System

Abstract

An object of a system according to an embodiment is to integrate weather forecast, train delay information, schedule information, and the like to make an optimal recommendation to a user.SOLUTION: A system includes a weather forecast acquisition unit, a train delay information acquisition unit, a schedule analysis unit, a current location information acquisition unit, and an alarm setting unit. The weather forecast acquisition unit acquires a weather forecast. The train delay information acquisition unit acquires train delay information. The schedule analyzer analyzes the schedule information. The current location information acquisition unit acquires current location information. The alarm setting unit sets an alarm.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately integrate weather forecasts, train delay information, schedule information, etc. to provide optimal recommendations to users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal recommendations to users by integrating weather forecasts, train delay information, schedule information, and the like. [Means for solving the problem]

[0006] The system according to the embodiment includes a weather forecast acquisition unit, a train delay information acquisition unit, a schedule analysis unit, a current location information acquisition unit, and an alarm setting unit. The weather forecast acquisition unit acquires a weather forecast. The train delay information acquisition unit acquires train delay information. The schedule analysis unit analyzes schedule information. The current location information acquisition unit acquires current location information. The alarm setting unit sets an alarm. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal recommendations to users by integrating weather forecasts, train delay information, schedule information, and the like. [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) A recommendation system according to an embodiment of the present invention is a system that makes optimal recommendations to users by utilizing weather forecasts, train delay information, schedules stored in smartphones, current location information, etc. This allows the recommendation system to facilitate schedule management for users and enable smooth adjustment of travel and plans.

[0029] A recommendation system according to an embodiment includes a weather forecast acquisition unit, a train delay information acquisition unit, a schedule analysis unit, a current location information acquisition unit, and an alarm setting unit. The weather forecast acquisition unit acquires weather forecasts. For example, the weather forecast acquisition unit acquires data from the Japan Meteorological Agency. The weather forecast acquisition unit can also acquire data from private weather services. The weather forecast acquisition unit can also acquire real-time weather forecasts via the Internet. The train delay information acquisition unit acquires train delay information. For example, the train delay information acquisition unit acquires delay information from an official app of a railway company. The train delay information acquisition unit can also acquire delay information from a traffic information service. The train delay information acquisition unit can also acquire real-time delay information via the Internet. The schedule analysis unit analyzes schedule information. For example, the schedule analysis unit analyzes data from a calendar app. The schedule analysis unit can also analyze digitized data from a planner. The schedule analysis unit can also analyze reminder data in a smartphone. The current location information acquisition unit acquires current location information. For example, the current location information acquisition unit acquires GPS data. The current location information acquisition unit can also acquire Wi-Fi location information. The current location information acquisition unit can also acquire current location information using a smartphone's location information service. The alarm setting unit sets an alarm. For example, the alarm setting unit sets a time. The alarm setting unit can also set a volume. The alarm setting unit can also set a recurrence. This allows the recommendation system according to the embodiment to provide optimal recommendations to the user. For example, by creating a travel plan that takes weather forecasts and train delay information into consideration, the user can arrive at their destination on schedule. Furthermore, automating alarm settings allows the user to manage important appointments without forgetting them.

[0030] The weather forecast acquisition unit can acquire a weather forecast related to the user's plans and provide weather information that may affect the plans. The weather forecast acquisition unit, for example, acquires a weather forecast related to the user's plans and provides weather information that may affect the plans. For example, if the user plans to participate in an outdoor event, the weather forecast acquisition unit checks the weather forecast for that day and notifies the user to bring an umbrella if rain is expected. Furthermore, if the user plans to go on a trip, the weather forecast acquisition unit can check the weather forecast for the user's destination and suggest appropriate clothing and items to bring. Furthermore, if the user plans to participate in a sporting event, the weather forecast acquisition unit can check the weather forecast for that day and notify the user to make appropriate preparations. In this way, weather information that may affect the user's plans can be provided.

[0031] The train delay information acquisition unit can acquire train delay information related to the user's travel and provide delay information that may affect the user's plans. For example, the train delay information acquisition unit can acquire train delay information related to the user's travel and provide delay information that may affect the user's plans. For example, if the user plans to travel by train, the train delay information acquisition unit can check delay information for that line and suggest an alternative route if a delay occurs. The train delay information acquisition unit can also acquire train delay information for the user's commute and notify the user to leave earlier if a delay occurs. The train delay information acquisition unit can also acquire train delay information for the user's trip and suggest alternative means of transportation if a delay occurs. This makes it possible to provide delay information that may affect the user's travel.

[0032] The schedule analysis unit can analyze schedule information stored in the user's smartphone and provide information related to the schedule. The schedule analysis unit can analyze schedule information stored in the user's smartphone, for example, and provide information related to the schedule. For example, if the user plans to attend a meeting, the schedule analysis unit can check the location and start time of the meeting and suggest travel time and travel methods. Furthermore, if the user plans to meet a friend, the schedule analysis unit can check the friend's contact information and suggest ways to contact them. Furthermore, if the user plans to go on a trip, the schedule analysis unit can check detailed information about the trip and suggest necessary preparations. In this way, the user's schedule information can be analyzed and information related to the schedule can be provided.

[0033] The current location information acquisition unit can acquire current location information of the user and provide travel time and travel method related to the schedule. The current location information acquisition unit, for example, acquires current location information of the user and provides travel time and travel method related to the schedule. For example, when the user wants to know the travel time from the current location to the destination, the current location information acquisition unit can suggest the optimal travel method and travel time based on the current location information. Furthermore, when the user wants to know how to travel from the current location to the nearest station, the current location information acquisition unit can also suggest the optimal route based on the current location information. Furthermore, when the user wants to know the means of transportation from the current location to the destination, the current location information acquisition unit can also suggest the optimal means of transportation based on the current location information. In this way, travel time and travel method can be provided based on the user's current location information.

[0034] The alarm setting unit can automatically set an alarm based on the user's schedule. The alarm setting unit automatically sets an alarm based on, for example, the user's schedule. For example, if the user plans to attend a meeting at 9 a.m., the alarm setting unit sets the alarm at an appropriate time, taking into account the start time of the meeting and travel time. Furthermore, if the user plans to go on a trip, the alarm setting unit can also set the alarm at an appropriate time, taking into account the departure time and preparation time for the trip. Furthermore, if the user plans to perform an important task, the alarm setting unit can also set the alarm at an appropriate time, taking into account the start time and preparation time for the task. In this way, it is possible to automatically set an alarm based on the user's schedule.

[0035] The weather forecast acquisition unit can suggest clothing and belongings suitable for the user's schedule based on the weather forecast. The weather forecast acquisition unit, for example, suggests clothing and belongings suitable for the user's schedule based on the weather forecast. For example, the weather forecast acquisition unit may notify the user to bring a waterproof jacket and an umbrella if rain is expected. The weather forecast acquisition unit may also notify the user to bring a bottle of water for hydration on hot days. The weather forecast acquisition unit may also notify the user to wear waterproof boots if snow is expected. In this way, clothing and belongings suitable for the user's schedule can be suggested.

[0036] The weather forecast acquisition unit can automatically suggest rescheduling of an event in accordance with changes in the weather forecast. The weather forecast acquisition unit automatically suggests rescheduling of an event in accordance with changes in the weather forecast, for example. For example, if rain is predicted, the weather forecast acquisition unit suggests changing an outdoor event to an indoor event. Furthermore, if snow is predicted, the weather forecast acquisition unit can also suggest shortening travel time. Furthermore, if a storm is predicted, the weather forecast acquisition unit can also suggest postponing an event. In this way, it is possible to automatically suggest rescheduling of an event in accordance with changes in the weather forecast.

[0037] The train delay information acquisition unit can automatically reschedule the user's schedule based on the train delay information and notify relevant parties. The train delay information acquisition unit, for example, automatically reschedules the user's schedule based on the train delay information and notifies relevant parties. For example, the train delay information acquisition unit automatically reschedules the start time of the user's meeting and notifies relevant parties. The train delay information acquisition unit can also automatically reschedule the user's travel schedule and notify relevant parties. The train delay information acquisition unit can also automatically reschedule the time of the user's appointment and notify relevant parties. In this way, the schedule can be rescheduled based on the train delay information and notify relevant parties.

[0038] The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on the delay information. The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on, for example, the delay information. For example, the train delay information acquisition unit can provide real-time information on buses and suggest alternative routes. The train delay information acquisition unit can also provide real-time information on taxis and suggest alternative means. The train delay information acquisition unit can also provide real-time information on bicycles and suggest alternative means. In this way, real-time information on other means of transportation can be provided based on the delay information.

[0039] The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times. The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times, for example. For example, the schedule analysis unit can notify the user to take a break after a long meeting. The schedule analysis unit can also suggest a short walk or stretching. The schedule analysis unit can also suggest appropriate timings for eating and hydrating. In this way, the user's daily energy level can be predicted and appropriate break times can be suggested.

[0040] The schedule analysis unit can analyze schedule information and propose an optimal schedule management method based on past patterns. The schedule analysis unit can, for example, analyze schedule information and propose an optimal schedule management method based on past patterns. For example, the schedule analysis unit can create an efficient schedule based on past data. The schedule analysis unit can also propose task priorities based on past data. The schedule analysis unit can also notify appropriate break times based on past data. This makes it possible to analyze schedule information and propose an optimal schedule management method based on past patterns.

[0041] The current location information acquisition unit can analyze the user's movement history based on the current location information and propose an optimal movement route. The current location information acquisition unit can, for example, analyze the user's movement history based on the current location information and propose an optimal movement route. For example, the current location information acquisition unit can propose the shortest route based on past movement patterns. The current location information acquisition unit can also select an optimal route based on past congestion information. The current location information acquisition unit can also propose an optimal transfer route based on past usage history. This makes it possible to analyze the movement history based on the current location information and propose an optimal movement route.

[0042] The current location information acquisition unit can provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. The current location information acquisition unit can, for example, provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. For example, the current location information acquisition unit can suggest a route that avoids congested roads. The current location information acquisition unit can also suggest an alternative route to avoid crowded trains. The current location information acquisition unit can also suggest an alternative to avoid crowded restaurants. In this way, the congestion status can be provided in real time based on the current location information and a route that avoids the congestion can be suggested.

[0043] The alarm setting unit can analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. The alarm setting unit can, for example, analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. For example, the alarm setting unit notifies the user of the timing to wake up from deep sleep. The alarm setting unit can also suggest alarm settings for getting enough sleep. The alarm setting unit can also provide advice for waking up comfortably. In this way, the sleep pattern can be analyzed based on the alarm setting and an optimal wake-up time can be suggested.

[0044] The alarm setting unit can optimize the user's daily schedule based on the alarm setting and suggest efficient time management. The alarm setting unit can, for example, optimize the user's daily schedule based on the alarm setting and suggest efficient time management. For example, the alarm setting unit prioritizes important tasks in the schedule. The alarm setting unit can also notify the user to take a break after working for a long period of time. The alarm setting unit can also balance work and private time. This makes it possible to optimize the schedule based on the alarm setting and suggest efficient time management.

[0045] The alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting. The alarm setting unit, for example, suggests a morning routine for the user in conjunction with the alarm setting. For example, the alarm setting unit suggests a morning exercise routine. The alarm setting unit can also suggest a breakfast plan. The alarm setting unit can also optimize the morning schedule. In this way, the alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting.

[0046] The alarm setting unit can automatically remind the user of daily tasks based on the alarm setting. The alarm setting unit automatically reminds the user of daily tasks based on, for example, the alarm setting. For example, the alarm setting unit automatically reminds the user of important tasks. The alarm setting unit can also automatically remind the user of routine tasks. The alarm setting unit can also automatically remind the user of schedules. In this way, the user can automatically be reminded of daily tasks based on the alarm setting.

[0047] The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on the delay information. The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on, for example, the delay information. For example, the train delay information acquisition unit can provide real-time information on buses and suggest alternative routes. The train delay information acquisition unit can also provide real-time information on taxis and suggest alternative means. The train delay information acquisition unit can also provide real-time information on bicycles and suggest alternative means. In this way, real-time information on other means of transportation can be provided based on the delay information.

[0048] The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times. The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times, for example. For example, the schedule analysis unit can notify the user to take a break after a long meeting. The schedule analysis unit can also suggest a short walk or stretching. The schedule analysis unit can also suggest appropriate timings for eating and hydrating. In this way, the user's daily energy level can be predicted and appropriate break times can be suggested.

[0049] The schedule analysis unit can analyze schedule information and propose an optimal schedule management method based on past patterns. The schedule analysis unit can, for example, analyze schedule information and propose an optimal schedule management method based on past patterns. For example, the schedule analysis unit can create an efficient schedule based on past data. The schedule analysis unit can also propose task priorities based on past data. The schedule analysis unit can also notify appropriate break times based on past data. This makes it possible to analyze schedule information and propose an optimal schedule management method based on past patterns.

[0050] The current location information acquisition unit can analyze the user's movement history based on the current location information and propose an optimal movement route. The current location information acquisition unit can, for example, analyze the user's movement history based on the current location information and propose an optimal movement route. For example, the current location information acquisition unit can propose the shortest route based on past movement patterns. The current location information acquisition unit can also select an optimal route based on past congestion information. The current location information acquisition unit can also propose an optimal transfer route based on past usage history. This makes it possible to analyze the movement history based on the current location information and propose an optimal movement route.

[0051] The current location information acquisition unit can provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. The current location information acquisition unit can, for example, provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. For example, the current location information acquisition unit can suggest a route that avoids congested roads. The current location information acquisition unit can also suggest an alternative route to avoid crowded trains. The current location information acquisition unit can also suggest an alternative to avoid crowded restaurants. In this way, the congestion status can be provided in real time based on the current location information and a route that avoids the congestion can be suggested.

[0052] The alarm setting unit can analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. The alarm setting unit can, for example, analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. For example, the alarm setting unit notifies the user of the timing to wake up from deep sleep. The alarm setting unit can also suggest alarm settings for getting enough sleep. The alarm setting unit can also provide advice for waking up comfortably. In this way, the sleep pattern can be analyzed based on the alarm setting and an optimal wake-up time can be suggested.

[0053] The alarm setting unit can optimize the user's daily schedule based on the alarm setting and suggest efficient time management. The alarm setting unit can, for example, optimize the user's daily schedule based on the alarm setting and suggest efficient time management. For example, the alarm setting unit prioritizes important tasks in the schedule. The alarm setting unit can also notify the user to take a break after working for a long period of time. The alarm setting unit can also balance work and private time. This makes it possible to optimize the schedule based on the alarm setting and suggest efficient time management.

[0054] The alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting. The alarm setting unit, for example, suggests a morning routine for the user in conjunction with the alarm setting. For example, the alarm setting unit suggests a morning exercise routine. The alarm setting unit can also suggest a breakfast plan. The alarm setting unit can also optimize the morning schedule. In this way, the alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting.

[0055] The alarm setting unit can automatically remind the user of daily tasks based on the alarm setting. The alarm setting unit automatically reminds the user of daily tasks based on, for example, the alarm setting. For example, the alarm setting unit automatically reminds the user of important tasks. The alarm setting unit can also automatically remind the user of routine tasks. The alarm setting unit can also automatically remind the user of schedules. In this way, the user can automatically be reminded of daily tasks based on the alarm setting.

[0056] The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on the delay information. The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on, for example, the delay information. For example, the train delay information acquisition unit can provide real-time information on buses and suggest alternative routes. The train delay information acquisition unit can also provide real-time information on taxis and suggest alternative means. The train delay information acquisition unit can also provide real-time information on bicycles and suggest alternative means. In this way, real-time information on other means of transportation can be provided based on the delay information.

[0057] The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times. The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times, for example. For example, the schedule analysis unit can notify the user to take a break after a long meeting. The schedule analysis unit can also suggest a short walk or stretching. The schedule analysis unit can also suggest appropriate timings for eating and hydrating. In this way, the user's daily energy level can be predicted and appropriate break times can be suggested.

[0058] The schedule analysis unit can analyze schedule information and propose an optimal schedule management method based on past patterns. The schedule analysis unit can, for example, analyze schedule information and propose an optimal schedule management method based on past patterns. For example, the schedule analysis unit can create an efficient schedule based on past data. The schedule analysis unit can also propose task priorities based on past data. The schedule analysis unit can also notify appropriate break times based on past data. This makes it possible to analyze schedule information and propose an optimal schedule management method based on past patterns.

[0059] The current location information acquisition unit can analyze the user's movement history based on the current location information and propose an optimal movement route. The current location information acquisition unit can, for example, analyze the user's movement history based on the current location information and propose an optimal movement route. For example, the current location information acquisition unit can propose the shortest route based on past movement patterns. The current location information acquisition unit can also select an optimal route based on past congestion information. The current location information acquisition unit can also propose an optimal transfer route based on past usage history. This makes it possible to analyze the movement history based on the current location information and propose an optimal movement route.

[0060] The current location information acquisition unit can provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. The current location information acquisition unit can, for example, provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. For example, the current location information acquisition unit can suggest a route that avoids congested roads. The current location information acquisition unit can also suggest an alternative route to avoid crowded trains. The current location information acquisition unit can also suggest an alternative to avoid crowded restaurants. In this way, the congestion status can be provided in real time based on the current location information and a route that avoids the congestion can be suggested.

[0061] The alarm setting unit can analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. The alarm setting unit can, for example, analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. For example, the alarm setting unit notifies the user of the timing to wake up from deep sleep. The alarm setting unit can also suggest alarm settings for getting enough sleep. The alarm setting unit can also provide advice for waking up comfortably. In this way, the sleep pattern can be analyzed based on the alarm setting and an optimal wake-up time can be suggested.

[0062] The alarm setting unit can optimize the user's daily schedule based on the alarm setting and suggest efficient time management. The alarm setting unit can, for example, optimize the user's daily schedule based on the alarm setting and suggest efficient time management. For example, the alarm setting unit prioritizes important tasks in the schedule. The alarm setting unit can also notify the user to take a break after working for a long period of time. The alarm setting unit can also balance work and private time. This makes it possible to optimize the schedule based on the alarm setting and suggest efficient time management.

[0063] The alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting. The alarm setting unit, for example, suggests a morning routine for the user in conjunction with the alarm setting. For example, the alarm setting unit suggests a morning exercise routine. The alarm setting unit can also suggest a breakfast plan. The alarm setting unit can also optimize the morning schedule. In this way, the alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting.

[0064] The alarm setting unit can automatically remind the user of daily tasks based on the alarm setting. The alarm setting unit automatically reminds the user of daily tasks based on, for example, the alarm setting. For example, the alarm setting unit automatically reminds the user of important tasks. The alarm setting unit can also automatically remind the user of routine tasks. The alarm setting unit can also automatically remind the user of schedules. In this way, the user can automatically be reminded of daily tasks based on the alarm setting.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The recommendation system may further include a health management unit that acquires the user's health data and makes recommendations based on the user's health condition. For example, the health management unit may acquire the user's heart rate and sleep data and suggest appropriate exercise and rest. The health management unit may also acquire the user's dietary data and suggest a balanced meal plan. The health management unit may also monitor the user's stress level and suggest relaxation methods. This allows optimal recommendations to be made based on the user's health condition.

[0067] The recommendation system may further include a hobby analysis unit that makes recommendations based on the user's hobbies and interests. For example, the hobby analysis unit may analyze the user's musical preferences and suggest new artists and songs. The hobby analysis unit may also analyze the user's reading history and suggest books that interest them. The hobby analysis unit may also analyze the user's movie viewing history and suggest the next movie to watch. This allows optimal recommendations to be made based on the user's hobbies and interests.

[0068] The recommendation system may further include a behavior analysis unit that analyzes the user's past behavior history and makes recommendations based on the user's behavior patterns. For example, the behavior analysis unit may analyze places the user has visited in the past and suggest new places to visit. The behavior analysis unit may also analyze the user's past purchase history and suggest the next product to purchase. The behavior analysis unit may also analyze the user's past event participation history and suggest the next event to attend. This allows optimal recommendations to be made based on the user's behavior patterns.

[0069] The recommendation system may further include a social analysis unit that analyzes the user's social relationships and makes recommendations based on the social relationships. For example, the social analysis unit may analyze the user's friendships and introduce new friends. The social analysis unit may also analyze the user's past communication history and suggest people to contact next. The social analysis unit may also analyze the user's past event participation history and suggest the next social event to attend. This allows optimal recommendations to be made based on the user's social relationships.

[0070] The recommendation system may further include a learning analysis unit that analyzes the user's learning history and makes recommendations based on the user's learning patterns. For example, the learning analysis unit may analyze the user's past learning history and suggest what content to study next. The learning analysis unit may also analyze the user's learning style and suggest the optimal learning method. The learning analysis unit may also analyze the user's learning progress and suggest an appropriate learning plan. This allows the system to make optimal recommendations based on the user's learning patterns.

[0071] The recommendation system may further include a travel analysis unit that analyzes the user's travel history and makes recommendations based on the user's travel patterns. For example, the travel analysis unit may analyze the user's past travel history and suggest new travel destinations. The travel analysis unit may also analyze the user's travel style and suggest optimal travel plans. The travel analysis unit may also analyze the user's travel budget and suggest travel destinations that fit the budget. This allows optimal recommendations to be made based on the user's travel patterns.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The weather forecast acquisition unit acquires a weather forecast. For example, the weather forecast acquisition unit may acquire data from the Japan Meteorological Agency or private weather services, and may also acquire real-time weather forecasts via the Internet. Step 2: The train delay information acquisition unit acquires train delay information. For example, delay information can be acquired from an official railroad company app or a traffic information service, or real-time delay information can be acquired via the Internet. Step 3: The schedule analysis unit analyzes schedule information. For example, it can analyze data from a calendar app, digitized data from a notebook, and reminder data on a smartphone. Step 4: The current location information acquisition unit acquires current location information. For example, current location information can be acquired using GPS data, Wi-Fi location information, or a smartphone location information service. Step 5: The alarm setting unit sets the alarm, for example, the time, volume, and repeat settings.

[0074] (Example 2) A recommendation system according to an embodiment of the present invention is a system that makes optimal recommendations to users by utilizing weather forecasts, train delay information, schedules stored in smartphones, current location information, etc. This allows the recommendation system to facilitate schedule management for users and enable smooth adjustment of travel and plans.

[0075] A recommendation system according to an embodiment includes a weather forecast acquisition unit, a train delay information acquisition unit, a schedule analysis unit, a current location information acquisition unit, and an alarm setting unit. The weather forecast acquisition unit acquires weather forecasts. For example, the weather forecast acquisition unit acquires data from the Japan Meteorological Agency. The weather forecast acquisition unit can also acquire data from private weather services. The weather forecast acquisition unit can also acquire real-time weather forecasts via the Internet. The train delay information acquisition unit acquires train delay information. For example, the train delay information acquisition unit acquires delay information from an official app of a railway company. The train delay information acquisition unit can also acquire delay information from a traffic information service. The train delay information acquisition unit can also acquire real-time delay information via the Internet. The schedule analysis unit analyzes schedule information. For example, the schedule analysis unit analyzes data from a calendar app. The schedule analysis unit can also analyze digitized data from a planner. The schedule analysis unit can also analyze reminder data in a smartphone. The current location information acquisition unit acquires current location information. For example, the current location information acquisition unit acquires GPS data. The current location information acquisition unit can also acquire Wi-Fi location information. The current location information acquisition unit can also acquire current location information using a smartphone's location information service. The alarm setting unit sets an alarm. For example, the alarm setting unit sets a time. The alarm setting unit can also set a volume. The alarm setting unit can also set a recurrence. This allows the recommendation system according to the embodiment to provide optimal recommendations to the user. For example, by creating a travel plan that takes weather forecasts and train delay information into consideration, the user can arrive at their destination on schedule. Furthermore, automating alarm settings allows the user to manage important appointments without forgetting them.

[0076] The weather forecast acquisition unit can acquire a weather forecast related to the user's plans and provide weather information that may affect the plans. The weather forecast acquisition unit, for example, acquires a weather forecast related to the user's plans and provides weather information that may affect the plans. For example, if the user plans to participate in an outdoor event, the weather forecast acquisition unit checks the weather forecast for that day and notifies the user to bring an umbrella if rain is expected. Furthermore, if the user plans to go on a trip, the weather forecast acquisition unit can check the weather forecast for the user's destination and suggest appropriate clothing and items to bring. Furthermore, if the user plans to participate in a sporting event, the weather forecast acquisition unit can check the weather forecast for that day and notify the user to make appropriate preparations. In this way, weather information that may affect the user's plans can be provided.

[0077] The train delay information acquisition unit can acquire train delay information related to the user's travel and provide delay information that may affect the user's plans. For example, the train delay information acquisition unit can acquire train delay information related to the user's travel and provide delay information that may affect the user's plans. For example, if the user plans to travel by train, the train delay information acquisition unit can check delay information for that line and suggest an alternative route if a delay occurs. The train delay information acquisition unit can also acquire train delay information for the user's commute and notify the user to leave earlier if a delay occurs. The train delay information acquisition unit can also acquire train delay information for the user's trip and suggest alternative means of transportation if a delay occurs. This makes it possible to provide delay information that may affect the user's travel.

[0078] The schedule analysis unit can analyze schedule information stored in the user's smartphone and provide information related to the schedule. The schedule analysis unit can analyze schedule information stored in the user's smartphone, for example, and provide information related to the schedule. For example, if the user plans to attend a meeting, the schedule analysis unit can check the location and start time of the meeting and suggest travel time and travel methods. Furthermore, if the user plans to meet a friend, the schedule analysis unit can check the friend's contact information and suggest ways to contact them. Furthermore, if the user plans to go on a trip, the schedule analysis unit can check detailed information about the trip and suggest necessary preparations. In this way, the user's schedule information can be analyzed and information related to the schedule can be provided.

[0079] The current location information acquisition unit can acquire current location information of the user and provide travel time and travel method related to the schedule. The current location information acquisition unit, for example, acquires current location information of the user and provides travel time and travel method related to the schedule. For example, when the user wants to know the travel time from the current location to the destination, the current location information acquisition unit can suggest the optimal travel method and travel time based on the current location information. Furthermore, when the user wants to know how to travel from the current location to the nearest station, the current location information acquisition unit can also suggest the optimal route based on the current location information. Furthermore, when the user wants to know the means of transportation from the current location to the destination, the current location information acquisition unit can also suggest the optimal means of transportation based on the current location information. In this way, travel time and travel method can be provided based on the user's current location information.

[0080] The alarm setting unit can automatically set an alarm based on the user's schedule. The alarm setting unit automatically sets an alarm based on, for example, the user's schedule. For example, if the user plans to attend a meeting at 9 a.m., the alarm setting unit sets the alarm at an appropriate time, taking into account the start time of the meeting and travel time. Furthermore, if the user plans to go on a trip, the alarm setting unit can also set the alarm at an appropriate time, taking into account the departure time and preparation time for the trip. Furthermore, if the user plans to perform an important task, the alarm setting unit can also set the alarm at an appropriate time, taking into account the start time and preparation time for the task. In this way, it is possible to automatically set an alarm based on the user's schedule.

[0081] The weather forecast acquisition unit can suggest clothing and belongings suitable for the user's schedule based on the weather forecast. The weather forecast acquisition unit, for example, suggests clothing and belongings suitable for the user's schedule based on the weather forecast. For example, the weather forecast acquisition unit may notify the user to bring a waterproof jacket and an umbrella if rain is expected. The weather forecast acquisition unit may also notify the user to bring a bottle of water for hydration on hot days. The weather forecast acquisition unit may also notify the user to wear waterproof boots if snow is expected. In this way, clothing and belongings suitable for the user's schedule can be suggested.

[0082] The weather forecast acquisition unit can automatically suggest rescheduling of an event in accordance with changes in the weather forecast. The weather forecast acquisition unit automatically suggests rescheduling of an event in accordance with changes in the weather forecast, for example. For example, if rain is predicted, the weather forecast acquisition unit suggests changing an outdoor event to an indoor event. Furthermore, if snow is predicted, the weather forecast acquisition unit can also suggest shortening travel time. Furthermore, if a storm is predicted, the weather forecast acquisition unit can also suggest postponing an event. In this way, it is possible to automatically suggest rescheduling of an event in accordance with changes in the weather forecast.

[0083] The weather forecast acquisition unit can use the emotion estimation function to analyze the impact of the weather forecast on the user's emotions and provide advice to elicit positive emotions. The weather forecast acquisition unit can, for example, use the emotion estimation function to analyze the impact of the weather forecast on the user's emotions and provide advice to elicit positive emotions. For example, the weather forecast acquisition unit can suggest indoor activities that can be enjoyed on rainy days. The weather forecast acquisition unit can also suggest listening to relaxing music on cloudy days. The weather forecast acquisition unit can also suggest outdoor exercise on sunny days. In this way, the impact of the weather forecast on the user's emotions can be analyzed and advice to elicit positive emotions can be provided.

[0084] The train delay information acquisition unit can automatically reschedule the user's schedule based on the train delay information and notify relevant parties. The train delay information acquisition unit, for example, automatically reschedules the user's schedule based on the train delay information and notifies relevant parties. For example, the train delay information acquisition unit automatically reschedules the start time of the user's meeting and notifies relevant parties. The train delay information acquisition unit can also automatically reschedule the user's travel schedule and notify relevant parties. The train delay information acquisition unit can also automatically reschedule the time of the user's appointment and notify relevant parties. In this way, the schedule can be rescheduled based on the train delay information and notify relevant parties.

[0085] The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on the delay information. The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on, for example, the delay information. For example, the train delay information acquisition unit can provide real-time information on buses and suggest alternative routes. The train delay information acquisition unit can also provide real-time information on taxis and suggest alternative means. The train delay information acquisition unit can also provide real-time information on bicycles and suggest alternative means. In this way, real-time information on other means of transportation can be provided based on the delay information.

[0086] The train delay information acquisition unit can use the emotion estimation function to analyze the impact of delay information on the user's emotions and provide advice for reducing stress. The train delay information acquisition unit can, for example, use the emotion estimation function to analyze the impact of delay information on the user's emotions and provide advice for reducing stress. For example, the train delay information acquisition unit can suggest music that is relaxing during a delay. The train delay information acquisition unit can also suggest activities that can be enjoyed during a delay. The train delay information acquisition unit can also suggest relaxation methods for reducing stress during a delay. In this way, the impact of delay information on the user's emotions can be analyzed and advice for reducing stress can be provided.

[0087] The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times. The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times, for example. For example, the schedule analysis unit can notify the user to take a break after a long meeting. The schedule analysis unit can also suggest a short walk or stretching. The schedule analysis unit can also suggest appropriate timings for eating and hydrating. In this way, the user's daily energy level can be predicted and appropriate break times can be suggested.

[0088] The schedule analysis unit can analyze schedule information and propose an optimal schedule management method based on past patterns. The schedule analysis unit can, for example, analyze schedule information and propose an optimal schedule management method based on past patterns. For example, the schedule analysis unit can create an efficient schedule based on past data. The schedule analysis unit can also propose task priorities based on past data. The schedule analysis unit can also notify appropriate break times based on past data. This makes it possible to analyze schedule information and propose an optimal schedule management method based on past patterns.

[0089] The schedule analysis unit can use the emotion estimation function to analyze the user's emotions regarding the schedule and suggest schedule adjustments to reduce stress. The schedule analysis unit can, for example, use the emotion estimation function to analyze the user's emotions regarding the schedule and suggest schedule adjustments to reduce stress. For example, the schedule analysis unit can insert breaks before and after high-stress tasks. The schedule analysis unit can also incorporate enjoyable activities into the schedule. The schedule analysis unit can also incorporate time for meditation or yoga into the schedule. In this way, the user's emotions regarding the schedule can be analyzed and schedule adjustments to reduce stress can be suggested.

[0090] The current location information acquisition unit can analyze the user's movement history based on the current location information and propose an optimal movement route. The current location information acquisition unit can, for example, analyze the user's movement history based on the current location information and propose an optimal movement route. For example, the current location information acquisition unit can propose the shortest route based on past movement patterns. The current location information acquisition unit can also select an optimal route based on past congestion information. The current location information acquisition unit can also propose an optimal transfer route based on past usage history. This makes it possible to analyze the movement history based on the current location information and propose an optimal movement route.

[0091] The current location information acquisition unit can provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. The current location information acquisition unit can, for example, provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. For example, the current location information acquisition unit can suggest a route that avoids congested roads. The current location information acquisition unit can also suggest an alternative route to avoid crowded trains. The current location information acquisition unit can also suggest an alternative to avoid crowded restaurants. In this way, the congestion status can be provided in real time based on the current location information and a route that avoids the congestion can be suggested.

[0092] The current location information acquisition unit can use the emotion estimation function to analyze the impact of the current location information on the user's emotions and provide advice for a comfortable journey. The current location information acquisition unit can, for example, use the emotion estimation function to analyze the impact of the current location information on the user's emotions and provide advice for a comfortable journey. For example, the current location information acquisition unit can suggest a route to avoid congestion. The current location information acquisition unit can also suggest a quiet route. The current location information acquisition unit can also suggest a scenic route. This makes it possible to analyze the impact of the current location information on the user's emotions and provide advice for a comfortable journey.

[0093] The alarm setting unit can analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. The alarm setting unit can, for example, analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. For example, the alarm setting unit notifies the user of the timing to wake up from deep sleep. The alarm setting unit can also suggest alarm settings for getting enough sleep. The alarm setting unit can also provide advice for waking up comfortably. In this way, the sleep pattern can be analyzed based on the alarm setting and an optimal wake-up time can be suggested.

[0094] The alarm setting unit can optimize the user's daily schedule based on the alarm setting and suggest efficient time management. The alarm setting unit can, for example, optimize the user's daily schedule based on the alarm setting and suggest efficient time management. For example, the alarm setting unit prioritizes important tasks in the schedule. The alarm setting unit can also notify the user to take a break after working for a long period of time. The alarm setting unit can also balance work and private time. This makes it possible to optimize the schedule based on the alarm setting and suggest efficient time management.

[0095] The alarm setting unit can use the emotion estimation function to analyze the effect of the alarm setting on the user's emotions and suggest alarm settings for stress reduction. The alarm setting unit can, for example, use the emotion estimation function to analyze the effect of the alarm setting on the user's emotions and suggest alarm settings for stress reduction. For example, the alarm setting unit can suggest an alarm that wakes up with gentle music. The alarm setting unit can also suggest an alarm that includes an encouraging message. The alarm setting unit can also suggest stretching exercises to do after waking up. In this way, the effect of the alarm setting on the user's emotions can be analyzed and alarm settings for stress reduction can be suggested.

[0096] The alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting. The alarm setting unit, for example, suggests a morning routine for the user in conjunction with the alarm setting. For example, the alarm setting unit suggests a morning exercise routine. The alarm setting unit can also suggest a breakfast plan. The alarm setting unit can also optimize the morning schedule. In this way, the alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting.

[0097] The alarm setting unit can automatically remind the user of daily tasks based on the alarm setting. The alarm setting unit automatically reminds the user of daily tasks based on, for example, the alarm setting. For example, the alarm setting unit automatically reminds the user of important tasks. The alarm setting unit can also automatically remind the user of routine tasks. The alarm setting unit can also automatically remind the user of schedules. In this way, the user can automatically be reminded of daily tasks based on the alarm setting.

[0098] The alarm setting unit can use the emotion estimation function to collect the user's emotional response to the alarm setting and suggest an alarm sound or message based on the emotion. The alarm setting unit can, for example, use the emotion estimation function to collect the user's emotional response to the alarm setting and suggest an alarm sound or message based on the emotion. For example, the alarm setting unit can suggest an alarm sound that elicits positive emotions. The alarm setting unit can also suggest an alarm message for reducing stress. The alarm setting unit can also suggest customizing the alarm based on the emotion. In this way, the emotional response to the alarm setting can be collected and an alarm sound or message based on the emotion can be suggested.

[0099] The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on the delay information. The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on, for example, the delay information. For example, the train delay information acquisition unit can provide real-time information on buses and suggest alternative routes. The train delay information acquisition unit can also provide real-time information on taxis and suggest alternative means. The train delay information acquisition unit can also provide real-time information on bicycles and suggest alternative means. In this way, real-time information on other means of transportation can be provided based on the delay information.

[0100] The train delay information acquisition unit can use the emotion estimation function to analyze the impact of delay information on the user's emotions and provide advice for reducing stress. The train delay information acquisition unit can, for example, use the emotion estimation function to analyze the impact of delay information on the user's emotions and provide advice for reducing stress. For example, the train delay information acquisition unit can suggest music that is relaxing during a delay. The train delay information acquisition unit can also suggest activities that can be enjoyed during a delay. The train delay information acquisition unit can also suggest relaxation methods for reducing stress during a delay. In this way, the impact of delay information on the user's emotions can be analyzed and advice for reducing stress can be provided.

[0101] The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times. The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times, for example. For example, the schedule analysis unit can notify the user to take a break after a long meeting. The schedule analysis unit can also suggest a short walk or stretching. The schedule analysis unit can also suggest appropriate timings for eating and hydrating. In this way, the user's daily energy level can be predicted and appropriate break times can be suggested.

[0102] The schedule analysis unit can analyze schedule information and propose an optimal schedule management method based on past patterns. The schedule analysis unit can, for example, analyze schedule information and propose an optimal schedule management method based on past patterns. For example, the schedule analysis unit can create an efficient schedule based on past data. The schedule analysis unit can also propose task priorities based on past data. The schedule analysis unit can also notify appropriate break times based on past data. This makes it possible to analyze schedule information and propose an optimal schedule management method based on past patterns.

[0103] The schedule analysis unit can use the emotion estimation function to analyze the user's emotions regarding the schedule and suggest schedule adjustments to reduce stress. The schedule analysis unit can, for example, use the emotion estimation function to analyze the user's emotions regarding the schedule and suggest schedule adjustments to reduce stress. For example, the schedule analysis unit can insert breaks before and after high-stress tasks. The schedule analysis unit can also incorporate enjoyable activities into the schedule. The schedule analysis unit can also incorporate time for meditation or yoga into the schedule. In this way, the user's emotions regarding the schedule can be analyzed and schedule adjustments to reduce stress can be suggested.

[0104] The current location information acquisition unit can analyze the user's movement history based on the current location information and propose an optimal movement route. The current location information acquisition unit can, for example, analyze the user's movement history based on the current location information and propose an optimal movement route. For example, the current location information acquisition unit can propose the shortest route based on past movement patterns. The current location information acquisition unit can also select an optimal route based on past congestion information. The current location information acquisition unit can also propose an optimal transfer route based on past usage history. This makes it possible to analyze the movement history based on the current location information and propose an optimal movement route.

[0105] The current location information acquisition unit can provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. The current location information acquisition unit can, for example, provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. For example, the current location information acquisition unit can suggest a route that avoids congested roads. The current location information acquisition unit can also suggest an alternative route to avoid crowded trains. The current location information acquisition unit can also suggest an alternative to avoid crowded restaurants. In this way, the congestion status can be provided in real time based on the current location information and a route that avoids the congestion can be suggested.

[0106] The current location information acquisition unit can use the emotion estimation function to analyze the impact of the current location information on the user's emotions and provide advice for a comfortable journey. The current location information acquisition unit can, for example, use the emotion estimation function to analyze the impact of the current location information on the user's emotions and provide advice for a comfortable journey. For example, the current location information acquisition unit can suggest a route to avoid congestion. The current location information acquisition unit can also suggest a quiet route. The current location information acquisition unit can also suggest a scenic route. This makes it possible to analyze the impact of the current location information on the user's emotions and provide advice for a comfortable journey.

[0107] The alarm setting unit can analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. The alarm setting unit can, for example, analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. For example, the alarm setting unit notifies the user of the timing to wake up from deep sleep. The alarm setting unit can also suggest alarm settings for getting enough sleep. The alarm setting unit can also provide advice for waking up comfortably. In this way, the sleep pattern can be analyzed based on the alarm setting and an optimal wake-up time can be suggested.

[0108] The alarm setting unit can optimize the user's daily schedule based on the alarm setting and suggest efficient time management. The alarm setting unit can, for example, optimize the user's daily schedule based on the alarm setting and suggest efficient time management. For example, the alarm setting unit prioritizes important tasks in the schedule. The alarm setting unit can also notify the user to take a break after working for a long period of time. The alarm setting unit can also balance work and private time. This makes it possible to optimize the schedule based on the alarm setting and suggest efficient time management.

[0109] The alarm setting unit can use the emotion estimation function to analyze the effect of the alarm setting on the user's emotions and suggest alarm settings for stress reduction. The alarm setting unit can, for example, use the emotion estimation function to analyze the effect of the alarm setting on the user's emotions and suggest alarm settings for stress reduction. For example, the alarm setting unit can suggest an alarm that wakes up with gentle music. The alarm setting unit can also suggest an alarm that includes an encouraging message. The alarm setting unit can also suggest stretching exercises to do after waking up. In this way, the effect of the alarm setting on the user's emotions can be analyzed and alarm settings for stress reduction can be suggested.

[0110] The alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting. The alarm setting unit, for example, suggests a morning routine for the user in conjunction with the alarm setting. For example, the alarm setting unit suggests a morning exercise routine. The alarm setting unit can also suggest a breakfast plan. The alarm setting unit can also optimize the morning schedule. In this way, the alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting.

[0111] The alarm setting unit can automatically remind the user of daily tasks based on the alarm setting. The alarm setting unit automatically reminds the user of daily tasks based on, for example, the alarm setting. For example, the alarm setting unit automatically reminds the user of important tasks. The alarm setting unit can also automatically remind the user of routine tasks. The alarm setting unit can also automatically remind the user of schedules. In this way, the user can automatically be reminded of daily tasks based on the alarm setting.

[0112] The alarm setting unit can use the emotion estimation function to collect the user's emotional response to the alarm setting and suggest an alarm sound or message based on the emotion. The alarm setting unit can, for example, use the emotion estimation function to collect the user's emotional response to the alarm setting and suggest an alarm sound or message based on the emotion. For example, the alarm setting unit can suggest an alarm sound that elicits positive emotions. The alarm setting unit can also suggest an alarm message for reducing stress. The alarm setting unit can also suggest customizing the alarm based on the emotion. In this way, the emotional response to the alarm setting can be collected and an alarm sound or message based on the emotion can be suggested.

[0113] The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on the delay information. The train delay information acquisition unit can provide real-time information on other means of transportation used by the user based on, for example, the delay information. For example, the train delay information acquisition unit can provide real-time information on buses and suggest alternative routes. The train delay information acquisition unit can also provide real-time information on taxis and suggest alternative means. The train delay information acquisition unit can also provide real-time information on bicycles and suggest alternative means. In this way, real-time information on other means of transportation can be provided based on the delay information.

[0114] The train delay information acquisition unit can use the emotion estimation function to analyze the impact of delay information on the user's emotions and provide advice for reducing stress. The train delay information acquisition unit can, for example, use the emotion estimation function to analyze the impact of delay information on the user's emotions and provide advice for reducing stress. For example, the train delay information acquisition unit can suggest music that is relaxing during a delay. The train delay information acquisition unit can also suggest activities that can be enjoyed during a delay. The train delay information acquisition unit can also suggest relaxation methods for reducing stress during a delay. In this way, the impact of delay information on the user's emotions can be analyzed and advice for reducing stress can be provided.

[0115] The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times. The schedule analysis unit can predict the user's daily energy level based on the schedule information and suggest appropriate break times, for example. For example, the schedule analysis unit can notify the user to take a break after a long meeting. The schedule analysis unit can also suggest a short walk or stretching. The schedule analysis unit can also suggest appropriate timings for eating and hydrating. In this way, the user's daily energy level can be predicted and appropriate break times can be suggested.

[0116] The schedule analysis unit can analyze schedule information and propose an optimal schedule management method based on past patterns. The schedule analysis unit can, for example, analyze schedule information and propose an optimal schedule management method based on past patterns. For example, the schedule analysis unit can create an efficient schedule based on past data. The schedule analysis unit can also propose task priorities based on past data. The schedule analysis unit can also notify appropriate break times based on past data. This makes it possible to analyze schedule information and propose an optimal schedule management method based on past patterns.

[0117] The schedule analysis unit can use the emotion estimation function to analyze the user's emotions regarding the schedule and suggest schedule adjustments to reduce stress. The schedule analysis unit can, for example, use the emotion estimation function to analyze the user's emotions regarding the schedule and suggest schedule adjustments to reduce stress. For example, the schedule analysis unit can insert breaks before and after high-stress tasks. The schedule analysis unit can also incorporate enjoyable activities into the schedule. The schedule analysis unit can also incorporate time for meditation or yoga into the schedule. In this way, the user's emotions regarding the schedule can be analyzed and schedule adjustments to reduce stress can be suggested.

[0118] The current location information acquisition unit can analyze the user's movement history based on the current location information and propose an optimal movement route. The current location information acquisition unit can, for example, analyze the user's movement history based on the current location information and propose an optimal movement route. For example, the current location information acquisition unit can propose the shortest route based on past movement patterns. The current location information acquisition unit can also select an optimal route based on past congestion information. The current location information acquisition unit can also propose an optimal transfer route based on past usage history. This makes it possible to analyze the movement history based on the current location information and propose an optimal movement route.

[0119] The current location information acquisition unit can provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. The current location information acquisition unit can, for example, provide the congestion status around the user in real time based on the current location information and suggest a route that avoids the congestion. For example, the current location information acquisition unit can suggest a route that avoids congested roads. The current location information acquisition unit can also suggest an alternative route to avoid crowded trains. The current location information acquisition unit can also suggest an alternative to avoid crowded restaurants. In this way, the congestion status can be provided in real time based on the current location information and a route that avoids the congestion can be suggested.

[0120] The current location information acquisition unit can use the emotion estimation function to analyze the impact of the current location information on the user's emotions and provide advice for a comfortable journey. The current location information acquisition unit can, for example, use the emotion estimation function to analyze the impact of the current location information on the user's emotions and provide advice for a comfortable journey. For example, the current location information acquisition unit can suggest a route to avoid congestion. The current location information acquisition unit can also suggest a quiet route. The current location information acquisition unit can also suggest a scenic route. This makes it possible to analyze the impact of the current location information on the user's emotions and provide advice for a comfortable journey.

[0121] The alarm setting unit can analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. The alarm setting unit can, for example, analyze the user's sleep pattern based on the alarm setting and suggest an optimal wake-up time. For example, the alarm setting unit notifies the user of the timing to wake up from deep sleep. The alarm setting unit can also suggest alarm settings for getting enough sleep. The alarm setting unit can also provide advice for waking up comfortably. In this way, the sleep pattern can be analyzed based on the alarm setting and an optimal wake-up time can be suggested.

[0122] The alarm setting unit can optimize the user's daily schedule based on the alarm setting and suggest efficient time management. The alarm setting unit can, for example, optimize the user's daily schedule based on the alarm setting and suggest efficient time management. For example, the alarm setting unit prioritizes important tasks in the schedule. The alarm setting unit can also notify the user to take a break after working for a long period of time. The alarm setting unit can also balance work and private time. This makes it possible to optimize the schedule based on the alarm setting and suggest efficient time management.

[0123] The alarm setting unit can use the emotion estimation function to analyze the effect of the alarm setting on the user's emotions and suggest alarm settings for stress reduction. The alarm setting unit can, for example, use the emotion estimation function to analyze the effect of the alarm setting on the user's emotions and suggest alarm settings for stress reduction. For example, the alarm setting unit can suggest an alarm that wakes up with gentle music. The alarm setting unit can also suggest an alarm that includes an encouraging message. The alarm setting unit can also suggest stretching exercises to do after waking up. In this way, the effect of the alarm setting on the user's emotions can be analyzed and alarm settings for stress reduction can be suggested.

[0124] The alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting. The alarm setting unit, for example, suggests a morning routine for the user in conjunction with the alarm setting. For example, the alarm setting unit suggests a morning exercise routine. The alarm setting unit can also suggest a breakfast plan. The alarm setting unit can also optimize the morning schedule. In this way, the alarm setting unit can suggest a morning routine for the user in conjunction with the alarm setting.

[0125] The alarm setting unit can automatically remind the user of daily tasks based on the alarm setting. The alarm setting unit automatically reminds the user of daily tasks based on, for example, the alarm setting. For example, the alarm setting unit automatically reminds the user of important tasks. The alarm setting unit can also automatically remind the user of routine tasks. The alarm setting unit can also automatically remind the user of schedules. In this way, the user can automatically be reminded of daily tasks based on the alarm setting.

[0126] The alarm setting unit can use the emotion estimation function to collect the user's emotional response to the alarm setting and suggest an alarm sound or message based on the emotion. The alarm setting unit can, for example, use the emotion estimation function to collect the user's emotional response to the alarm setting and suggest an alarm sound or message based on the emotion. For example, the alarm setting unit can suggest an alarm sound that elicits positive emotions. The alarm setting unit can also suggest an alarm message for reducing stress. The alarm setting unit can also suggest customizing the alarm based on the emotion. In this way, the emotional response to the alarm setting can be collected and an alarm sound or message based on the emotion can be suggested.

[0127] The alarm setting unit can use the emotion estimation function to collect the user's emotional response to the alarm setting and suggest an alarm sound or message based on the emotion. The alarm setting unit can, for example, use the emotion estimation function to collect the user's emotional response to the alarm setting and suggest an alarm sound or message based on the emotion. For example, the alarm setting unit can suggest an alarm sound that elicits positive emotions. The alarm setting unit can also suggest an alarm message for reducing stress. The alarm setting unit can also suggest customizing the alarm based on the emotion. In this way, the emotional response to the alarm setting can be collected and an alarm sound or message based on the emotion can be suggested.

[0128] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0129] The recommendation system may further include a health management unit that acquires the user's health data and makes recommendations based on the user's health condition. For example, the health management unit may acquire the user's heart rate and sleep data and suggest appropriate exercise and rest. The health management unit may also acquire the user's dietary data and suggest a balanced meal plan. The health management unit may also monitor the user's stress level and suggest relaxation methods. This allows optimal recommendations to be made based on the user's health condition.

[0130] The recommendation system may further include a hobby analysis unit that makes recommendations based on the user's hobbies and interests. For example, the hobby analysis unit may analyze the user's musical preferences and suggest new artists and songs. The hobby analysis unit may also analyze the user's reading history and suggest books that interest them. The hobby analysis unit may also analyze the user's movie viewing history and suggest the next movie to watch. This allows optimal recommendations to be made based on the user's hobbies and interests.

[0131] The recommendation system may further include an emotion analysis unit that estimates the user's emotion and makes recommendations based on the emotion. For example, if the user is feeling stressed, the emotion analysis unit may suggest relaxation methods. If the user is tired, the emotion analysis unit may also suggest taking a rest. If the user is happy, the emotion analysis unit may also suggest activities to further enhance that emotion. This allows optimal recommendations to be made based on the user's emotion.

[0132] The recommendation system may further include a behavior analysis unit that analyzes the user's past behavior history and makes recommendations based on the user's behavior patterns. For example, the behavior analysis unit may analyze places the user has visited in the past and suggest new places to visit. The behavior analysis unit may also analyze the user's past purchase history and suggest the next product to purchase. The behavior analysis unit may also analyze the user's past event participation history and suggest the next event to attend. This allows optimal recommendations to be made based on the user's behavior patterns.

[0133] The recommendation system may further include an emotion schedule adjustment unit that estimates the user's emotions and adjusts the schedule based on the emotions. For example, if the user is feeling stressed, the emotion schedule adjustment unit adjusts the schedule to increase rest time. Also, if the user is tired, the emotion schedule adjustment unit may adjust the schedule to postpone important tasks. Also, if the user is happy, the emotion schedule adjustment unit may incorporate activities into the schedule to maintain that emotion. In this way, the schedule can be optimally adjusted based on the user's emotions.

[0134] The recommendation system may further include a social analysis unit that analyzes the user's social relationships and makes recommendations based on the social relationships. For example, the social analysis unit may analyze the user's friendships and introduce new friends. The social analysis unit may also analyze the user's past communication history and suggest people to contact next. The social analysis unit may also analyze the user's past event participation history and suggest the next social event to attend. This allows optimal recommendations to be made based on the user's social relationships.

[0135] The recommendation system may further include an emotional health management unit that estimates the user's emotions and performs health management based on the emotions. For example, if the user is feeling stressed, the emotional health management unit may suggest relaxation methods. If the user is tired, the emotional health management unit may also suggest appropriate rest. If the user is happy, the emotional health management unit may also suggest healthy activities to maintain that emotion. This allows optimal health management to be performed based on the user's emotions.

[0136] The recommendation system may further include a learning analysis unit that analyzes the user's learning history and makes recommendations based on the user's learning patterns. For example, the learning analysis unit may analyze the user's past learning history and suggest what content to study next. The learning analysis unit may also analyze the user's learning style and suggest the optimal learning method. The learning analysis unit may also analyze the user's learning progress and suggest an appropriate learning plan. This allows the system to make optimal recommendations based on the user's learning patterns.

[0137] The recommendation system may further include an emotional learning adjustment unit that estimates the user's emotions and adjusts the study plan based on the emotions. For example, if the user is feeling stressed, the emotional learning adjustment unit may adjust the study plan to shorten the study time. Also, if the user is tired, the emotional learning adjustment unit may adjust the study plan to increase breaks. Also, if the user is happy, the emotional learning adjustment unit may incorporate fun study activities into the study plan to maintain that emotion. In this way, the study plan can be optimally adjusted based on the user's emotions.

[0138] The recommendation system may further include a travel analysis unit that analyzes the user's travel history and makes recommendations based on the user's travel patterns. For example, the travel analysis unit may analyze the user's past travel history and suggest new travel destinations. The travel analysis unit may also analyze the user's travel style and suggest optimal travel plans. The travel analysis unit may also analyze the user's travel budget and suggest travel destinations that fit the budget. This allows optimal recommendations to be made based on the user's travel patterns.

[0139] The processing flow of the second embodiment will be briefly explained below.

[0140] Step 1: The weather forecast acquisition unit acquires a weather forecast. For example, the weather forecast acquisition unit may acquire data from the Japan Meteorological Agency or private weather services, and may also acquire real-time weather forecasts via the Internet. Step 2: The train delay information acquisition unit acquires train delay information. For example, delay information can be acquired from an official railroad company app or a traffic information service, or real-time delay information can be acquired via the Internet. Step 3: The schedule analysis unit analyzes schedule information. For example, it can analyze data from a calendar app, digitized data from a notebook, and reminder data on a smartphone. Step 4: The current location information acquisition unit acquires current location information. For example, current location information can be acquired using GPS data, Wi-Fi location information, or a smartphone location information service. Step 5: The alarm setting unit sets the alarm, for example, the time, volume, and repeat settings.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0160] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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]

[0208] 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 weather forecast acquisition unit that acquires a weather forecast; a train delay information acquisition unit that acquires train delay information; a schedule analysis unit that analyzes schedule information; a current location information acquisition unit that acquires current location information; an alarm setting unit that sets an alarm; A system characterized by:

2. The weather forecast acquisition unit Obtaining weather forecasts relevant to a user's schedule and providing weather information that may affect said schedule 2. The system of claim 1.

3. The train delay information acquisition unit Obtain train delay information related to the user's travel and provide delay information that may affect the user's schedule 2. The system of claim 1.

4. The schedule analysis unit Analyze the schedule information stored in the user's smartphone and provide information related to the schedule.

2. The system of claim 1.

5. The current location information acquisition unit Obtaining the user's current location and providing travel times and methods related to the appointment 2. The system of claim 1.

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