes the speed and route selection of other drivers and proposes a route that avoids traffic jams. The prediction unit also analyzes traffic conditions in real time based on the driving data of other drivers and proposes the optimal route. For example, it calculates the optimal route taking into account traffic accidents and construction information. This makes it possible to collect driving data of other drivers in real time and propose the optimal route.

[0068] The prediction unit collects driving data of other drivers in real time and can propose the optimal route. For example, the generation AI in the prediction unit collects driving data of other drivers in real time and proposes the optimal route. For example, it analyzes the speed and route selection of other drivers and proposes a route that avoids traffic jams. The prediction unit also analyzes traffic conditions in real time based on the driving data of other drivers and proposes the optimal route. For example, it calculates the optimal route taking into account traffic accidents and construction information. This makes it possible to collect driving data of other drivers in real time and propose the optimal route.

[0069] The prediction unit combines past weather data with current weather information to make more accurate weather forecasts. For example, the generation AI in the prediction unit combines past weather data with current weather information to make more accurate weather forecasts. For example, it learns past weather patterns and compares them with current weather information to make predictions. The prediction unit also analyzes weather patterns for specific regions and seasons based on past weather data and combines them with current weather information to make predictions. For example, it predicts weather fluctuations in specific regions based on past data. This allows for more accurate weather forecasts to be made by combining past weather data with current weather information.

[0070] The prediction unit can use the emotion estimation function to monitor the driver's stress level and suggest route changes to reduce stress. For example, the prediction unit can use the emotion estimation function to monitor the driver's stress level in real time and suggest route changes to reduce stress. For example, if the driver is feeling stressed, the prediction unit can suggest a route to avoid traffic jams. The prediction unit can also analyze the driver's stress level and suggest rest points or relaxation spots to reduce stress. For example, if the driver is tired, the prediction unit can suggest appropriate times to take a break. This makes it possible to monitor the driver's stress level and suggest route changes to reduce stress.

[0071] The prediction unit can reflect the real-time congestion situation at tourist spots in the proposed route. For example, the generation AI collects real-time congestion information at tourist spots and proposes routes that avoid crowds. For example, the prediction unit analyzes live camera footage and social media posts at tourist spots to understand the congestion situation. The prediction unit also analyzes the congestion situation at tourist spots in real time and adjusts routes to allow users to tour efficiently. For example, it proposes routes that allow users to visit tourist spots while avoiding busy times. This allows the proposed route to reflect the real-time congestion situation at tourist spots.

[0072] The prediction unit can incorporate tourist spots and restaurants according to the user's preferences. For example, the generation AI of the prediction unit suggests tourist spots and restaurants according to the user's preferences. For example, the prediction unit incorporates the user's favorite tourist spots and restaurants into the schedule based on the user's past travel history and ratings. The prediction unit also suggests tourist spots and restaurants based on the user's interests. For example, the prediction unit incorporates places that serve the user's favorite activities and cuisine into the schedule. This makes it possible to incorporate tourist spots and restaurants according to the user's preferences.

[0073] The prediction unit can use the emotion estimation function to suggest music or podcasts that match the driver's mood. For example, the prediction unit uses the emotion estimation function to analyze the driver's mood in real time and suggest music that matches that mood. For example, if the driver feels like relaxing, the prediction unit suggests relaxing music. The prediction unit also analyzes the driver's emotional state and suggests podcasts that match that mood. For example, if the driver is excited, the prediction unit suggests podcasts with calming content. This makes it possible to suggest music or podcasts that match the driver's mood.

[0074] The communication unit can analyze the driver's facial expressions and body movements to detect signs of fatigue and stress. For example, the generative AI in the communication unit uses an in-car camera to analyze the driver's facial expressions to detect signs of fatigue and stress. For example, it analyzes the frequency of eye opening and closing and the degree of facial tension to evaluate the level of fatigue. The communication unit also analyzes the driver's body movements to detect signs of fatigue and stress. For example, it analyzes changes in the driver's posture and movements to evaluate the level of fatigue and stress. This makes it possible to analyze the driver's facial expressions and body movements to detect signs of fatigue and stress.

[0075] The communication unit can analyze the driver's tone of voice and speaking style to understand their emotional state. For example, the generation AI in the communication unit analyzes the driver's tone of voice to understand their emotional state. For example, it analyzes the pitch and strength of the voice to evaluate the driver's emotional state. The communication unit also analyzes the driver's speaking style to understand their emotional state. For example, it analyzes the speed and rhythm of speech to evaluate the driver's emotional state. This makes it possible to analyze the driver's tone of voice and speaking style to understand their emotional state.

[0076] The communication unit can use the emotion estimation function to suggest a relaxation method according to the emotional state of the driver. For example, the communication unit uses the emotion estimation function to analyze the emotional state of the driver and suggest a relaxation method according to the state. For example, if the driver is feeling stressed, the communication unit suggests relaxation music. The communication unit also analyzes the emotional state of the driver in real time and suggests a relaxation method. For example, if the driver is tired, the communication unit suggests an appropriate time to take a break. This makes it possible to suggest a relaxation method according to the emotional state of the driver.

[0077] The communication unit can also monitor the condition of passengers using an in-car camera and manage the physical condition of everyone. For example, the communication unit can use an in-car camera to analyze the facial expressions and body movements of passengers and manage the physical condition of everyone. For example, it can evaluate the fatigue and stress levels of passengers and suggest appropriate times to take a break. The communication unit can also monitor the condition of passengers in real time and build a system to manage the physical condition of everyone. For example, it can analyze the facial color and eye movements of passengers to evaluate their physical condition. This makes it possible to use an in-car camera to monitor the condition of passengers and manage the physical condition of everyone.

[0078] The communication unit can use an in-vehicle camera to analyze the driver's posture and provide advice to maintain appropriate posture. The communication unit can, for example, use an in-vehicle camera to analyze the driver's posture and provide advice to maintain appropriate posture. For example, the communication unit can analyze the driver's spine curvature and shoulder position to provide advice to improve posture. The communication unit can also build a system that analyzes the driver's posture in real time and provides advice to maintain appropriate posture. For example, if the driver's posture is poor, the communication unit can provide advice to correct the posture. This makes it possible to use an in-vehicle camera to analyze the driver's posture and provide advice to maintain appropriate posture.

[0079] The communication unit can use the emotion estimation function to suggest topics to promote communication between the driver and passengers. For example, the communication unit uses the emotion estimation function to analyze the emotional states of the driver and passengers and, based on the analysis, suggest topics to promote communication. For example, it suggests topics that will relax the driver and passengers. The communication unit also builds a system that analyzes the emotional states of the driver and passengers in real time and suggests topics to promote communication. For example, it suggests topics that the driver and passengers have a common interest in. In this way, it is possible to use the emotion estimation function to suggest topics to promote communication between the driver and passengers.

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

[0081] The schedule suggestion unit can learn the user's past travel history and preferences and propose individually customized schedules. For example, the generation AI analyzes the user's past travel history and proposes travel schedules that suit the user's preferences based on data such as places visited, length of stay, and preferred activities. For example, it can suggest similar tourist spots based on ratings and reviews of tourist spots visited in the past. The schedule suggestion unit can also learn preferences for specific seasons and events from the user's past travel history and propose a schedule that includes the optimal travel time and events based on that. For example, if the user has previously preferred traveling during cherry blossom season, it can propose a schedule that includes famous cherry blossom spots. This makes it possible to propose customized schedules based on the user's past travel history and preferences.

[0082] The schedule suggestion unit can analyze the plans of other travelers in real time and suggest the optimal departure time to avoid congestion. For example, the generation AI collects the plans of other travelers in real time and suggests the optimal departure time to avoid times when congestion is expected. For example, it analyzes social media posts and reservation information from other travelers to predict times when congestion will occur. In addition, the schedule suggestion unit uses the generation AI to collect travel data of other travelers in real time and suggest the optimal departure time to avoid routes and times when congestion is expected. For example, it analyzes data from transportation apps and car navigation systems to calculate departure times to avoid congestion. This makes it possible to suggest the optimal departure time to avoid congestion.

[0083] The schedule suggestion unit can use the emotion estimation function to analyze the user's expectations and anxieties about the trip and propose an optimal schedule based on the results. For example, the emotion estimation function can be used to analyze the user's expectations and anxieties about the trip and propose an optimal schedule based on the results. For example, if the user feels like relaxing, the schedule suggestion unit can propose a schedule that includes relaxation spots. The schedule suggestion unit can also analyze the user's emotions about the trip in real time and propose a schedule that elicits positive emotions. For example, it can prioritize activities that the user is looking forward to. This makes it possible to analyze the user's expectations and anxieties about the trip and propose an optimal schedule based on the results.

[0084] The schedule suggestion unit incorporates information on the congestion status of tourist spots and events, allowing it to provide more comprehensive travel plans. For example, the generation AI collects information on the congestion status of tourist spots in real time and suggests the optimal time to visit to avoid crowds. For example, it analyzes live camera footage and social media posts at tourist spots to understand the congestion status. In addition, the schedule suggestion unit uses the generation AI to collect event information and incorporate events that match the user's interests into the schedule. For example, it suggests events that the user can enjoy based on information on local festivals and special exhibitions. This allows it to provide more comprehensive travel plans by incorporating information on the congestion status of tourist spots and events.

[0085] The schedule suggestion unit can include rest points that take into account the user's health condition and physical condition. For example, the generation AI can incorporate appropriate rest points into the schedule by taking into account the user's health condition and physical condition. For example, it can suggest appropriate breaks to avoid long periods of driving. The schedule suggestion unit can also suggest rest points that match the user's physical condition based on the user's health data. For example, it can analyze the user's heart rate and fatigue level and suggest appropriate break times. This makes it possible to include rest points that take into account the user's health condition and physical condition.

[0086] The schedule suggestion unit can use the emotion estimation function to suggest a relaxation plan to reduce stress felt by the user before traveling. For example, the emotion estimation function can be used to analyze the stress felt by the user before traveling and suggest a relaxation plan to reduce that stress. For example, relaxation spots and spas can be incorporated into the schedule. The schedule suggestion unit can also analyze the user's emotional state in real time and suggest activities to reduce stress. For example, places and activities where the user can relax can be incorporated into the schedule. This makes it possible to suggest a relaxation plan to reduce stress felt by the user before traveling.

[0087] The prediction unit collects driving data of other drivers in real time and can propose the optimal route. For example, the generation AI collects driving data of other drivers in real time and proposes the optimal route. For example, it analyzes the speed and route choices of other drivers and proposes a route that avoids traffic jams. The prediction unit also analyzes traffic conditions in real time based on the driving data of other drivers and proposes the optimal route. For example, it takes into account traffic accidents and construction information to calculate the optimal route. This makes it possible to collect driving data of other drivers in real time and propose the optimal route.

[0088] The prediction unit combines past weather data with current weather information to make more accurate weather forecasts. For example, the generation AI combines past weather data with current weather information to make more accurate weather forecasts. For example, it learns past weather patterns and compares them with current weather information to make predictions. The prediction unit also analyzes weather patterns for specific regions and seasons based on past weather data and combines them with current weather information to make predictions. For example, it predicts weather fluctuations in specific regions based on past data. This allows for more accurate weather forecasts to be made by combining past weather data with current weather information.

[0089] The prediction unit can use the emotion estimation function to monitor the driver's stress level and suggest route changes to reduce stress. For example, the emotion estimation function can be used to monitor the driver's stress level in real time and suggest route changes to reduce stress. For example, if the driver is feeling stressed, the prediction unit can suggest a route to avoid traffic jams. The prediction unit can also analyze the driver's stress level and suggest rest points or relaxation spots to reduce stress. For example, if the driver is tired, the prediction unit can suggest appropriate times to take a break. This makes it possible to monitor the driver's stress level and suggest route changes to reduce stress.

[0090] The prediction unit can reflect the real-time congestion situation at tourist spots in the proposed route. For example, the generation AI collects real-time congestion information at tourist spots and proposes routes that avoid crowds. For example, it analyzes live camera footage and social media posts at tourist spots to understand the congestion situation. The prediction unit also analyzes the congestion situation at tourist spots in real time and adjusts routes to allow users to tour efficiently. For example, it proposes routes that allow users to visit tourist spots while avoiding busy times. This makes it possible to reflect the real-time congestion situation at tourist spots in the proposed route.

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

[0092] Step 1: The schedule suggestion unit proposes the optimal schedule before the trip. For example, the generation AI proposes the optimal travel schedule based on information entered by the user, such as the travel destination, departure date and time, and destination. The generation AI analyzes past traffic data and weather forecasts to propose the optimal departure time and route to avoid traffic jams. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a schedule based on that prompt. Step 2: The forecasting part predicts weather and traffic conditions during the trip. For example, the generation AI analyzes current traffic conditions and weather forecasts and suggests new routes and rest stops to avoid traffic jams and bad weather. The generation AI predicts weather and traffic conditions in real time and suggests schedule changes if necessary. Step 3: The communication unit communicates with the driver. For example, the generation AI can monitor the driver's condition by linking with the in-car camera and communicate as needed. The generation AI analyzes the driver's facial expression and eye movements, and issues a warning if it detects signs of drowsiness at the wheel. The generation AI also manages the driver's physical condition and suggests appropriate times for breaks.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0127] 7, 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A schedule suggestion department that suggests the best schedule before traveling, a prediction unit that predicts weather and traffic conditions during the trip; A communication unit that communicates with the driver. A system characterized by:

2. The schedule proposal unit Analyzes past traffic data and weather forecasts to suggest optimal departure times and routes to avoid traffic jams 2. The system of claim 1.

3. The prediction unit Analyzes current traffic conditions and weather forecasts to suggest new routes and rest stops to avoid traffic jams and bad weather 2. The system of claim 1.

4. The communication unit The system analyzes the driver's facial expression and eye movements, and issues a warning if it detects signs of drowsy driving.

2. The system of claim 1.

5. The schedule proposal unit Learns your travel history and preferences to suggest personalized itineraries 2. The system of claim 1.

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