system

Abstract

The system according to the embodiment aims to predict the availability of seats during commute times and propose optimal commuting times. [Solution] A system according to an embodiment includes a reception unit, a prediction unit, and a suggestion unit. The reception unit accepts input of a user's commuting time and commuting route. The prediction unit learns past seat usage data based on the information accepted by the reception unit and predicts seat availability. The suggestion unit suggests commuting time slots based on the seat availability predicted by the prediction unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to predict seat availability during commute times and suggest optimal commuting times.

[0005] The system according to the embodiment aims to predict the availability of seats during commute times and propose optimal commuting times. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a prediction unit, and a suggestion unit. The reception unit receives input of a user's commuting time and commuting route. The prediction unit learns past seat usage data based on the information received by the reception unit and predicts seat availability. The suggestion unit suggests commuting time slots based on the seat availability predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the availability of seats during commute times and suggest optimal commute times. [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 commuter support system according to an embodiment of the present invention allows a user to input their commute time and route, and a generation AI learns past seat usage data to predict seat availability and suggest the optimal commute time. In the commuter support system, a user inputs their commute time and route, and a generation AI predicts seat availability based on past data and suggests the optimal time slot. For example, in the commuter support system, a user inputs "I would like to commute between 8:00 and 9:00." This information is input to the generation AI. The generation AI then analyzes the input information and predicts seat availability based on past data. The generation AI learns past seat usage data and predicts seat availability for a specific time slot. For example, if past data predicts that "seats are often available between 8:30 and 9:00," the generation AI suggests that time slot to the user. The generation AI matches the user's commute time with seat availability and suggests the optimal time slot. For example, if a user inputs "I would like to commute between 8:00 and 9:00," the generation AI suggests "it is easier to secure a seat if you commute between 8:30 and 9:00." This allows users to know the time periods when it is easier to secure a seat, making commuting more comfortable. This allows the commuting support system to predict seat availability and suggest the optimal commuting time period based on the user's commute time and route. For example, if the user commutes during the suggested time period, it will be easier to secure a seat and the stress of commuting will be reduced. In addition, the generating AI can improve the accuracy of its predictions by learning from past data. This allows for more accurate suggestions to be made to users.

[0029] A commuting support system according to an embodiment includes a reception unit, a prediction unit, and a suggestion unit. The reception unit accepts input of a user's commuting time and commuting route. The user's commuting time includes, but is not limited to, the morning rush hour and the evening rush hour. The commuting route includes, but is not limited to, the mode of transportation used and the stations and bus stops passed through. The prediction unit uses a generation AI to learn past seat usage data based on the information accepted by the reception unit and predict seat availability. The generation AI analyzes the data, taking into account factors such as the day of the week, weather, and specific events, and predicts seat availability during a specific time period. For example, the generation AI can predict from past data that "seats are often available between 8:30 and 9:00." The suggestion unit suggests an optimal commuting time period based on the seat availability predicted by the prediction unit. The suggestion unit can suggest an optimal time period, taking into account, for example, the user's commuting route and station options. As a result, the commuting support system according to the embodiment can predict seat availability and suggest optimal commuting times based on the user's commuting time and route. For example, by commuting during the suggested time slot, the user can more easily secure a seat and reduce the stress of commuting. Furthermore, the generation AI can improve the accuracy of predictions by learning from past data. This allows for more accurate suggestions to be made to the user.

[0030] The prediction unit can analyze data taking into account factors such as the day of the week, weather, and specific events, and predict seat availability. The prediction unit can, for example, predict seat availability taking into account differences between days of the week. For example, it can consider the difference between weekdays and weekends, or the degree of congestion on specific days of the week. The prediction unit can also predict seat availability taking into account differences in weather. For example, it can consider the effects of sunny, rainy, and snowy days. Furthermore, the prediction unit can predict seat availability taking into account the effects of specific events. For example, it can consider the effects of sporting events, concerts, festivals, etc. This improves the accuracy of seat availability predictions by taking into account factors such as the day of the week, weather, and specific events. Some or all of the above-mentioned processing in the prediction unit can be performed using a generation AI. For example, the prediction unit can input data on the day of the week, weather, and specific events into the generation AI, and have the generation AI execute a seat availability prediction.

[0031] The suggestion unit can suggest a time period taking into consideration the user's commute route and station options. The suggestion unit, for example, can suggest an optimal time period taking into consideration the user's commute route. For example, if the user passes through a specific station, the suggestion unit can suggest a time period taking into consideration the congestion level at that station. The suggestion unit can also suggest an optimal time period taking into consideration the user's station options. For example, the suggestion unit can suggest a time period taking into consideration the distinction between major stations and secondary stations. This makes it possible to suggest a more appropriate commute time period by taking into consideration the user's commute route and station options. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input data on the user's commute route and station options into AI and have the AI ​​suggest the optimal time period.

[0032] The reception unit can analyze the user's past commuting history and select an input method. For example, the reception unit can automatically display as candidates the commuting times and routes that the user frequently entered in the past. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest commuting routes to be used during specific time periods based on the user's past commuting history. In this way, the optimal input method can be provided by analyzing the user's past commuting history. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's past commuting history data into AI and have the AI ​​select the optimal input method.

[0033] When entering a commute time and a commute route, the reception unit can select options based on the user's current lifestyle and areas of interest. The reception unit, for example, suggests an optimal commute route based on the user's current lifestyle. The reception unit can also filter commute times and routes based on the user's areas of interest. The reception unit can also narrow down commute route options by taking the user's lifestyle and areas of interest into consideration. This allows filtering based on the user's lifestyle and areas of interest to suggest a more appropriate commute route. Some or all of the above-described processing in the reception unit can be performed using AI. For example, the reception unit can input data on the user's lifestyle and areas of interest into AI and have the AI ​​select an optimal commute route.

[0034] The reception unit can select an input means according to the user's input method when entering the commute time and commute route. For example, if the user uses voice input, the reception unit can input the commute time and commute route using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also input the commute time and commute route using text analysis technology. Furthermore, if the user uses image input, the reception unit can also input the commute time and commute route using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit can input data on the user's input method into AI and have the AI ​​select the optimal input means.

[0035] When inputting commuting times and routes, the reception unit can prioritize inputting highly relevant information while taking into account the user's geographical location information. The reception unit can, for example, suggest an optimal commuting route based on the user's current location. The reception unit can also filter commuting times and routes while taking into account the user's geographical location information. The reception unit can also prioritize displaying highly relevant commuting routes based on the user's geographical location information. This allows for more relevant commuting routes to be suggested by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using AI. For example, the reception unit can input the user's geographical location information data into AI and have the AI ​​suggest an optimal commuting route.

[0036] The reception unit can analyze the user's social media activity and input related information when entering the commute time and commute route. The reception unit, for example, analyzes the user's social media posts and suggests related commute routes. The reception unit can also suggest commute routes by referring to the activities of the user's friends on social media. The reception unit can also suggest commute routes based on the user's social media check-in information. In this way, by analyzing the user's social media activity, it is possible to suggest a more appropriate commute route. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit can input data on the user's social media activity into AI and have the AI ​​analyze the related information.

[0037] The reception unit can change the input method by reflecting the user's past feedback when inputting the commute time and commute route. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input method for the commute time and commute route by reflecting the user's past feedback. The reception unit can also customize the input interface based on the user's feedback. This makes it possible to provide a more appropriate input method by reflecting the user's past feedback. Some or all of the above-mentioned processing by the reception unit can be performed using AI. For example, the reception unit can input the user's past feedback data into AI and have the AI ​​change the input method.

[0038] When predicting seat availability, the prediction unit can adjust the prediction algorithm based on past data. The prediction unit, for example, generates an optimal prediction algorithm based on past seat usage data. The prediction unit can also optimize the algorithm that predicts seat availability during a specific time period from the past data. The prediction unit can also analyze past data to improve the accuracy of the prediction algorithm. In this way, optimizing the prediction algorithm based on past data improves prediction accuracy. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input past seat usage data into the generation AI and cause the generation AI to optimize the prediction algorithm.

[0039] When predicting seat availability, the prediction unit can improve prediction accuracy by taking into account the user's commuting route and station options. The prediction unit, for example, predicts the optimal seat availability based on the user's commuting route. The prediction unit can also predict seat availability by taking into account the user's station options. The prediction unit can also improve prediction accuracy based on the user's commuting route and station options. In this way, prediction accuracy is improved by taking into account the user's commuting route and station options. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input data on the user's commuting route and station options into the generation AI and cause the generation AI to improve prediction accuracy.

[0040] When predicting seat availability, the prediction unit can improve the accuracy of the prediction by referring to the user's past commuting history. The prediction unit predicts seat availability based on, for example, the user's past commuting history. The prediction unit can also analyze the user's past commuting history to improve the accuracy of the prediction. The prediction unit can also generate an optimal prediction result by referring to the user's past commuting history. In this way, by referring to the user's past commuting history, the accuracy of the prediction is improved. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input the user's past commuting history data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0041] When predicting seat availability, the prediction unit can improve prediction accuracy by taking into account the user's geographical location information. The prediction unit, for example, predicts the availability of the optimal seat based on the user's current location. The prediction unit can also predict seat availability by taking into account the user's geographical location information. The prediction unit can also improve prediction accuracy based on the user's geographical location information. In this way, prediction accuracy is improved by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input data on the user's geographical location information into the generation AI and cause the generation AI to improve prediction accuracy.

[0042] When predicting seat availability, the prediction unit can improve prediction accuracy by referring to data on related transportation facilities. For example, the prediction unit predicts seat availability based on operation data of the related transportation facilities. The prediction unit can also predict seat availability taking into account the congestion status of the related transportation facilities. The prediction unit can also improve prediction accuracy by referring to data on the related transportation facilities. In this way, prediction accuracy is improved by referring to data on the related transportation facilities. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input data on related transportation facilities into the generation AI and cause the generation AI to improve prediction accuracy.

[0043] When predicting seat availability, the prediction unit can make the prediction based on the congestion status of the user's commute route. For example, the prediction unit predicts seat availability based on the congestion status of the user's commute route. The prediction unit can also improve prediction accuracy by taking into account the congestion status of the user's commute route. The prediction unit can also generate optimal prediction results by referring to the congestion status of the user's commute route. This improves prediction accuracy by taking into account the congestion status of the user's commute route. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input congestion status data of the user's commute route into the generation AI and cause the generation AI to improve prediction accuracy.

[0044] When suggesting a commuting time slot, the suggestion unit can analyze the user's past commuting history to improve the accuracy of the suggestion. For example, the suggestion unit suggests an optimal commuting time slot based on the user's past commuting history. The suggestion unit can also analyze the user's past commuting history to improve the accuracy of the suggestion. The suggestion unit can also make optimal suggestions by referring to the user's past commuting history. In this way, the accuracy of the suggestion is improved by analyzing the user's past commuting history. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input the user's past commuting history data into AI and cause the AI ​​to improve the accuracy of the suggestion.

[0045] When suggesting a commuting time slot, the suggestion unit can change the suggestion content based on the user's current living situation and areas of interest. For example, the suggestion unit can suggest an optimal commuting time slot based on the user's current living situation. The suggestion unit can also customize the commuting time slot based on the user's areas of interest. The suggestion unit can also adjust the suggestion content taking into account the user's living situation and areas of interest. This enables more appropriate suggestions to be made by customizing the suggestion content based on the user's living situation and areas of interest. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input data on the user's living situation and areas of interest into AI and have the AI ​​change the suggestion content.

[0046] When suggesting commuting timeslots, the suggestion unit can change the suggestion method by reflecting user feedback. For example, the suggestion unit suggests optimal commuting timeslots based on user feedback. The suggestion unit can also improve the suggestion method by reflecting the user's past feedback. The suggestion unit can also customize the suggestion content by referring to the user's feedback. In this way, by reflecting the user's feedback, the suggestion method can be improved, enabling more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input user feedback data into AI and have the AI ​​change the suggestion method.

[0047] When suggesting a commuting time slot, the suggestion unit can change the suggestion content taking into account the user's geographical location information. For example, the suggestion unit can suggest an optimal commuting time slot based on the user's current location. The suggestion unit can also customize the commuting time slot taking into account the user's geographical location information. The suggestion unit can also adjust the suggestion content based on the user's geographical location information. This enables more appropriate suggestions by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input the user's geographical location information data into AI and have the AI ​​change the suggestion content.

[0048] When suggesting commuting hours, the suggestion unit can analyze the user's social media activity and make suggestions. For example, the suggestion unit can analyze the content of the user's social media posts and suggest relevant commuting hours. The suggestion unit can also suggest commuting hours based on the activity of the user's friends on social media. The suggestion unit can also suggest commuting hours based on the user's social media check-in information. This enables more appropriate suggestions to be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input data on the user's social media activity into AI and have the AI ​​execute relevant suggestions.

[0049] When suggesting a commuting time slot, the suggestion unit can change the suggestion content by reflecting the user's past feedback. For example, the suggestion unit suggests an optimal commuting time slot based on the user's past feedback. The suggestion unit can also customize the suggestion content by reflecting the user's feedback. The suggestion unit can also adjust the suggestion content by referring to the user's feedback. This enables more appropriate suggestions by reflecting the user's past feedback. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input the user's feedback data into AI and have the AI ​​change the suggestion content.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The prediction unit can collect information on traffic accidents and delays that may occur during the commute in real time based on the user's input of commuting time and commuting route, and reflect this information in the prediction. For example, if traffic accidents frequently occur during a certain time period, it can suggest avoiding that time period. Also, if delays are likely to occur on a certain route, it can suggest avoiding that route. Furthermore, the prediction unit can suggest the optimal commuting route based on real-time traffic information. This allows for more accurate predictions by taking traffic accident and delay information into account.

[0052] When considering the user's commute route and station options, the suggestion unit can make customized suggestions based on the user's preferences and past selection history. For example, if the user has preferred to use a particular station in the past, the suggestion unit can preferentially suggest routes that pass through that station. Also, if the user has preferred to use a particular means of transportation, the suggestion unit can suggest routes that include that means of transportation. Furthermore, the suggestion unit can take into account routes that the user has avoided in the past and suggest routes that should be avoided. This makes it possible to make more personalized suggestions by taking into account the user's preferences and past selection history.

[0053] The reception unit can analyze the user's past commuting history and select an input method. For example, it can automatically display as candidates the commuting times and routes that the user has frequently entered in the past. It can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest commuting routes to be used during specific time periods based on the user's past commuting history. This makes it possible to provide the optimal input method by analyzing the user's past commuting history.

[0054] When entering commuting times and routes, the reception unit can select options based on the user's current lifestyle and areas of interest. For example, the reception unit can suggest an optimal commuting route based on the user's current lifestyle. The reception unit can also filter commuting times and routes based on the user's areas of interest. The reception unit can also narrow down commuting route options by taking the user's lifestyle and areas of interest into consideration. This makes it possible to suggest more appropriate commuting routes by filtering based on the user's lifestyle and areas of interest.

[0055] When inputting the commute time and commute route, the reception unit can select an input means according to the input method of the user. For example, if the user uses voice input, the commute time and commute route can be input using voice recognition technology. If the user uses text input, the commute time and commute route can also be input using text analysis technology. If the user uses image input, the commute time and commute route can also be input using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method.

[0056] When inputting commuting times and routes, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, the reception unit can suggest an optimal commuting route based on the user's current location. The reception unit can also filter commuting times and routes in consideration of the user's geographical location information. The reception unit can also prioritize displaying highly relevant commuting routes based on the user's geographical location information. In this way, more relevant commuting routes can be suggested by taking the user's geographical location information into consideration.

[0057] When entering a commute time and commute route, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit can analyze the content of the user's social media posts and suggest related commute routes. The reception unit can also suggest commute routes based on the activity of the user's friends on social media. The reception unit can also suggest commute routes based on the user's check-in information on social media. In this way, by analyzing the user's social media activity, it is possible to suggest more appropriate commute routes.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives input of the user's commute time and commute route. The user's commute time includes, but is not limited to, the morning rush hour and the evening rush hour. The commute route includes, but is not limited to, the means of transportation used and the stations and bus stops to be passed through. Step 2: The prediction unit uses the generation AI to learn past seat usage data based on the information received by the reception unit and predict seat availability. The generation AI analyzes the data, taking into account factors such as the day of the week, weather, and specific events, and predicts seat availability during specific time periods. For example, the generation AI can predict from past data that "seats are often available between 8:30 and 9:00." Step 3: The suggestion unit suggests an optimal commuting time slot based on the seat availability predicted by the prediction unit. The suggestion unit can suggest the optimal time slot by taking into consideration, for example, the user's commuting route and station options. This makes it easier for the user to secure a seat by commuting during the suggested time slot, reducing stress during commuting.

[0060] (Example 2) A commuter support system according to an embodiment of the present invention allows a user to input their commute time and route, and a generation AI learns past seat usage data to predict seat availability and suggest the optimal commute time. In the commuter support system, a user inputs their commute time and route, and a generation AI predicts seat availability based on past data and suggests the optimal time slot. For example, in the commuter support system, a user inputs "I would like to commute between 8:00 and 9:00." This information is input to the generation AI. The generation AI then analyzes the input information and predicts seat availability based on past data. The generation AI learns past seat usage data and predicts seat availability for a specific time slot. For example, if past data predicts that "seats are often available between 8:30 and 9:00," the generation AI suggests that time slot to the user. The generation AI matches the user's commute time with seat availability and suggests the optimal time slot. For example, if a user inputs "I would like to commute between 8:00 and 9:00," the generation AI suggests "it is easier to secure a seat if you commute between 8:30 and 9:00." This allows users to know the time periods when it is easier to secure a seat, making commuting more comfortable. This allows the commuting support system to predict seat availability and suggest the optimal commuting time period based on the user's commute time and route. For example, if the user commutes during the suggested time period, it will be easier to secure a seat and the stress of commuting will be reduced. In addition, the generating AI can improve the accuracy of its predictions by learning from past data. This allows for more accurate suggestions to be made to users.

[0061] A commuting support system according to an embodiment includes a reception unit, a prediction unit, and a suggestion unit. The reception unit accepts input of a user's commuting time and commuting route. The user's commuting time includes, but is not limited to, the morning rush hour and the evening rush hour. The commuting route includes, but is not limited to, the mode of transportation used and the stations and bus stops passed through. The prediction unit uses a generation AI to learn past seat usage data based on the information accepted by the reception unit and predict seat availability. The generation AI analyzes the data, taking into account factors such as the day of the week, weather, and specific events, and predicts seat availability during a specific time period. For example, the generation AI can predict from past data that "seats are often available between 8:30 and 9:00." The suggestion unit suggests an optimal commuting time period based on the seat availability predicted by the prediction unit. The suggestion unit can suggest an optimal time period, taking into account, for example, the user's commuting route and station options. As a result, the commuting support system according to the embodiment can predict seat availability and suggest optimal commuting times based on the user's commuting time and route. For example, by commuting during the suggested time slot, the user can more easily secure a seat and reduce the stress of commuting. Furthermore, the generation AI can improve the accuracy of predictions by learning from past data. This allows for more accurate suggestions to be made to the user.

[0062] The prediction unit can analyze data taking into account factors such as the day of the week, weather, and specific events, and predict seat availability. The prediction unit can, for example, predict seat availability taking into account differences between days of the week. For example, it can consider the difference between weekdays and weekends, or the degree of congestion on specific days of the week. The prediction unit can also predict seat availability taking into account differences in weather. For example, it can consider the effects of sunny, rainy, and snowy days. Furthermore, the prediction unit can predict seat availability taking into account the effects of specific events. For example, it can consider the effects of sporting events, concerts, festivals, etc. This improves the accuracy of seat availability predictions by taking into account factors such as the day of the week, weather, and specific events. Some or all of the above-mentioned processing in the prediction unit can be performed using a generation AI. For example, the prediction unit can input data on the day of the week, weather, and specific events into the generation AI, and have the generation AI execute a seat availability prediction.

[0063] The suggestion unit can suggest a time period taking into consideration the user's commute route and station options. The suggestion unit, for example, can suggest an optimal time period taking into consideration the user's commute route. For example, if the user passes through a specific station, the suggestion unit can suggest a time period taking into consideration the congestion level at that station. The suggestion unit can also suggest an optimal time period taking into consideration the user's station options. For example, the suggestion unit can suggest a time period taking into consideration the distinction between major stations and secondary stations. This makes it possible to suggest a more appropriate commute time period by taking into consideration the user's commute route and station options. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input data on the user's commute route and station options into AI and have the AI ​​suggest the optimal time period.

[0064] The reception unit can estimate the user's emotions and change the input method for the commute time and commute route based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the commute time and commute route. This adjusts the input method according to the user's emotions, reducing stress for the user and providing a comfortable input experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The reception unit can analyze the user's past commuting history and select an input method. For example, the reception unit can automatically display as candidates the commuting times and routes that the user frequently entered in the past. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest commuting routes to be used during specific time periods based on the user's past commuting history. In this way, the optimal input method can be provided by analyzing the user's past commuting history. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's past commuting history data into AI and have the AI ​​select the optimal input method.

[0066] When entering a commute time and a commute route, the reception unit can select options based on the user's current lifestyle and areas of interest. The reception unit, for example, suggests an optimal commute route based on the user's current lifestyle. The reception unit can also filter commute times and routes based on the user's areas of interest. The reception unit can also narrow down commute route options by taking the user's lifestyle and areas of interest into consideration. This allows filtering based on the user's lifestyle and areas of interest to suggest a more appropriate commute route. Some or all of the above-described processing in the reception unit can be performed using AI. For example, the reception unit can input data on the user's lifestyle and areas of interest into AI and have the AI ​​select an optimal commute route.

[0067] The reception unit can select an input means according to the user's input method when entering the commute time and commute route. For example, if the user uses voice input, the reception unit can input the commute time and commute route using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also input the commute time and commute route using text analysis technology. Furthermore, if the user uses image input, the reception unit can also input the commute time and commute route using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit can input data on the user's input method into AI and have the AI ​​select the optimal input means.

[0068] The reception unit can estimate the user's emotions and determine the order of commuting times and routes to be entered based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize suggesting the easiest commuting route. Furthermore, if the user is relaxed, the reception unit can also suggest multiple commuting routes to increase the user's options. Furthermore, if the user is in a hurry, the reception unit can prioritize suggesting the shortest commuting route. This enables more appropriate suggestions by prioritizing commuting times and routes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] When inputting commuting times and routes, the reception unit can prioritize inputting highly relevant information while taking into account the user's geographical location information. The reception unit can, for example, suggest an optimal commuting route based on the user's current location. The reception unit can also filter commuting times and routes while taking into account the user's geographical location information. The reception unit can also prioritize displaying highly relevant commuting routes based on the user's geographical location information. This allows for more relevant commuting routes to be suggested by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using AI. For example, the reception unit can input the user's geographical location information data into AI and have the AI ​​suggest an optimal commuting route.

[0070] The reception unit can analyze the user's social media activity and input related information when entering the commute time and commute route. The reception unit, for example, analyzes the user's social media posts and suggests related commute routes. The reception unit can also suggest commute routes by referring to the activities of the user's friends on social media. The reception unit can also suggest commute routes based on the user's social media check-in information. In this way, by analyzing the user's social media activity, it is possible to suggest a more appropriate commute route. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit can input data on the user's social media activity into AI and have the AI ​​analyze the related information.

[0071] The reception unit can change the input method by reflecting the user's past feedback when inputting the commute time and commute route. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input method for the commute time and commute route by reflecting the user's past feedback. The reception unit can also customize the input interface based on the user's feedback. This makes it possible to provide a more appropriate input method by reflecting the user's past feedback. Some or all of the above-mentioned processing by the reception unit can be performed using AI. For example, the reception unit can input the user's past feedback data into AI and have the AI ​​change the input method.

[0072] The prediction unit can estimate the user's emotions and change the seat availability prediction method based on the estimated user emotions. For example, if the user is relaxed, the prediction unit generates a prediction result that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the prediction unit can generate a prediction result that allows a seat to be secured in the shortest time. Furthermore, if the user is excited, the prediction unit can generate a prediction result that adds a visually stimulating effect. This allows for adjusting the prediction method according to the user's emotions to provide a more appropriate prediction result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit is performed using the generation AI. For example, the prediction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the prediction method.

[0073] When predicting seat availability, the prediction unit can adjust the prediction algorithm based on past data. The prediction unit, for example, generates an optimal prediction algorithm based on past seat usage data. The prediction unit can also optimize the algorithm that predicts seat availability during a specific time period from the past data. The prediction unit can also analyze past data to improve the accuracy of the prediction algorithm. In this way, optimizing the prediction algorithm based on past data improves prediction accuracy. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input past seat usage data into the generation AI and cause the generation AI to optimize the prediction algorithm.

[0074] When predicting seat availability, the prediction unit can improve prediction accuracy by taking into account the user's commuting route and station options. The prediction unit, for example, predicts the optimal seat availability based on the user's commuting route. The prediction unit can also predict seat availability by taking into account the user's station options. The prediction unit can also improve prediction accuracy based on the user's commuting route and station options. In this way, prediction accuracy is improved by taking into account the user's commuting route and station options. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input data on the user's commuting route and station options into the generation AI and cause the generation AI to improve prediction accuracy.

[0075] When predicting seat availability, the prediction unit can improve the accuracy of the prediction by referring to the user's past commuting history. The prediction unit predicts seat availability based on, for example, the user's past commuting history. The prediction unit can also analyze the user's past commuting history to improve the accuracy of the prediction. The prediction unit can also generate an optimal prediction result by referring to the user's past commuting history. In this way, by referring to the user's past commuting history, the accuracy of the prediction is improved. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input the user's past commuting history data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0076] The prediction unit can estimate the user's emotions and change the order in which predicted results of seat availability are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the prediction unit can prioritize displaying the time slot in which a seat can be most reliably secured. Furthermore, if the user is relaxed, the prediction unit can display multiple predicted results to increase the user's options. Furthermore, if the user is in a hurry, the prediction unit can prioritize displaying the predicted results that will secure a seat in the shortest time. This allows for more appropriate information to be provided by adjusting the display order of the predicted results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit is performed using the generation AI. For example, the prediction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display order of the predicted results.

[0077] When predicting seat availability, the prediction unit can improve prediction accuracy by taking into account the user's geographical location information. The prediction unit, for example, predicts the availability of the optimal seat based on the user's current location. The prediction unit can also predict seat availability by taking into account the user's geographical location information. The prediction unit can also improve prediction accuracy based on the user's geographical location information. In this way, prediction accuracy is improved by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input data on the user's geographical location information into the generation AI and cause the generation AI to improve prediction accuracy.

[0078] When predicting seat availability, the prediction unit can improve prediction accuracy by referring to data on related transportation facilities. For example, the prediction unit predicts seat availability based on operation data of the related transportation facilities. The prediction unit can also predict seat availability taking into account the congestion status of the related transportation facilities. The prediction unit can also improve prediction accuracy by referring to data on the related transportation facilities. In this way, prediction accuracy is improved by referring to data on the related transportation facilities. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input data on related transportation facilities into the generation AI and cause the generation AI to improve prediction accuracy.

[0079] When predicting seat availability, the prediction unit can make the prediction based on the congestion status of the user's commute route. For example, the prediction unit predicts seat availability based on the congestion status of the user's commute route. The prediction unit can also improve prediction accuracy by taking into account the congestion status of the user's commute route. The prediction unit can also generate optimal prediction results by referring to the congestion status of the user's commute route. This improves prediction accuracy by taking into account the congestion status of the user's commute route. Some or all of the above-mentioned processing in the prediction unit is performed using a generation AI. For example, the prediction unit can input congestion status data of the user's commute route into the generation AI and cause the generation AI to improve prediction accuracy.

[0080] The suggestion unit can estimate the user's emotions and change the commuting time suggestion method based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can suggest a leisurely pace. If the user is in a hurry, the suggestion unit can suggest a time period in which a seat can be secured in the shortest time. If the user is excited, the suggestion unit can add visually stimulating effects. This allows for more appropriate suggestions by adjusting the suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit is performed using AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI adjust the suggestion method.

[0081] When suggesting a commuting time slot, the suggestion unit can analyze the user's past commuting history to improve the accuracy of the suggestion. For example, the suggestion unit suggests an optimal commuting time slot based on the user's past commuting history. The suggestion unit can also analyze the user's past commuting history to improve the accuracy of the suggestion. The suggestion unit can also make optimal suggestions by referring to the user's past commuting history. In this way, the accuracy of the suggestion is improved by analyzing the user's past commuting history. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input the user's past commuting history data into AI and cause the AI ​​to improve the accuracy of the suggestion.

[0082] When suggesting a commuting time slot, the suggestion unit can change the suggestion content based on the user's current living situation and areas of interest. For example, the suggestion unit can suggest an optimal commuting time slot based on the user's current living situation. The suggestion unit can also customize the commuting time slot based on the user's areas of interest. The suggestion unit can also adjust the suggestion content taking into account the user's living situation and areas of interest. This enables more appropriate suggestions to be made by customizing the suggestion content based on the user's living situation and areas of interest. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input data on the user's living situation and areas of interest into AI and have the AI ​​change the suggestion content.

[0083] When suggesting commuting timeslots, the suggestion unit can change the suggestion method by reflecting user feedback. For example, the suggestion unit suggests optimal commuting timeslots based on user feedback. The suggestion unit can also improve the suggestion method by reflecting the user's past feedback. The suggestion unit can also customize the suggestion content by referring to the user's feedback. In this way, by reflecting the user's feedback, the suggestion method can be improved, enabling more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input user feedback data into AI and have the AI ​​change the suggestion method.

[0084] The suggestion unit can estimate the user's emotions and determine the order of commuting time slots to be suggested based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting a time slot that will most reliably secure a seat. Furthermore, if the user is relaxed, the suggestion unit can suggest multiple commuting time slots to increase the user's options. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggesting a time slot that will secure a seat in the shortest time. This allows for more appropriate suggestions by determining the priority of commuting time slots according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit is performed using AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the order of commuting time slots.

[0085] When suggesting a commuting time slot, the suggestion unit can change the suggestion content taking into account the user's geographical location information. For example, the suggestion unit can suggest an optimal commuting time slot based on the user's current location. The suggestion unit can also customize the commuting time slot taking into account the user's geographical location information. The suggestion unit can also adjust the suggestion content based on the user's geographical location information. This enables more appropriate suggestions by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input the user's geographical location information data into AI and have the AI ​​change the suggestion content.

[0086] When suggesting commuting hours, the suggestion unit can analyze the user's social media activity and make suggestions. For example, the suggestion unit can analyze the content of the user's social media posts and suggest relevant commuting hours. The suggestion unit can also suggest commuting hours based on the activity of the user's friends on social media. The suggestion unit can also suggest commuting hours based on the user's social media check-in information. This enables more appropriate suggestions to be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input data on the user's social media activity into AI and have the AI ​​execute relevant suggestions.

[0087] When suggesting a commuting time slot, the suggestion unit can change the suggestion content by reflecting the user's past feedback. For example, the suggestion unit suggests an optimal commuting time slot based on the user's past feedback. The suggestion unit can also customize the suggestion content by reflecting the user's feedback. The suggestion unit can also adjust the suggestion content by referring to the user's feedback. This enables more appropriate suggestions by reflecting the user's past feedback. Some or all of the above-mentioned processing in the suggestion unit is performed using AI. For example, the suggestion unit can input the user's feedback data into AI and have the AI ​​change the suggestion content. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, prediction unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and accepts input of the user's commuting time and commuting route. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to learn past seat usage data and predict seat availability. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal commuting time slot based on the predicted seat availability. The suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, prediction unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and accepts input of the user's commuting time and commuting route. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to learn past seat usage data and predict seat availability. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal commuting time slot based on the predicted seat availability. The suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, prediction unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and accepts input of the user's commuting time and commuting route. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to learn past seat usage data and predict seat availability. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal commuting time slot based on the predicted seat availability. The suggestion unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, prediction unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts input of the user's commuting time and commuting route. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to learn past seat usage data and predict seat availability. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal commuting time slot based on the predicted seat availability. The suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] When accepting input of the user's commuting time and commuting route, the reception unit can also monitor the user's health condition and suggest the optimal commuting route based on that information. For example, if the user feels tired, the reception unit can suggest the most comfortable route. Also, if the user is in good health, the reception unit can suggest a slightly longer route with better scenery. Furthermore, if the user is in a hurry, the reception unit can suggest the shortest route. This allows the user to have a more comfortable commuting experience by suggesting the optimal commuting route based on their health condition.

[0090] The prediction unit can collect information on traffic accidents and delays that may occur during the commute in real time based on the user's input of commuting time and commuting route, and reflect this information in the prediction. For example, if traffic accidents frequently occur during a certain time period, it can suggest avoiding that time period. Also, if delays are likely to occur on a certain route, it can suggest avoiding that route. Furthermore, the prediction unit can suggest the optimal commuting route based on real-time traffic information. This allows for more accurate predictions by taking traffic accident and delay information into account.

[0091] When considering the user's commute route and station options, the suggestion unit can make customized suggestions based on the user's preferences and past selection history. For example, if the user has preferred to use a particular station in the past, the suggestion unit can preferentially suggest routes that pass through that station. Also, if the user has preferred to use a particular means of transportation, the suggestion unit can suggest routes that include that means of transportation. Furthermore, the suggestion unit can take into account routes that the user has avoided in the past and suggest routes that should be avoided. This makes it possible to make more personalized suggestions by taking into account the user's preferences and past selection history.

[0092] The reception unit can estimate the user's emotions and change the input method for commuting time and route based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Alternatively, if the user is in a hurry, voice input can be prioritized to enable the user to quickly input commuting time and route. In this way, adjusting the input method according to the user's emotions can reduce stress for the user and provide a comfortable input experience.

[0093] The reception unit can analyze the user's past commuting history and select an input method. For example, it can automatically display as candidates the commuting times and routes that the user has frequently entered in the past. It can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest commuting routes to be used during specific time periods based on the user's past commuting history. This makes it possible to provide the optimal input method by analyzing the user's past commuting history.

[0094] When entering commuting times and routes, the reception unit can select options based on the user's current lifestyle and areas of interest. For example, the reception unit can suggest an optimal commuting route based on the user's current lifestyle. The reception unit can also filter commuting times and routes based on the user's areas of interest. The reception unit can also narrow down commuting route options by taking the user's lifestyle and areas of interest into consideration. This makes it possible to suggest more appropriate commuting routes by filtering based on the user's lifestyle and areas of interest.

[0095] When inputting the commute time and commute route, the reception unit can select an input means according to the input method of the user. For example, if the user uses voice input, the commute time and commute route can be input using voice recognition technology. If the user uses text input, the commute time and commute route can also be input using text analysis technology. If the user uses image input, the commute time and commute route can also be input using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method.

[0096] The reception unit can estimate the user's emotions and determine the order of commuting times and routes to be input based on the estimated user emotions. For example, if the user is feeling stressed, the easiest commuting route can be suggested with priority. If the user is relaxed, multiple commuting routes can be suggested to increase the options. If the user is in a hurry, the shortest commuting route can be suggested with priority. This allows for more appropriate suggestions to be made by prioritizing commuting times and routes according to the user's emotions.

[0097] When inputting commuting times and routes, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, the reception unit can suggest an optimal commuting route based on the user's current location. The reception unit can also filter commuting times and routes in consideration of the user's geographical location information. The reception unit can also prioritize displaying highly relevant commuting routes based on the user's geographical location information. In this way, more relevant commuting routes can be suggested by taking the user's geographical location information into consideration.

[0098] When entering a commute time and commute route, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit can analyze the content of the user's social media posts and suggest related commute routes. The reception unit can also suggest commute routes based on the activity of the user's friends on social media. The reception unit can also suggest commute routes based on the user's check-in information on social media. In this way, by analyzing the user's social media activity, it is possible to suggest more appropriate commute routes.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit receives input of the user's commute time and commute route. The user's commute time includes, but is not limited to, the morning rush hour and the evening rush hour. The commute route includes, but is not limited to, the means of transportation used and the stations and bus stops to be passed through. Step 2: The prediction unit uses the generation AI to learn past seat usage data based on the information received by the reception unit and predict seat availability. The generation AI analyzes the data, taking into account factors such as the day of the week, weather, and specific events, and predicts seat availability during specific time periods. For example, the generation AI can predict from past data that "seats are often available between 8:30 and 9:00." Step 3: The suggestion unit suggests an optimal commuting time slot based on the seat availability predicted by the prediction unit. The suggestion unit can suggest the optimal time slot by taking into consideration, for example, the user's commuting route and station options. This makes it easier for the user to secure a seat by commuting during the suggested time slot, reducing stress during commuting.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 reception unit that receives input of a user's commuting time and commuting route; a prediction unit that learns past seat occupancy data and predicts seat availability based on the information received by the reception unit; a proposal unit that proposes a commuting time slot based on the seat availability predicted by the prediction unit. A system characterized by:

2. The prediction unit Analyze data to predict seat availability, taking into account factors such as the day of the week, weather, and specific events 2. The system of claim 1.

3. The proposal unit Suggests time slots based on the user's commute route and station options 2. The system of claim 1.

4. The reception unit Estimate the user's emotions and change the way they input their commute time and route based on the estimated emotions.

2. The system of claim 1.

5. The reception unit Analyze the user's past commuting history and select the input method 2. The system of claim 1.

6. The reception unit When entering commute time and route, filtering is performed based on the user's current lifestyle and interests.

2. The system of claim 1.

7. The reception unit When entering commuting time and route, select the input method according to the user's input method.

2. The system of claim 1.

8. The reception unit Estimate the user's emotions and determine the order of commuting times and routes to be entered based on the estimated user emotions.

2. The system of claim 1.

Images