System

Abstract

An object of a system according to an embodiment is to promote contribution to carbon neutrality in consideration of CO2 emissions in selection of a moving means.SOLUTION: A system includes a reception unit, a prediction unit, a calculation unit, an identification unit, and an assignment unit. The reception unit receives an input of a destination. The prediction unit performs weather prediction, traffic congestion prediction, and other seasonal variations based on the information received by the reception unit. The calculation unit calculates a travel time, an amount of money, and CO2 emissions for each transportation means on the basis of the prediction result obtained by the prediction unit. The identification unit identifies the transportation means on the basis of the information calculated by the calculation unit. The awarding unit awards points according to the amount of CO2 emission based on the transportation means identified by the identification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to take CO2 emissions into consideration when choosing a means of transportation, and there was a problem that it did not contribute sufficiently to carbon neutrality.

[0005] The system according to the embodiment aims to promote contributions to carbon neutrality by taking CO2 emissions into consideration when selecting a means of transportation. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a prediction unit, a calculation unit, an identification unit, and an assigning unit. The receiving unit accepts input of a destination. The prediction unit performs weather prediction, traffic congestion prediction, and other seasonal fluctuation prediction based on the information accepted by the receiving unit. The calculation unit calculates travel time, cost, and CO2 emissions for each mode of transportation based on the prediction results obtained by the prediction unit. The identification unit identifies the mode of transportation based on the information calculated by the calculation unit. The assigning unit assigns points according to the CO2 emissions based on the mode of transportation identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment takes into consideration CO2 emissions when selecting a means of transportation, and can promote contributions to carbon neutrality. [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 system according to an embodiment of the present invention utilizes a map app integrated with a generation AI to calculate travel time, cost, and CO2 emissions for each mode of transportation, taking into account weather forecasts, traffic congestion forecasts, and other seasonal variations. In the system, a user searches for a destination, and the generation AI calculates travel time, cost, and CO2 emissions for each mode of transportation, taking into account weather forecasts, traffic congestion forecasts, and other seasonal variations. Furthermore, during actual travel, the generation AI identifies the mode of transportation based on location information and determines CO2 emissions. Finally, points are awarded based on CO2 emissions. For example, a user opens a map app and inputs their destination, such as "from home to work." This information is input into the generation AI. Next, the generation AI analyzes the input information and calculates travel time, cost, and CO2 emissions for each mode of transportation, taking into account weather forecasts, traffic congestion forecasts, and other seasonal variations. For example, travel time, cost, and CO2 emissions are displayed for each mode of transportation, such as train, bicycle, and walking. Furthermore, during actual travel, the generation AI acquires the user's location information and identifies the mode of transportation. For example, if a user is riding a train, the generating AI will identify the train and determine the amount of CO2 emissions. Finally, points are awarded according to the amount of CO2 emissions. For example, if a user chooses a mode of transportation with lower CO2 emissions, more points are awarded. This system allows users to earn points while contributing to reducing CO2 emissions. As a result, the system is expected to contribute to reducing CO2 emissions and achieving carbon neutrality. It also has ESG benefits by promoting health and improving corporate value. For example, choosing to walk or cycle promotes health. Furthermore, by companies adopting this system, their corporate value will increase and ESG benefits will be realized.

[0029] A mobility assistance system according to an embodiment includes a reception unit, a prediction unit, a calculation unit, an identification unit, and an assignment unit. The reception unit accepts destination information input by a user. For example, a user opens a map app and enters "from home to work." This information is input to a generation AI. The prediction unit uses the generation AI to perform weather forecasts, traffic congestion forecasts, and other seasonal fluctuations based on the information accepted by the reception unit. For example, the generation AI analyzes weather data, traffic data, and seasonal data to generate a prediction result. The calculation unit calculates travel time, cost, and CO2 emissions for each mode of transportation based on the prediction result obtained by the prediction unit. For example, the generation AI calculates travel time, cost, and CO2 emissions for each mode of transportation, such as train, bicycle, and walking. The identification unit identifies the mode of transportation based on the information calculated by the calculation unit. For example, the generation AI acquires user location information and identifies the mode of transportation. The assignment unit assigns points according to CO2 emissions based on the mode of transportation identified by the identification unit. For example, more points are assigned if a mode of transportation with lower CO2 emissions is selected. As a result, the travel assistance system according to the embodiment is expected to contribute to carbon neutrality by calculating the amount of CO2 emissions during the user's travel and awarding points.

[0030] The identification unit can identify the means of transportation using the smartphone's sensors or GPS data. The identification unit can, for example, use the smartphone's acceleration sensor to detect the user's movement speed and identify the means of transportation. The identification unit can also acquire the user's location information based on GPS data and identify the means of transportation. Furthermore, the identification unit can use the smartphone's gyro sensor to detect the user's movement direction and identify the means of transportation. This improves the accuracy of identifying the means of transportation by using the smartphone's sensors and GPS data. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the identification unit can input the smartphone's sensor or GPS data into the generation AI and have the generation AI identify the means of transportation.

[0031] The reception unit may be equipped with a mechanism for obtaining user consent. For example, the reception unit may display a consent screen and obtain user consent when the user uses the map app for the first time. The reception unit may also display a screen that explains the consent details in detail before the user inputs a destination. Furthermore, the reception unit may also clarify the procedure for the user to withdraw consent. This enhances privacy protection by obtaining user consent. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may cause the generation AI to display the consent screen and explain the consent details.

[0032] The calculation unit can calculate CO2 emissions using a CO2 emission coefficient for each transportation mode. The calculation unit calculates CO2 emissions for each transportation mode, such as train, bicycle, and walking, using the CO2 emission coefficient. The calculation unit can also accurately calculate CO2 emissions for each transportation mode using the latest data. Furthermore, the calculation unit can also suggest the most environmentally friendly transportation mode based on the CO2 emission coefficient. By using the CO2 emission coefficient for each transportation mode, the accuracy of calculating CO2 emissions is improved. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input the CO2 emission coefficient for each transportation mode into the generation AI and have the generation AI calculate the CO2 emissions.

[0033] The awarding unit can set criteria for awarding points according to CO2 emissions. For example, the awarding unit can set criteria for awarding more points when a mode of transportation with low CO2 emissions is selected. The awarding unit can also set criteria for awarding fewer points when a mode of transportation with high CO2 emissions is selected. Furthermore, the awarding unit can award optimal points based on the point awarding criteria according to CO2 emissions. In this way, by setting point awarding criteria according to CO2 emissions, it is possible to increase the user's environmental awareness. Some or all of the above-mentioned processing in the awarding unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the awarding unit can input point awarding criteria according to CO2 emissions into the generation AI and leave the execution of point awarding to the generation AI.

[0034] The reception unit can analyze the user's past destination input history and suggest an appropriate input method. For example, the reception unit can automatically display destinations that the user has frequently input in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations to be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI suggest the optimal input method.

[0035] When inputting a destination, the reception unit can present input candidates based on the user's current activity status and areas of interest. For example, the reception unit presents locations related to an event the user is currently participating in as candidates. The reception unit can also present related tourist spots and stores as candidates based on the user's areas of interest. Furthermore, the reception unit can also suggest appropriate destinations depending on the user's current activity status (e.g., at work, on vacation, etc.). This improves user convenience by presenting input candidates based on the user's activity status and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the user's current activity status and areas of interest into the generation AI and cause the generation AI to present input candidates.

[0036] When inputting a destination, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the destination using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also identify the destination using image recognition technology. This improves input efficiency by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0037] When a destination is input, the reception unit can prioritize presenting highly relevant destinations by taking into account the user's geographical location information. For example, the reception unit can prioritize displaying locations close to the user's current location as candidates. The reception unit can also suggest optimal destinations by taking into account the distance from the user's current location. Furthermore, the reception unit can prioritize presenting related tourist spots and stores based on the user's current location. In this way, highly relevant destinations can be prioritized 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, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to present highly relevant destinations.

[0038] When a destination is input, the reception unit can analyze the user's social media activity and suggest related destinations. For example, the reception unit can suggest places where the user has checked in on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidates. Furthermore, the reception unit can also suggest related places by referring to the activities of the user's friends on social media. In this way, related destinations can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest related destinations.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting a destination. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0040] The prediction unit can improve the prediction algorithm by referring to past weather data and traffic data when making predictions. The prediction unit, for example, improves the accuracy of weather predictions based on past weather data. The prediction unit can also improve the accuracy of traffic congestion predictions by referring to past traffic data. Furthermore, the prediction unit can optimize the prediction algorithm based on past seasonal fluctuation data. In this way, the accuracy of the prediction algorithm is improved by referring to past data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input past weather data and traffic data into the generation AI and have the generation AI improve the prediction algorithm.

[0041] The prediction unit can customize the prediction results based on the user's current activity status and areas of interest at the time of prediction. For example, when the user is at work, the prediction unit can prioritize displaying prediction results related to the user's commute route. Furthermore, when the user is on vacation, the prediction unit can prioritize displaying prediction results related to tourist spots. Furthermore, the prediction unit can customize related prediction results based on the user's areas of interest. This improves user convenience by customizing the prediction results based on the user's activity status and areas of interest. Some or all of the above-described processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit can input data on the user's current activity status and areas of interest into the generation AI and have the generation AI customize the prediction results.

[0042] The prediction unit can improve the prediction algorithm by reflecting user feedback during prediction. The prediction unit improves the prediction algorithm based on, for example, feedback provided by the user. The prediction unit can also preferentially adopt a specific prediction method based on the user's past feedback. Furthermore, the prediction unit can analyze the user's feedback and improve the accuracy of the prediction algorithm. In this way, the accuracy of the prediction algorithm is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input user feedback data into the generation AI and cause the generation AI to improve the prediction algorithm.

[0043] The prediction unit can provide an appropriate prediction result by taking into account the user's geographical location information when making a prediction. The prediction unit, for example, provides an optimal weather forecast based on the user's current location. The prediction unit can also provide an optimal traffic congestion forecast by taking into account the distance from the user's current location. Furthermore, the prediction unit can also provide a related seasonal variation forecast based on the user's current location. This allows for optimal prediction results to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the prediction unit can be performed using, or without, a generation AI. For example, the prediction unit can input the user's geographical location information into the generation AI and cause the generation AI to provide an optimal prediction result.

[0044] At the time of prediction, the prediction unit can analyze the user's social media activity and provide related prediction results. For example, the prediction unit can provide prediction results regarding places where the user has checked in on social media. The prediction unit can also analyze the content of the user's social media posts and provide related prediction results. Furthermore, the prediction unit can provide related prediction results by referring to the activities of the user's friends on social media. In this way, related prediction results can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related prediction results.

[0045] The prediction unit can customize the prediction result by reflecting the user's past feedback when making a prediction. The prediction unit can provide an optimal prediction result based on, for example, feedback provided by the user in the past. The prediction unit can also preferentially adopt a specific prediction method based on the user's past feedback. Furthermore, the prediction unit can analyze the user's past feedback and customize the prediction result. This allows the prediction result to be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the prediction unit can input the user's past feedback data into the generation AI and have the generation AI customize the prediction result.

[0046] The calculation unit can improve the calculation algorithm using a CO2 emission coefficient for each transportation mode during calculation. The calculation unit optimizes the calculation algorithm using a CO2 emission coefficient for each transportation mode, such as train, bicycle, or walking. The calculation unit can also use the latest data to accurately calculate the CO2 emissions for each transportation mode. Furthermore, the calculation unit can also suggest the most environmentally friendly transportation mode based on the CO2 emission coefficient. In this way, using the CO2 emission coefficient for each transportation mode improves the accuracy of the calculation algorithm. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the calculation unit can input the CO2 emission coefficient for each transportation mode into the generation AI and have the generation AI improve the calculation algorithm.

[0047] The calculation unit can customize the calculation results based on the user's current activity status and areas of interest during calculation. For example, when the user is at work, the calculation unit can prioritize displaying calculation results related to commuting routes. Furthermore, when the user is on vacation, the calculation unit can prioritize displaying calculation results related to tourist spots. Furthermore, the calculation unit can customize related calculation results based on the user's areas of interest. This improves user convenience by customizing the calculation results based on the user's activity status and areas of interest. Some or all of the above-described processing in the calculation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the calculation unit can input data on the user's current activity status and areas of interest into the generation AI and have the generation AI customize the calculation results.

[0048] The calculation unit can improve the calculation algorithm by reflecting user feedback during calculation. The calculation unit improves the calculation algorithm based on, for example, feedback provided by the user. The calculation unit can also preferentially adopt a specific calculation method based on the user's past feedback. Furthermore, the calculation unit can analyze the user's feedback and improve the accuracy of the calculation algorithm. In this way, the accuracy of the calculation algorithm is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the calculation unit can input user feedback data into the generation AI and cause the generation AI to improve the calculation algorithm.

[0049] The calculation unit can provide an appropriate calculation result by taking into account the user's geographical location information during calculation. The calculation unit, for example, calculates the optimal CO2 emissions based on the user's current location. The calculation unit can also calculate the optimal travel time and cost by taking into account the distance from the user's current location. Furthermore, the calculation unit can provide a calculation result of a relevant means of transportation based on the user's current location. In this way, an optimal calculation result can be provided by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input the user's geographical location information into the generation AI and cause the generation AI to provide the optimal calculation result.

[0050] During the calculation, the calculation unit can analyze the user's social media activity and provide related calculation results. For example, the calculation unit can provide calculation results related to places where the user checked in on social media. The calculation unit can also analyze the content of the user's social media posts and provide related calculation results. Furthermore, the calculation unit can provide related calculation results by referring to the activities of the user's friends on social media. In this way, related calculation results can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the calculation unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related calculation results.

[0051] The calculation unit can customize the calculation result by reflecting the user's past feedback during calculation. The calculation unit provides an optimal calculation result based on, for example, feedback provided by the user in the past. The calculation unit can also preferentially adopt a specific calculation method based on the user's past feedback. Furthermore, the calculation unit can analyze the user's past feedback and customize the calculation result. In this way, the calculation result can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the calculation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the calculation result.

[0052] During identification, the identification unit can improve the identification algorithm using the smartphone's sensor or GPS data. The identification unit, for example, uses the smartphone's acceleration sensor to detect the user's movement speed and identify the means of transportation. The identification unit can also acquire the user's location information based on GPS data and identify the means of transportation. Furthermore, the identification unit can use the smartphone's gyro sensor to detect the user's movement direction and identify the means of transportation. This improves the accuracy of the identification algorithm by using the smartphone's sensor or GPS data. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the identification unit can input the smartphone's sensor or GPS data into the generation AI and have the generation AI improve the identification algorithm.

[0053] During the identification process, the identification unit can customize the identification results based on the user's current activity status and areas of interest. For example, when the user is at work, the identification unit can prioritize displaying identification results related to commuting routes. Furthermore, when the user is on vacation, the identification unit can prioritize displaying identification results related to tourist spots. Furthermore, the identification unit can customize related identification results based on the user's areas of interest. This improves user convenience by customizing the identification results based on the user's activity status and areas of interest. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input data on the user's current activity status and areas of interest into the generation AI and have the generation AI customize the identification results.

[0054] The identification unit can improve the identification algorithm by reflecting user feedback during identification. The identification unit improves the identification algorithm based on, for example, feedback provided by the user. The identification unit can also preferentially adopt a specific identification method based on the user's past feedback. Furthermore, the identification unit can analyze the user's feedback and improve the accuracy of the identification algorithm. In this way, the accuracy of the identification algorithm is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input user feedback data into the generation AI and cause the generation AI to improve the identification algorithm.

[0055] The identification unit can provide an appropriate identification result by taking into account the user's geographical location information during identification. The identification unit, for example, identifies the optimal means of transportation based on the user's current location. The identification unit can also provide the optimal identification result by taking into account the distance from the user's current location. Furthermore, the identification unit can also provide the identification result of a related means of transportation based on the user's current location. This allows the optimal identification result to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input the user's geographical location information to the generation AI and cause the generation AI to provide the optimal identification result.

[0056] During identification, the identification unit can analyze the user's social media activity and provide related identification results. For example, the identification unit can provide identification results related to places where the user has checked in on social media. The identification unit can also analyze the content of the user's social media posts and provide related identification results. Furthermore, the identification unit can provide related identification results by referring to the activities of the user's friends on social media. In this way, related identification results can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the identification unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the identification unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related identification results.

[0057] During classification, the classification unit can customize the classification result by reflecting the user's past feedback. The classification unit provides an optimal classification result, for example, based on feedback provided by the user in the past. The classification unit can also preferentially adopt a specific classification method based on the user's past feedback. Furthermore, the classification unit can analyze the user's past feedback and customize the classification result. This allows the classification result to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the classification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the classification unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the classification result.

[0058] When awarding points, the awarding unit can improve the awarding algorithm using point awarding criteria based on CO2 emissions. For example, the awarding unit awards more points when a mode of transportation with low CO2 emissions is selected. The awarding unit can also reduce the number of points awarded when a mode of transportation with high CO2 emissions is selected. Furthermore, the awarding unit can optimally award points based on the point awarding criteria based on CO2 emissions. As a result, the accuracy of the awarding algorithm is improved by using the point awarding criteria based on CO2 emissions. Some or all of the above-mentioned processing in the awarding unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the awarding unit can input the point awarding criteria based on CO2 emissions into the generation AI and cause the generation AI to improve the awarding algorithm.

[0059] The awarding unit can customize point awarding based on the user's current activity status and areas of interest at the time of awarding points. For example, when the user is at work, the awarding unit can prioritize point awarding related to the commute route. Also, when the user is on vacation, the awarding unit can prioritize point awarding related to tourist spots. Furthermore, the awarding unit can customize related point awarding based on the user's areas of interest. This improves user convenience by customizing point awarding based on the user's activity status and areas of interest. Some or all of the above-described processing in the awarding unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the awarding unit can input data on the user's current activity status and areas of interest into the generation AI and cause the generation AI to customize point awarding.

[0060] The awarding unit can improve the point awarding algorithm by reflecting user feedback when awarding points. The awarding unit can improve the point awarding algorithm based on, for example, feedback provided by the user. The awarding unit can also preferentially adopt a specific point awarding method based on the user's past feedback. Furthermore, the awarding unit can analyze the user's feedback and improve the accuracy of the point awarding algorithm. In this way, the accuracy of the point awarding algorithm is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the awarding unit can be performed, for example, using the generation AI or can be performed without using the generation AI. For example, the awarding unit can input user feedback data into the generation AI and cause the generation AI to improve the point awarding algorithm.

[0061] The awarding unit can provide appropriate point awards by taking into account the user's geographical location information when awarding points. The awarding unit, for example, awards optimal points based on the user's current location. The awarding unit can also provide optimal point awards by taking into account the distance from the user's current location. Furthermore, the awarding unit can also provide related point awards based on the user's current location. In this way, optimal point awards can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the awarding unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the awarding unit can input the user's geographical location information into the generation AI and cause the generation AI to provide optimal point awards.

[0062] At the time of awarding points, the awarding unit can analyze the user's social media activity and provide related point awards. For example, the awarding unit can provide point awards related to places where the user has checked in on social media. The awarding unit can also analyze the content of the user's social media posts and provide related point awards. Furthermore, the awarding unit can also provide related point awards by referring to the activities of the user's friends on social media. In this way, related point awards can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the awarding unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the awarding unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related point awards.

[0063] The awarding unit can customize point awarding by reflecting the user's past feedback when awarding points. The awarding unit can provide optimal point awarding based on, for example, feedback provided by the user in the past. The awarding unit can also preferentially adopt a specific point awarding method based on the user's past feedback. Furthermore, the awarding unit can analyze the user's past feedback and customize point awarding. In this way, point awarding can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the awarding unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the awarding unit can input the user's past feedback data into the generation AI and cause the generation AI to customize point awarding.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit can analyze the user's past travel history and suggest the optimal means of transportation. For example, it can prioritize suggestions of means of transportation that the user has frequently used in the past. The reception unit can also predict and suggest the optimal means of transportation for a specific time period based on the user's past travel history. Furthermore, the reception unit can also suggest the optimal route based on the user's past travel history. In this way, the optimal means of transportation and route can be suggested by analyzing the user's past travel history.

[0066] The identification unit can monitor the user's health condition and suggest the optimal means of transportation. For example, it can monitor the user's heart rate and number of steps and suggest a means of transportation that suits the user's health condition. The identification unit can also preferentially suggest means of transportation that involve a high amount of exercise (such as walking or cycling) based on the user's health condition. Furthermore, the identification unit can also suggest the optimal travel route based on the user's health condition. This makes it possible to suggest the optimal means of transportation and route that take the user's health condition into consideration.

[0067] The calculation unit can customize travel time and cost taking into account the user's current activity status. For example, if the user is at work, the calculation unit can prioritize displaying travel time and cost related to a commuter route. Also, if the user is on vacation, the calculation unit can prioritize displaying travel time and cost related to tourist spots. Furthermore, the calculation unit can suggest the optimal means of transportation based on the user's current activity status. This makes it possible to suggest optimal travel time and cost according to the user's activity status.

[0068] The reception unit can customize the destination input interface by reflecting the user's past feedback. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. In this way, the input method can be customized by reflecting the user's past feedback.

[0069] The identification unit can improve the accuracy of identifying transportation means by taking into account the user's current activity status. For example, when the user is at work, the identification unit can prioritize identifying transportation means related to the commute route. Also, when the user is on vacation, the identification unit can prioritize identifying transportation means related to tourist spots. Furthermore, the identification unit can also identify the optimal transportation means based on the user's current activity status. This makes it possible to identify the optimal transportation means according to the user's activity status.

[0070] The reception unit can analyze the user's social media activity and suggest related destinations. For example, it can suggest places where the user has checked in on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidates. Furthermore, the reception unit can also suggest related places by referring to the activities of the user's friends on social media. In this way, it is possible to suggest related destinations by analyzing the user's social media activity.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit accepts the destination information entered by the user. For example, the user opens a map app and enters "from home to work." This information is input into the generation AI. Step 2: The prediction unit uses the generation AI to perform weather predictions, traffic congestion predictions, and other seasonal fluctuations based on the information received by the reception unit. For example, the generation AI analyzes weather data, traffic data, and seasonal data to generate prediction results. Step 3: The calculation unit calculates travel time, cost, and CO2 emissions for each mode of transportation based on the prediction results obtained by the prediction unit. For example, the generation AI calculates travel time, cost, and CO2 emissions for each mode of transportation, such as train, bicycle, and walking. Step 4: The identification unit identifies the means of transportation based on the information calculated by the calculation unit. For example, the generation AI acquires the user's location information and identifies the means of transportation. Step 5: The awarding unit awards points according to the amount of CO2 emissions based on the transportation mode identified by the identification unit. For example, if a transportation mode with low CO2 emissions is selected, more points are awarded.

[0073] (Example 2) A system according to an embodiment of the present invention utilizes a map app integrated with a generation AI to calculate travel time, cost, and CO2 emissions for each mode of transportation, taking into account weather forecasts, traffic congestion forecasts, and other seasonal variations. In the system, a user searches for a destination, and the generation AI calculates travel time, cost, and CO2 emissions for each mode of transportation, taking into account weather forecasts, traffic congestion forecasts, and other seasonal variations. Furthermore, during actual travel, the generation AI identifies the mode of transportation based on location information and determines CO2 emissions. Finally, points are awarded based on CO2 emissions. For example, a user opens a map app and inputs their destination, such as "from home to work." This information is input into the generation AI. Next, the generation AI analyzes the input information and calculates travel time, cost, and CO2 emissions for each mode of transportation, taking into account weather forecasts, traffic congestion forecasts, and other seasonal variations. For example, travel time, cost, and CO2 emissions are displayed for each mode of transportation, such as train, bicycle, and walking. Furthermore, during actual travel, the generation AI acquires the user's location information and identifies the mode of transportation. For example, if a user is riding a train, the generating AI will identify the train and determine the amount of CO2 emissions. Finally, points are awarded according to the amount of CO2 emissions. For example, if a user chooses a mode of transportation with lower CO2 emissions, more points are awarded. This system allows users to earn points while contributing to reducing CO2 emissions. As a result, the system is expected to contribute to reducing CO2 emissions and achieving carbon neutrality. It also has ESG benefits by promoting health and improving corporate value. For example, choosing to walk or cycle promotes health. Furthermore, by companies adopting this system, their corporate value will increase and ESG benefits will be realized.

[0074] A mobility assistance system according to an embodiment includes a reception unit, a prediction unit, a calculation unit, an identification unit, and an assignment unit. The reception unit accepts destination information input by a user. For example, a user opens a map app and enters "from home to work." This information is input to a generation AI. The prediction unit uses the generation AI to perform weather forecasts, traffic congestion forecasts, and other seasonal fluctuations based on the information accepted by the reception unit. For example, the generation AI analyzes weather data, traffic data, and seasonal data to generate a prediction result. The calculation unit calculates travel time, cost, and CO2 emissions for each mode of transportation based on the prediction result obtained by the prediction unit. For example, the generation AI calculates travel time, cost, and CO2 emissions for each mode of transportation, such as train, bicycle, and walking. The identification unit identifies the mode of transportation based on the information calculated by the calculation unit. For example, the generation AI acquires user location information and identifies the mode of transportation. The assignment unit assigns points according to CO2 emissions based on the mode of transportation identified by the identification unit. For example, more points are assigned if a mode of transportation with lower CO2 emissions is selected. As a result, the travel assistance system according to the embodiment is expected to contribute to carbon neutrality by calculating the amount of CO2 emissions during the user's travel and awarding points.

[0075] The identification unit can identify the means of transportation using the smartphone's sensors or GPS data. The identification unit can, for example, use the smartphone's acceleration sensor to detect the user's movement speed and identify the means of transportation. The identification unit can also acquire the user's location information based on GPS data and identify the means of transportation. Furthermore, the identification unit can use the smartphone's gyro sensor to detect the user's movement direction and identify the means of transportation. This improves the accuracy of identifying the means of transportation by using the smartphone's sensors and GPS data. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the identification unit can input the smartphone's sensor or GPS data into the generation AI and have the generation AI identify the means of transportation.

[0076] The reception unit may be equipped with a mechanism for obtaining user consent. For example, the reception unit may display a consent screen and obtain user consent when the user uses the map app for the first time. The reception unit may also display a screen that explains the consent details in detail before the user inputs a destination. Furthermore, the reception unit may also clarify the procedure for the user to withdraw consent. This enhances privacy protection by obtaining user consent. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may cause the generation AI to display the consent screen and explain the consent details.

[0077] The calculation unit can calculate CO2 emissions using a CO2 emission coefficient for each transportation mode. The calculation unit calculates CO2 emissions for each transportation mode, such as train, bicycle, and walking, using the CO2 emission coefficient. The calculation unit can also accurately calculate CO2 emissions for each transportation mode using the latest data. Furthermore, the calculation unit can also suggest the most environmentally friendly transportation mode based on the CO2 emission coefficient. By using the CO2 emission coefficient for each transportation mode, the accuracy of calculating CO2 emissions is improved. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input the CO2 emission coefficient for each transportation mode into the generation AI and have the generation AI calculate the CO2 emissions.

[0078] The awarding unit can set criteria for awarding points according to CO2 emissions. For example, the awarding unit can set criteria for awarding more points when a mode of transportation with low CO2 emissions is selected. The awarding unit can also set criteria for awarding fewer points when a mode of transportation with high CO2 emissions is selected. Furthermore, the awarding unit can award optimal points based on the point awarding criteria according to CO2 emissions. In this way, by setting point awarding criteria according to CO2 emissions, it is possible to increase the user's environmental awareness. Some or all of the above-mentioned processing in the awarding unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the awarding unit can input point awarding criteria according to CO2 emissions into the generation AI and leave the execution of point awarding to the generation AI.

[0079] The reception unit can estimate the user's emotions and change the destination input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive 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 quick destination input. This improves the user experience by customizing the interface according to the user's emotions. 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, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI change the interface.

[0080] The reception unit can analyze the user's past destination input history and suggest an appropriate input method. For example, the reception unit can automatically display destinations that the user has frequently input in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations to be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI suggest the optimal input method.

[0081] When inputting a destination, the reception unit can present input candidates based on the user's current activity status and areas of interest. For example, the reception unit presents locations related to an event the user is currently participating in as candidates. The reception unit can also present related tourist spots and stores as candidates based on the user's areas of interest. Furthermore, the reception unit can also suggest appropriate destinations depending on the user's current activity status (e.g., at work, on vacation, etc.). This improves user convenience by presenting input candidates based on the user's activity status and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the user's current activity status and areas of interest into the generation AI and cause the generation AI to present input candidates.

[0082] When inputting a destination, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the destination using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also identify the destination using image recognition technology. This improves input efficiency by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0083] The reception unit can estimate the user's emotions and prioritize input candidates based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize simple and intuitive input candidates. Furthermore, if the user is relaxed, the reception unit can provide detailed input candidates and suggest customizable options. Furthermore, if the user is in a hurry, the reception unit can prioritize candidates that can be quickly entered. This improves user convenience by prioritizing input candidates according to the user's emotions. The emotion estimation is realized 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, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI prioritize the input candidates.

[0084] When a destination is input, the reception unit can prioritize presenting highly relevant destinations by taking into account the user's geographical location information. For example, the reception unit can prioritize displaying locations close to the user's current location as candidates. The reception unit can also suggest optimal destinations by taking into account the distance from the user's current location. Furthermore, the reception unit can prioritize presenting related tourist spots and stores based on the user's current location. In this way, highly relevant destinations can be prioritized 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, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to present highly relevant destinations.

[0085] When a destination is input, the reception unit can analyze the user's social media activity and suggest related destinations. For example, the reception unit can suggest places where the user has checked in on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidates. Furthermore, the reception unit can also suggest related places by referring to the activities of the user's friends on social media. In this way, related destinations can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest related destinations.

[0086] The reception unit can customize the input method by reflecting the user's past feedback when inputting a destination. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0087] The prediction unit can estimate the user's emotions and change the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the prediction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a display method that focuses on the main points. This improves the user experience by adjusting the display method of the prediction results according to the user's emotions. The emotion estimation is realized 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 can be performed using, for example, the generation AI, or can be performed without 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 change the display method of the prediction results.

[0088] The prediction unit can improve the prediction algorithm by referring to past weather data and traffic data when making predictions. The prediction unit, for example, improves the accuracy of weather predictions based on past weather data. The prediction unit can also improve the accuracy of traffic congestion predictions by referring to past traffic data. Furthermore, the prediction unit can optimize the prediction algorithm based on past seasonal fluctuation data. In this way, the accuracy of the prediction algorithm is improved by referring to past data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input past weather data and traffic data into the generation AI and have the generation AI improve the prediction algorithm.

[0089] The prediction unit can customize the prediction results based on the user's current activity status and areas of interest at the time of prediction. For example, when the user is at work, the prediction unit can prioritize displaying prediction results related to the user's commute route. Furthermore, when the user is on vacation, the prediction unit can prioritize displaying prediction results related to tourist spots. Furthermore, the prediction unit can customize related prediction results based on the user's areas of interest. This improves user convenience by customizing the prediction results based on the user's activity status and areas of interest. Some or all of the above-described processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit can input data on the user's current activity status and areas of interest into the generation AI and have the generation AI customize the prediction results.

[0090] The prediction unit can improve the prediction algorithm by reflecting user feedback during prediction. The prediction unit improves the prediction algorithm based on, for example, feedback provided by the user. The prediction unit can also preferentially adopt a specific prediction method based on the user's past feedback. Furthermore, the prediction unit can analyze the user's feedback and improve the accuracy of the prediction algorithm. In this way, the accuracy of the prediction algorithm is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input user feedback data into the generation AI and cause the generation AI to improve the prediction algorithm.

[0091] The prediction unit can estimate the user's emotions and prioritize prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit can prioritize displaying simple and intuitive prediction results. Furthermore, if the user is relaxed, the prediction unit can provide detailed prediction results and suggest customizable options. Furthermore, if the user is in a hurry, the prediction unit can prioritize displaying prediction results that can be quickly understood. This improves user convenience by prioritizing prediction results according to the user's emotions. 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 can be performed using, for example, the generation AI. For example, the prediction unit can input user emotion data into the generation AI and have the generation AI determine the priority of the prediction results.

[0092] The prediction unit can provide an appropriate prediction result by taking into account the user's geographical location information when making a prediction. The prediction unit, for example, provides an optimal weather forecast based on the user's current location. The prediction unit can also provide an optimal traffic congestion forecast by taking into account the distance from the user's current location. Furthermore, the prediction unit can also provide a related seasonal variation forecast based on the user's current location. This allows for optimal prediction results to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the prediction unit can be performed using, or without, a generation AI. For example, the prediction unit can input the user's geographical location information into the generation AI and cause the generation AI to provide an optimal prediction result.

[0093] At the time of prediction, the prediction unit can analyze the user's social media activity and provide related prediction results. For example, the prediction unit can provide prediction results regarding places where the user has checked in on social media. The prediction unit can also analyze the content of the user's social media posts and provide related prediction results. Furthermore, the prediction unit can provide related prediction results by referring to the activities of the user's friends on social media. In this way, related prediction results can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related prediction results.

[0094] The prediction unit can customize the prediction result by reflecting the user's past feedback when making a prediction. The prediction unit can provide an optimal prediction result based on, for example, feedback provided by the user in the past. The prediction unit can also preferentially adopt a specific prediction method based on the user's past feedback. Furthermore, the prediction unit can analyze the user's past feedback and customize the prediction result. This allows the prediction result to be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the prediction unit can input the user's past feedback data into the generation AI and have the generation AI customize the prediction result.

[0095] The calculation unit can estimate the user's emotion and change the display method of the calculation results based on the estimated user emotion. For example, if the user is nervous, the calculation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the calculation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the calculation unit can provide a display method that focuses on the main points. This improves the user experience by adjusting the display method of the calculation results according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 calculation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the calculation unit can input the user's emotion data into the generation AI and cause the generation AI to change the display method of the calculation results.

[0096] The calculation unit can improve the calculation algorithm using a CO2 emission coefficient for each transportation mode during calculation. The calculation unit optimizes the calculation algorithm using a CO2 emission coefficient for each transportation mode, such as train, bicycle, or walking. The calculation unit can also use the latest data to accurately calculate the CO2 emissions for each transportation mode. Furthermore, the calculation unit can also suggest the most environmentally friendly transportation mode based on the CO2 emission coefficient. In this way, using the CO2 emission coefficient for each transportation mode improves the accuracy of the calculation algorithm. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the calculation unit can input the CO2 emission coefficient for each transportation mode into the generation AI and have the generation AI improve the calculation algorithm.

[0097] The calculation unit can customize the calculation results based on the user's current activity status and areas of interest during calculation. For example, when the user is at work, the calculation unit can prioritize displaying calculation results related to commuting routes. Furthermore, when the user is on vacation, the calculation unit can prioritize displaying calculation results related to tourist spots. Furthermore, the calculation unit can customize related calculation results based on the user's areas of interest. This improves user convenience by customizing the calculation results based on the user's activity status and areas of interest. Some or all of the above-described processing in the calculation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the calculation unit can input data on the user's current activity status and areas of interest into the generation AI and have the generation AI customize the calculation results.

[0098] The calculation unit can improve the calculation algorithm by reflecting user feedback during calculation. The calculation unit improves the calculation algorithm based on, for example, feedback provided by the user. The calculation unit can also preferentially adopt a specific calculation method based on the user's past feedback. Furthermore, the calculation unit can analyze the user's feedback and improve the accuracy of the calculation algorithm. In this way, the accuracy of the calculation algorithm is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the calculation unit can input user feedback data into the generation AI and cause the generation AI to improve the calculation algorithm.

[0099] The calculation unit can estimate the user's emotions and prioritize the calculation results based on the estimated user emotions. For example, if the user is nervous, the calculation unit can prioritize displaying simple and intuitive calculation results. Furthermore, if the user is relaxed, the calculation unit can provide detailed calculation results and suggest customizable options. Furthermore, if the user is in a hurry, the calculation unit can prioritize displaying calculation results that can be quickly understood. This improves user convenience by prioritizing the calculation results according to the user's emotions. The emotion estimation is realized 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 calculation unit can be performed using, for example, the generation AI. For example, the calculation unit can input the user's emotion data into the generation AI and have the generation AI prioritize the calculation results.

[0100] The calculation unit can provide an appropriate calculation result by taking into account the user's geographical location information during calculation. The calculation unit, for example, calculates the optimal CO2 emissions based on the user's current location. The calculation unit can also calculate the optimal travel time and cost by taking into account the distance from the user's current location. Furthermore, the calculation unit can provide a calculation result of a relevant means of transportation based on the user's current location. In this way, an optimal calculation result can be provided by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the calculation unit can input the user's geographical location information into the generation AI and cause the generation AI to provide the optimal calculation result.

[0101] During the calculation, the calculation unit can analyze the user's social media activity and provide related calculation results. For example, the calculation unit can provide calculation results related to places where the user checked in on social media. The calculation unit can also analyze the content of the user's social media posts and provide related calculation results. Furthermore, the calculation unit can provide related calculation results by referring to the activities of the user's friends on social media. In this way, related calculation results can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the calculation unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related calculation results.

[0102] The calculation unit can customize the calculation result by reflecting the user's past feedback during calculation. The calculation unit provides an optimal calculation result based on, for example, feedback provided by the user in the past. The calculation unit can also preferentially adopt a specific calculation method based on the user's past feedback. Furthermore, the calculation unit can analyze the user's past feedback and customize the calculation result. In this way, the calculation result can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the calculation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the calculation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the calculation result.

[0103] The identification unit can estimate the user's emotions and change the transportation identification method based on the estimated user emotions. For example, if the user is nervous, the identification unit can provide a simple and intuitive identification method. Furthermore, if the user is relaxed, the identification unit can provide a detailed identification method and suggest customizable options. Furthermore, if the user is in a hurry, the identification unit can prioritize a method that can be identified quickly. This improves identification accuracy by adjusting the identification method according to the user's emotions. The emotion estimation is realized 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 identification unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the identification unit can input the user's emotion data into the generation AI and cause the generation AI to change the transportation identification method.

[0104] During identification, the identification unit can improve the identification algorithm using the smartphone's sensor or GPS data. The identification unit, for example, uses the smartphone's acceleration sensor to detect the user's movement speed and identify the means of transportation. The identification unit can also acquire the user's location information based on GPS data and identify the means of transportation. Furthermore, the identification unit can use the smartphone's gyro sensor to detect the user's movement direction and identify the means of transportation. This improves the accuracy of the identification algorithm by using the smartphone's sensor or GPS data. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the identification unit can input the smartphone's sensor or GPS data into the generation AI and have the generation AI improve the identification algorithm.

[0105] During the identification process, the identification unit can customize the identification results based on the user's current activity status and areas of interest. For example, when the user is at work, the identification unit can prioritize displaying identification results related to commuting routes. Furthermore, when the user is on vacation, the identification unit can prioritize displaying identification results related to tourist spots. Furthermore, the identification unit can customize related identification results based on the user's areas of interest. This improves user convenience by customizing the identification results based on the user's activity status and areas of interest. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input data on the user's current activity status and areas of interest into the generation AI and have the generation AI customize the identification results.

[0106] The identification unit can improve the identification algorithm by reflecting user feedback during identification. The identification unit improves the identification algorithm based on, for example, feedback provided by the user. The identification unit can also preferentially adopt a specific identification method based on the user's past feedback. Furthermore, the identification unit can analyze the user's feedback and improve the accuracy of the identification algorithm. In this way, the accuracy of the identification algorithm is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input user feedback data into the generation AI and cause the generation AI to improve the identification algorithm.

[0107] The identification unit can estimate the user's emotions and prioritize the identification results based on the estimated user emotions. For example, if the user is nervous, the identification unit can prioritize simple and intuitive identification results. Furthermore, if the user is relaxed, the identification unit can provide detailed identification results and suggest customizable options. Furthermore, if the user is in a hurry, the identification unit can prioritize results that can be quickly identified. This improves user convenience by prioritizing the identification results according to the user's emotions. The 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 identification unit can be performed using, for example, the generation AI. For example, the identification unit can input the user's emotion data into the generation AI and have the generation AI prioritize the identification results.

[0108] The identification unit can provide an appropriate identification result by taking into account the user's geographical location information during identification. The identification unit, for example, identifies the optimal means of transportation based on the user's current location. The identification unit can also provide the optimal identification result by taking into account the distance from the user's current location. Furthermore, the identification unit can also provide the identification result of a related means of transportation based on the user's current location. This allows the optimal identification result to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the identification unit may be performed using, or without, the generation AI. For example, the identification unit can input the user's geographical location information to the generation AI and cause the generation AI to provide the optimal identification result.

[0109] During identification, the identification unit can analyze the user's social media activity and provide related identification results. For example, the identification unit can provide identification results related to places where the user has checked in on social media. The identification unit can also analyze the content of the user's social media posts and provide related identification results. Furthermore, the identification unit can provide related identification results by referring to the activities of the user's friends on social media. In this way, related identification results can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the identification unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the identification unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related identification results.

[0110] During classification, the classification unit can customize the classification result by reflecting the user's past feedback. The classification unit provides an optimal classification result, for example, based on feedback provided by the user in the past. The classification unit can also preferentially adopt a specific classification method based on the user's past feedback. Furthermore, the classification unit can analyze the user's past feedback and customize the classification result. This allows the classification result to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the classification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the classification unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the classification result.

[0111] The awarding unit can estimate the user's emotions and change the point awarding criteria based on the estimated user emotions. For example, if the user is nervous, the awarding unit can provide simple and intuitive point awarding criteria. Furthermore, if the user is relaxed, the awarding unit can provide detailed point awarding criteria and suggest customizable options. Furthermore, if the user is in a hurry, the awarding unit can prioritize quickly understandable point awarding criteria. This improves the user experience by adjusting the point awarding criteria according to the user's emotions. The emotion estimation is realized 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 awarding unit can be performed using, for example, the generation AI. For example, the awarding unit can input the user's emotion data into the generation AI and cause the generation AI to change the point awarding criteria.

[0112] When awarding points, the awarding unit can improve the awarding algorithm using point awarding criteria based on CO2 emissions. For example, the awarding unit awards more points when a mode of transportation with low CO2 emissions is selected. The awarding unit can also reduce the number of points awarded when a mode of transportation with high CO2 emissions is selected. Furthermore, the awarding unit can optimally award points based on the point awarding criteria based on CO2 emissions. As a result, the accuracy of the awarding algorithm is improved by using the point awarding criteria based on CO2 emissions. Some or all of the above-mentioned processing in the awarding unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the awarding unit can input the point awarding criteria based on CO2 emissions into the generation AI and cause the generation AI to improve the awarding algorithm.

[0113] The awarding unit can customize point awarding based on the user's current activity status and areas of interest at the time of awarding points. For example, when the user is at work, the awarding unit can prioritize point awarding related to the commute route. Also, when the user is on vacation, the awarding unit can prioritize point awarding related to tourist spots. Furthermore, the awarding unit can customize related point awarding based on the user's areas of interest. This improves user convenience by customizing point awarding based on the user's activity status and areas of interest. Some or all of the above-described processing in the awarding unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the awarding unit can input data on the user's current activity status and areas of interest into the generation AI and cause the generation AI to customize point awarding.

[0114] The awarding unit can improve the point awarding algorithm by reflecting user feedback when awarding points. The awarding unit can improve the point awarding algorithm based on, for example, feedback provided by the user. The awarding unit can also preferentially adopt a specific point awarding method based on the user's past feedback. Furthermore, the awarding unit can analyze the user's feedback and improve the accuracy of the point awarding algorithm. In this way, the accuracy of the point awarding algorithm is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the awarding unit can be performed, for example, using the generation AI or can be performed without using the generation AI. For example, the awarding unit can input user feedback data into the generation AI and cause the generation AI to improve the point awarding algorithm.

[0115] The assigning unit can estimate the user's emotions and set priority for point awarding based on the estimated user's emotions. For example, if the user is nervous, the assigning unit can prioritize simple and intuitive point awarding. Furthermore, if the user is relaxed, the assigning unit can provide detailed point awarding and suggest customizable options. Furthermore, if the user is in a hurry, the assigning unit can prioritize point awarding that can be quickly understood. This improves user convenience by determining the priority for point awarding according to the user's emotions. The emotion estimation is realized 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 assigning unit can be performed using, for example, the generation AI. For example, the assigning unit can input the user's emotion data into the generation AI and cause the generation AI to set the priority for point awarding.

[0116] The awarding unit can provide appropriate point awards by taking into account the user's geographical location information when awarding points. The awarding unit, for example, awards optimal points based on the user's current location. The awarding unit can also provide optimal point awards by taking into account the distance from the user's current location. Furthermore, the awarding unit can also provide related point awards based on the user's current location. In this way, optimal point awards can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the awarding unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the awarding unit can input the user's geographical location information into the generation AI and cause the generation AI to provide optimal point awards.

[0117] At the time of awarding points, the awarding unit can analyze the user's social media activity and provide related point awards. For example, the awarding unit can provide point awards related to places where the user has checked in on social media. The awarding unit can also analyze the content of the user's social media posts and provide related point awards. Furthermore, the awarding unit can also provide related point awards by referring to the activities of the user's friends on social media. In this way, related point awards can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the awarding unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the awarding unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related point awards.

[0118] The awarding unit can customize point awarding by reflecting the user's past feedback when awarding points. The awarding unit can provide optimal point awarding based on, for example, feedback provided by the user in the past. The awarding unit can also preferentially adopt a specific point awarding method based on the user's past feedback. Furthermore, the awarding unit can analyze the user's past feedback and customize point awarding. In this way, point awarding can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the awarding unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the awarding unit can input the user's past feedback data into the generation AI and cause the generation AI to customize point awarding. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, prediction unit, calculation unit, identification unit, and assignment unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives destination information input by the user. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to perform weather forecasts, traffic congestion forecasts, and other seasonal fluctuations. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and calculates travel time, cost, and CO2 emissions for each mode of transportation. The identification unit is implemented, for example, by the control unit 46A of the smart device 14 and acquires user location information and identifies the mode of transportation. The assignment unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and assigns points according to the CO2 emissions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, prediction unit, calculation unit, identification unit, and assignment 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 microphone 238 of the smart glasses 214 and receives information input by the user regarding the destination. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs weather forecasting, traffic congestion forecasting, and other seasonal fluctuations using a generation AI. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates travel time, cost, and CO2 emissions for each mode of transportation. The identification unit is realized, for example, by the control unit 46A of the smart glasses 214 and acquires user location information and identifies the mode of transportation. The assignment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and assigns points according to the amount of CO2 emissions. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, prediction unit, calculation unit, identification unit, and assignment 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 microphone 238 of the headset-type terminal 314 and receives information about a destination input by a user. 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 perform weather forecasts, traffic congestion forecasts, and other seasonal fluctuations. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates travel time, cost, and CO2 emissions for each mode of transportation. The identification unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and acquires user location information and identifies the mode of transportation. The assignment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and assigns points according to the amount of CO2 emissions. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, prediction unit, calculation unit, identification unit, and assignment 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 microphone 238 of the robot 414 and receives information input by the user regarding the destination. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs weather forecasting, traffic congestion forecasting, and other seasonal fluctuations using a generation AI. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates travel time, cost, and CO2 emissions for each mode of transportation. The identification unit is realized, for example, by the control unit 46A of the robot 414 and acquires user location information and identifies the mode of transportation. The assignment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and assigns points according to the amount of CO2 emissions.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The reception unit can analyze the user's past travel history and suggest the optimal means of transportation. For example, it can prioritize suggestions of means of transportation that the user has frequently used in the past. The reception unit can also predict and suggest the optimal means of transportation for a specific time period based on the user's past travel history. Furthermore, the reception unit can also suggest the optimal route based on the user's past travel history. In this way, the optimal means of transportation and route can be suggested by analyzing the user's past travel history.

[0121] The identification unit can monitor the user's health condition and suggest the optimal means of transportation. For example, it can monitor the user's heart rate and number of steps and suggest a means of transportation that suits the user's health condition. The identification unit can also preferentially suggest means of transportation that involve a high amount of exercise (such as walking or cycling) based on the user's health condition. Furthermore, the identification unit can also suggest the optimal travel route based on the user's health condition. This makes it possible to suggest the optimal means of transportation and route that take the user's health condition into consideration.

[0122] The reception unit can estimate the user's emotions and customize the proposed transportation means based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can suggest a relaxing transportation means (such as walking or cycling). If the user is relaxed, the reception unit can also suggest an efficient transportation means (such as train or bus). Furthermore, if the user is in a hurry, the reception unit can also suggest the fastest transportation means. This makes it possible to suggest the optimal transportation means according to the user's emotions.

[0123] The calculation unit can customize travel time and cost taking into account the user's current activity status. For example, if the user is at work, the calculation unit can prioritize displaying travel time and cost related to a commuter route. Also, if the user is on vacation, the calculation unit can prioritize displaying travel time and cost related to tourist spots. Furthermore, the calculation unit can suggest the optimal means of transportation based on the user's current activity status. This makes it possible to suggest optimal travel time and cost according to the user's activity status.

[0124] The awarding unit can estimate the user's emotions and adjust the timing of point awarding based on the estimated user emotions. For example, if the user is feeling stressed, points can be awarded immediately after the user moves, thereby increasing the user's satisfaction. Furthermore, if the user is relaxed, the awarding unit can provide detailed point award information after the user moves. Furthermore, if the user is in a hurry, the awarding unit can provide a concise point award notification. This makes it possible to provide the optimal timing of point awarding according to the user's emotions.

[0125] The reception unit can customize the destination input interface by reflecting the user's past feedback. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. In this way, the input method can be customized by reflecting the user's past feedback.

[0126] The prediction unit can estimate the user's emotions and adjust the timing of displaying the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction results can be displayed quickly to reduce the user's anxiety. The prediction unit can also provide detailed prediction results when the user is relaxed. Furthermore, if the user is in a hurry, the prediction unit can quickly display prediction results that focus on the main points. This makes it possible to provide the optimal timing of displaying the prediction results according to the user's emotions.

[0127] The identification unit can improve the accuracy of identifying transportation means by taking into account the user's current activity status. For example, when the user is at work, the identification unit can prioritize identifying transportation means related to the commute route. Also, when the user is on vacation, the identification unit can prioritize identifying transportation means related to tourist spots. Furthermore, the identification unit can also identify the optimal transportation means based on the user's current activity status. This makes it possible to identify the optimal transportation means according to the user's activity status.

[0128] The awarding unit can estimate the user's emotions and customize the content of point awarding based on the estimated user's emotions. For example, if the user is feeling stressed, the awarding unit can provide a benefit that will help the user relax (e.g., a free cafe coupon). The awarding unit can also provide detailed point awarding information if the user is relaxed. Furthermore, the awarding unit can provide a concise point awarding notification if the user is in a hurry. This makes it possible to provide optimal point awarding content according to the user's emotions.

[0129] The reception unit can analyze the user's social media activity and suggest related destinations. For example, it can suggest places where the user has checked in on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidates. Furthermore, the reception unit can also suggest related places by referring to the activities of the user's friends on social media. In this way, it is possible to suggest related destinations by analyzing the user's social media activity.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The reception unit accepts the destination information entered by the user. For example, the user opens a map app and enters "from home to work." This information is input into the generation AI. Step 2: The prediction unit uses the generation AI to perform weather predictions, traffic congestion predictions, and other seasonal fluctuations based on the information received by the reception unit. For example, the generation AI analyzes weather data, traffic data, and seasonal data to generate prediction results. Step 3: The calculation unit calculates travel time, cost, and CO2 emissions for each mode of transportation based on the prediction results obtained by the prediction unit. For example, the generation AI calculates travel time, cost, and CO2 emissions for each mode of transportation, such as train, bicycle, and walking. Step 4: The identification unit identifies the means of transportation based on the information calculated by the calculation unit. For example, the generation AI acquires the user's location information and identifies the means of transportation. Step 5: The awarding unit awards points according to the amount of CO2 emissions based on the transportation mode identified by the identification unit. For example, if a transportation mode with low CO2 emissions is selected, more points are awarded.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Explanation of symbols]

[0204] 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 an input of a destination; a prediction unit that performs weather prediction, traffic congestion prediction, and other seasonal fluctuation prediction based on the information received by the reception unit; a calculation unit that calculates travel time, cost, and CO2 emissions for each means of transportation based on the prediction results obtained by the prediction unit; an identification unit that identifies a transportation means based on the information calculated by the calculation unit; and an awarding unit that awards points according to the amount of CO2 emissions based on the means of transportation identified by the identification unit. A system characterized by:

2. The identification unit Identifying transportation modes using smartphone sensors or GPS data 2. The system of claim 1.

3. The reception unit Have a mechanism for obtaining user consent 2. The system of claim 1.

4. The calculation unit Calculate CO2 emissions using the CO2 emission coefficient for each mode of transportation 2. The system of claim 1.

5. The granting unit Set criteria for awarding points according to CO2 emissions 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and change the destination input interface based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyzes the user's past destination input history and suggests the most appropriate input method 2. The system of claim 1.

8. The reception unit When entering a destination, suggestions are provided based on the user's current activities and areas of interest.

2. The system of claim 1.

9. The reception unit When entering a destination, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

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