system

Abstract

The system according to the embodiment aims to propose the most suitable means of transportation and route in consideration of the user's travel history and preferences. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a learning unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes transportation methods and routes based on the analysis results obtained by the analysis unit. The learning unit learns the user's travel history and preferences.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately take into account the user's travel history and preferences when proposing transportation methods and routes, so there is room for improvement in terms of realizing comfortable travel.

[0005] The system according to the embodiment aims to propose the most suitable means of transportation and route in consideration of the user's travel history and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a learning unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests transportation means and routes based on the analysis results obtained by the analysis unit. The learning unit learns the user's travel history and preferences. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable means of transportation and route in consideration of the user's travel history and preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The transportation suggestion system according to an embodiment of the present invention is a flat-rate service that suggests comfortable travel and transportation options for users by avoiding congestion and traffic jams for each mode of transportation, such as airplanes, trains, and cars. This transportation suggestion system aims to minimize the stress, economic losses, and environmental damage caused by congestion and traffic jams. The user fee is a flat rate of 110 yen per month (tax included), with the first three usages free (minimum usage period is one year). In addition, multiple sources of income are expected, such as discount coupons and advertising fees from various companies, and subsidies and grants from national and local governments for mitigating traffic jams. For example, a user selects a transportation mode and inputs a destination. Next, the transportation suggestion system uses AI to collect and analyze congestion and traffic information for each mode of transportation in real time. For example, it collects information on flight reservations, train operation information, and road congestion. Based on this information, it suggests optimal transportation modes and routes for users. Furthermore, the transportation suggestion system learns the user's travel history and preferences and makes optimal suggestions for each individual user. For example, it provides an optimal travel plan for each user, taking into account previously used transportation modes, routes, travel times, and other factors. The transportation suggestion system also provides discount coupons from various companies, which, when used by users, enhances the advertising effectiveness of the companies. For example, by providing discount coupons from specific airlines, railway companies, rental car companies, etc., and using them, the advertising effectiveness of the companies can be enhanced. Furthermore, the transportation suggestion system can receive subsidies and grants from national and local governments for alleviating traffic congestion. For example, alleviating road congestion reduces the environmental impact and can receive subsidies and grants from national and local governments. In this way, the present invention is a flat-rate service that avoids congestion and congestion for each transportation method and suggests comfortable travel and transportation for people, and is characterized by having multiple sources of income. As a result, the transportation suggestion system can avoid congestion and congestion and suggest comfortable travel and transportation for people.

[0029] A transportation suggestion system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a learning unit. The collection unit collects data. Examples of the data include, but are not limited to, flight reservation status, train operation information, and road congestion information. For example, the collection unit collects flight reservation status using an airline's API. The collection unit can also collect train operation information using a railway company's API. The collection unit can also collect road congestion information using a traffic management system. For example, the collection unit obtains real-time reservation status through an airline's API to understand flight congestion. The railway company's API is used to collect train operation schedules and delay information. The road congestion status is analyzed based on sensor data acquired from the traffic management system. The analysis unit analyzes the data collected by the collection unit. For example, the analysis is performed using, but is not limited to, statistical analysis or a machine learning algorithm. For example, the analysis unit analyzes trends in flight reservation status using statistical analysis. The analysis unit can also predict train operation patterns using a machine learning algorithm. The analysis unit can also predict road congestion based on data acquired from the traffic management system. For example, the analysis unit can analyze past reservation data using statistical analysis to identify peak congestion times. It can use a machine learning algorithm to predict the probability of delays from train operation data. It can build a road congestion prediction model based on traffic management system data to predict future congestion conditions. The suggestion unit can propose a means of transportation and a route based on the analysis results obtained by the analysis unit. The suggestion can be made, for example, taking into account the shortest distance, the shortest time, traffic conditions, etc., but is not limited to these examples. For example, the suggestion unit can propose a means of transportation and a route based on the shortest distance. The suggestion unit can also propose a means of transportation and a route based on the shortest time. The suggestion unit can also propose a means of transportation and a route taking traffic conditions into consideration. For example, the suggestion unit can propose a means of transportation and a route based on the shortest distance, providing the user with the most efficient travel plan. It can propose a means of transportation and a route based on the shortest time, minimizing the user's travel time. It can propose a route that avoids congestion by taking traffic conditions into consideration, thereby reducing the user's travel stress.The learning unit learns the user's travel history and preferences. Learning is performed, for example, using a machine learning algorithm, but is not limited to this example. For example, the learning unit learns preferences based on the user's past travel history. The learning unit can also learn the user's preferences based on survey results. The learning unit can also learn preferences based on the user's selection history. For example, the learning unit analyzes the user's past travel history to identify frequently used means of transportation and routes. The learning unit understands the user's travel preferences and requests based on survey results. The learning unit learns the means of transportation preferred for specific time periods and days of the week based on the user's selection history. As a result, the transportation suggestion system according to the embodiment can provide the user with the optimal means of transportation and routes through data collection, analysis, suggestion, and learning.

[0030] The suggestion unit can suggest a means of transportation and a route to the user. The suggestion unit, for example, suggests the optimal means of transportation and a route to the user. For example, the suggestion unit suggests the optimal means of transportation and a route based on the user's current location and destination. The suggestion unit can also suggest the optimal means of transportation and a route taking into account the user's travel history and preferences. For example, the suggestion unit can suggest frequently used means of transportation and a route based on the user's past travel history. The suggestion unit can also suggest a preferred means of transportation for a specific time period or day of the week taking into account the user's preferences. In this way, the suggestion unit can improve the efficiency and comfort of travel by suggesting the optimal means of transportation and a route to the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the user's current location and destination and outputs the optimal means of transportation and a route.

[0031] The learning unit can learn the user's travel history and preferences. The learning unit, for example, learns the user's travel history. For example, the learning unit learns frequently used means of transportation and routes based on the user's past travel history. The learning unit can also learn the user's preferences based on survey results. For example, the learning unit analyzes survey results to understand the user's travel preferences and needs. The learning unit can also learn preferences based on the user's selection history. For example, the learning unit learns the means of transportation preferred during specific time periods or days of the week. This enables the learning unit to learn the user's travel history and preferences, thereby enabling more personalized suggestions. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can perform learning using an AI model that inputs the user's travel history and preferences and outputs learning results.

[0032] The collection unit can collect flight reservation status, train operation information, and road congestion information. The collection unit, for example, collects flight reservation status. For example, the collection unit collects flight reservation status using an airline's API. The collection unit can also use a railway company's API to collect train operation information. For example, the collection unit collects train operation schedules and delay information using the railway company's API. The collection unit can also use a traffic management system to collect road congestion information. For example, the collection unit analyzes road congestion status based on sensor data acquired from the traffic management system. As a result, the collection unit can provide real-time travel information by collecting flight reservation status, train operation information, and road congestion information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input reservation status data acquired through an airline's API into a generation AI and have the generation AI analyze the data.

[0033] The transportation suggestion system includes a providing unit that provides discount coupons from each company. The providing unit provides, for example, discount coupons from a specific airline, railway company, rental car company, etc. For example, the providing unit provides discount coupons from a specific airline, and when a user uses the coupons, the providing unit enhances the advertising effectiveness of the company. The providing unit can also provide discount coupons from a specific railway company, and when a user uses the coupons, the providing unit can enhance the advertising effectiveness of the company. For example, the providing unit provides discount coupons from a specific airline, and when a user uses the coupons, the providing unit enhances the advertising effectiveness of the airline. The providing unit provides discount coupons from a specific railway company, and when a user uses the coupons, the providing unit enhances the advertising effectiveness of the railway company. The providing unit provides discount coupons from a specific rental car company, and when a user uses the coupons, the providing unit enhances the advertising effectiveness of the rental car company. In this way, by providing discount coupons from each company, the providing unit can enhance convenience for users and the advertising effectiveness of the company. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input coupon data provided by companies into a generation AI and cause the generation AI to determine the optimal timing to provide the coupons.

[0034] The transportation suggestion system includes a receiving unit that receives subsidies and grants from national and local governments for alleviating congestion. The receiving unit, for example, reduces environmental impact by alleviating road congestion and receives subsidies and grants from national and local governments. For example, the receiving unit evaluates the effect of alleviating congestion and applies for subsidies and grants based on the results. The receiving unit can also evaluate the effect of reducing environmental impact and apply for subsidies and grants based on the results. For example, the receiving unit evaluates the effect of alleviating road congestion and receives subsidies and grants from national and local governments based on the results. The receiving unit evaluates the effect of reducing environmental impact and receives subsidies and grants from national and local governments based on the results. In this way, the receiving unit can receive financial support by receiving subsidies and grants for alleviating congestion. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit may input evaluation data of the effect of alleviating congestion into a generating AI and have the generating AI create application documents for the subsidies and grants.

[0035] The collection unit can analyze past data collection history and select a data collection method. The collection unit, for example, analyzes past data collection history. For example, the collection unit selects the most efficient data collection method based on the past data collection history. The collection unit can also select an optimal data collection method for a specific time period based on the past data collection history. For example, the collection unit analyzes past data collection history to improve the accuracy of data collection. Next, the collection unit selects an optimal data collection method based on the analysis results. For example, the collection unit selects the most efficient data collection method from the past data collection history. The collection unit can also select an optimal data collection method for a specific time period. For example, the collection unit selects an optimal data collection method for a specific time period based on the past data collection history. In this way, the collection unit can improve the efficiency and accuracy of data collection by analyzing the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data collection history to a generation AI and cause the generation AI to select an optimal data collection method.

[0036] The collection unit can perform filtering based on the user's current movement status and areas of interest when collecting data. The collection unit, for example, filters data taking into account the user's current movement status. For example, when the user is moving, the collection unit prioritizes collecting data related to transportation means. The collection unit can also filter data based on the user's areas of interest. For example, the collection unit filters and collects highly relevant data based on the user's areas of interest. Next, the collection unit filters data based on the user's current movement status and areas of interest. For example, the collection unit collects optimal data taking into account the user's current movement status. The collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, when the user is moving, the collection unit prioritizes collecting data related to transportation means. The collection unit filters and collects highly relevant data based on the user's areas of interest. In this way, the collection unit can collect more relevant data by filtering data based on the user's current movement status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current travel status and areas of interest into the generation AI and have the generation AI perform data filtering.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, collects data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also prioritize collecting data from locations close to the user's current location. For example, the collection unit collects optimal data based on the user's geographical location information. Next, the collection unit collects data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also prioritize collecting data from locations close to the user's current location. For example, the collection unit collects optimal data based on the user's geographical location information. In this way, the collection unit can collect more relevant data by collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without using AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit, for example, analyzes the user's social media activities. For example, the collection unit analyzes the user's social media posts and collects related data. The collection unit can also collect optimal data based on the user's social media interests. For example, the collection unit analyzes the user's social media activities in real time and collects related data. Next, the collection unit analyzes the user's social media activities and collects related data. For example, the collection unit analyzes the user's social media posts and collects related data. The collection unit can also collect optimal data based on the user's social media interests. For example, the collection unit analyzes the user's social media activities in real time and collects related data. In this way, the collection unit can collect more relevant data by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to a generation AI and cause the generation AI to collect related data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, evaluates the importance of the data. For example, the analysis unit evaluates the importance based on the impact score of the data. The analysis unit can also evaluate the importance based on the relevance score of the data. For example, the analysis unit performs a detailed analysis on data with high importance based on the impact score of the data. The analysis unit can also perform a simplified analysis on data with low importance based on the relevance score of the data. Next, the analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a detailed analysis on data with high importance based on the impact score of the data. The analysis unit performs a simplified analysis on data with low importance based on the relevance score of the data. In this way, the analysis unit can perform an efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis department can input the data's impact score and relevance score into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit, for example, classifies data categories. For example, the analysis unit classifies data into categories such as text data, numerical data, and image data. The analysis unit can also apply an optimal analysis algorithm depending on the data category. For example, the analysis unit applies a specific analysis algorithm to text data. The analysis unit can also apply a different analysis algorithm to numerical data. The analysis unit can also apply an optimal analysis algorithm to image data. For example, the analysis unit applies a natural language processing algorithm to text data and a statistical analysis algorithm to numerical data. The analysis unit applies an image recognition algorithm to image data. In this way, the analysis unit can improve the accuracy of the analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of the analysis based on the time when the data was collected. The analysis unit, for example, evaluates the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit determines the priority of the analysis based on the time when the data was collected. Next, the analysis unit determines the priority of the analysis based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit determines the priority of the analysis based on the time when the data was collected. In this way, the analysis unit can perform an analysis that prioritizes the most recent data by determining the priority of the analysis based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected to the generation AI and cause the generation AI to determine the priority of the analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, evaluates the relevance of the data. For example, the analysis unit evaluates the relevance based on the co-occurrence frequency of the data. The analysis unit can also evaluate the relevance based on the correlation of the data. For example, the analysis unit prioritizes analyzing highly relevant data based on the co-occurrence frequency of the data. The analysis unit can also postpone analyzing less relevant data based on the correlation of the data. Next, the analysis unit adjusts the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analyzing highly relevant data. The analysis unit can also postpone analyzing less relevant data. For example, the analysis unit prioritizes analyzing highly relevant data based on the co-occurrence frequency of the data. Postpone less relevant data based on the correlation of the data. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the co-occurrence frequency and correlation of data into the generation AI and have the generation AI adjust the order of analysis.

[0043] The suggestion unit can adjust the level of detail of the proposal based on the importance of the means of transportation when making a proposal. The suggestion unit, for example, evaluates the importance of the means of transportation. For example, the suggestion unit evaluates the importance based on the time efficiency of the means of transportation. The suggestion unit can also evaluate the importance based on the cost efficiency of the means of transportation. For example, the suggestion unit makes a detailed proposal for a means of transportation with high importance based on the time efficiency of the means of transportation. The suggestion unit can also make a simplified proposal for a means of transportation with low importance based on the cost efficiency of the means of transportation. Next, the suggestion unit adjusts the level of detail of the proposal based on the importance of the means of transportation. For example, the suggestion unit makes a detailed proposal for a means of transportation with high importance. The suggestion unit can also make a simplified proposal for a means of transportation with low importance. For example, the suggestion unit makes a detailed proposal for a means of transportation with high importance based on the time efficiency of the means of transportation. The suggestion unit makes a simplified proposal for a means of transportation with low importance based on the cost efficiency of the means of transportation. In this way, the suggestion unit can make an efficient proposal by adjusting the level of detail of the proposal based on the importance of the means of transportation. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the time efficiency and cost efficiency of transportation means to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0044] The suggestion unit can apply different proposal algorithms depending on the category of the transportation means when making a proposal. The suggestion unit, for example, classifies the categories of transportation means. For example, the suggestion unit classifies the categories into public transportation means, personal transportation means, etc. The suggestion unit can also apply an optimal proposal algorithm depending on the category of the transportation means. For example, the suggestion unit applies a specific proposal algorithm to public transportation means. The suggestion unit can also apply a different proposal algorithm to personal transportation means. Next, the suggestion unit applies different proposal algorithms depending on the category of the transportation means. For example, the suggestion unit applies a specific proposal algorithm to public transportation means. The suggestion unit can also apply a different proposal algorithm to personal transportation means. For example, the suggestion unit applies a specific proposal algorithm to public transportation means and a different proposal algorithm to personal transportation means. In this way, the suggestion unit can improve the accuracy of the proposal by applying an optimal proposal algorithm depending on the category of the transportation means. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input a category of transportation means into the generation AI and cause the generation AI to apply the optimal suggestion algorithm.

[0045] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time when the means of transportation will be used. The suggestion unit, for example, evaluates the time when the means of transportation will be used. For example, the suggestion unit determines the priority based on peak hours for the means of transportation. The suggestion unit can also determine the priority based on off-peak hours for the means of transportation. For example, the suggestion unit determines the priority of the proposal based on the time when the means of transportation will be used. Next, the suggestion unit determines the priority of the proposal based on the time when the means of transportation will be used. For example, the suggestion unit prioritizes the most recent means of transportation. The suggestion unit can also postpone means of transportation in the distant future. For example, the suggestion unit determines the priority of the proposal based on the time when the means of transportation will be used. In this way, the suggestion unit can make efficient proposals by determining the priority of the proposal based on the time when the means of transportation will be used. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time when the means of transportation will be used to a generation AI and cause the generation AI to determine the priority of the proposals.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the means of transportation when making suggestions. The suggestion unit, for example, evaluates the relevance of the means of transportation. For example, the suggestion unit evaluates the relevance based on the frequency of use of the means of transportation. The suggestion unit can also evaluate the relevance based on the past usage history of the means of transportation. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the means of transportation. Next, the suggestion unit adjusts the order of suggestions based on the relevance of the means of transportation. For example, the suggestion unit prioritizes suggesting means of transportation with high relevance. The suggestion unit can also postpone suggesting means of transportation with low relevance. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the means of transportation. In this way, the suggestion unit can make efficient suggestions by adjusting the order of suggestions based on the relevance of the means of transportation. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the frequency of use and past usage history of the means of transportation into the generation AI and cause the generation AI to adjust the order of suggestions.

[0047] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, refers to past learning data. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also optimize the learning algorithm based on the past learning data. For example, the learning unit analyzes the past learning data to improve the accuracy of the learning algorithm. Next, the learning unit optimizes the learning algorithm by referring to the past learning data. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also optimize the learning algorithm based on the past learning data. For example, the learning unit analyzes the past learning data to improve the accuracy of the learning algorithm. In this way, the learning unit can improve the accuracy of the learning algorithm by referring to the past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to optimize the learning algorithm.

[0048] During learning, the learning unit can improve the accuracy of the learning based on the user's movement history. The learning unit, for example, refers to the user's movement history. For example, the learning unit optimizes the learning algorithm based on the user's movement history. The learning unit can also improve the accuracy of the learning based on the user's movement history. For example, the learning unit analyzes the user's movement history and improves the accuracy of the learning algorithm. Next, the learning unit improves the accuracy of the learning based on the user's movement history. For example, the learning unit optimizes the learning algorithm based on the user's movement history. The learning unit can also improve the accuracy of the learning based on the user's movement history. For example, the learning unit analyzes the user's movement history and improves the accuracy of the learning algorithm. In this way, the learning unit can improve the accuracy of the learning based on the user's movement history and make more appropriate suggestions. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's movement history to the generation AI and cause the generation AI to optimize the learning algorithm.

[0049] During learning, the learning unit can weight the learning data based on the time when the travel history was submitted. The learning unit, for example, evaluates the time when the travel history was submitted. For example, the learning unit may assign a higher weight to the most recent travel history. The learning unit may also assign a lower weight to older travel histories. For example, the learning unit weights the learning data based on the time when the travel history was submitted. Next, the learning unit weights the learning data based on the time when the travel history was submitted. For example, the learning unit may assign a higher weight to the most recent travel history. The learning unit may also assign a lower weight to older travel histories. For example, the learning unit weights the learning data based on the time when the travel history was submitted. In this way, the learning unit can perform more appropriate learning by weighting the learning data based on the time when the travel history was submitted. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input the time when the travel history was submitted to the generation AI and cause the generation AI to weight the learning data.

[0050] During learning, the learning unit can select learning data based on the relevance of the user's movement history. The learning unit, for example, evaluates the relevance of the user's movement history. For example, the learning unit selects learning data based on the relevance of the movement history. The learning unit can also select learning data based on the relevance of the movement history. For example, the learning unit selects learning data based on the relevance of the movement history. Next, the learning unit selects learning data based on the relevance of the user's movement history. For example, the learning unit prioritizes learning of highly relevant movement history. The learning unit can also postpone learning of less relevant movement history. For example, the learning unit selects learning data based on the relevance of the movement history. In this way, the learning unit can perform more appropriate learning by selecting learning data based on the relevance of the user's movement history. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input the relevance of the user's movement history to a generation AI and cause the generation AI to select learning data.

[0051] The providing unit can select an optimal coupon by referring to the user's past coupon usage history when providing a coupon. The providing unit, for example, refers to the user's past coupon usage history. For example, the providing unit selects an optimal coupon based on coupons used by the user in the past. The providing unit can also provide an optimal coupon based on the user's past coupon usage history. For example, the providing unit analyzes the user's past coupon usage history and selects the most effective coupon. Next, the providing unit selects an optimal coupon by referring to the user's past coupon usage history when providing a coupon. For example, the providing unit selects an optimal coupon based on coupons used by the user in the past. The providing unit can also provide an optimal coupon based on the user's past coupon usage history. For example, the providing unit analyzes the user's past coupon usage history and selects the most effective coupon. In this way, the providing unit can provide more effective coupons by referring to the user's past coupon usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past coupon usage history into the generation AI and have the generation AI select the most suitable coupon.

[0052] The providing unit can provide the optimal coupon by taking into consideration the user's current location information when providing a coupon. The providing unit, for example, provides the coupon by taking into consideration the user's current location information. For example, if the user is in a specific area, the providing unit provides a coupon related to that area. The providing unit can also provide coupons for locations close to the user's current location. For example, the providing unit provides the optimal coupon based on the user's current location information. Next, the providing unit provides the optimal coupon by taking into consideration the user's current location information when providing a coupon. For example, if the user is in a specific area, the providing unit provides a coupon related to that area. The providing unit can also provide coupons for locations close to the user's current location. For example, the providing unit provides the optimal coupon based on the user's current location information. This allows the providing unit to provide the optimal coupon by taking into consideration the user's current location information, thereby enabling more effective coupon provision. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the user's current location information to a generation AI and cause the generation AI to provide the optimal coupon.

[0053] The providing unit can provide the optimal coupon by taking into consideration the geographical location information of the user when providing a coupon. The providing unit, for example, provides the coupon by taking into consideration the geographical location information of the user. For example, if the user is in a specific area, the providing unit provides a coupon related to that area. The providing unit can also provide coupons for locations close to the user's current location. For example, the providing unit provides the optimal coupon based on the geographical location information of the user. Next, the providing unit provides the optimal coupon by taking into consideration the geographical location information of the user when providing a coupon. For example, if the user is in a specific area, the providing unit provides a coupon related to that area. The providing unit can also provide coupons for locations close to the user's current location. For example, the providing unit provides the optimal coupon based on the geographical location information of the user. This allows the providing unit to provide the optimal coupon by taking into consideration the geographical location information of the user, thereby providing more effective coupon provision. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the user's geographical location information to a generation AI and cause the generation AI to provide the optimal coupon.

[0054] The providing unit can analyze the user's social media activity and provide a relevant coupon when providing a coupon. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit analyzes the user's social media posts and provides a relevant coupon. The providing unit can also provide an optimal coupon based on the user's social media interests. For example, the providing unit analyzes the user's social media activity in real time and provides a relevant coupon. Next, the providing unit analyzes the user's social media activity and provides a relevant coupon when providing a coupon. For example, the providing unit analyzes the user's social media posts and provides a relevant coupon. The providing unit can also provide an optimal coupon based on the user's social media interests. For example, the providing unit analyzes the user's social media activity in real time and provides a relevant coupon. In this way, the providing unit can provide more effective coupons by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide a relevant coupon.

[0055] The receiving unit can select the optimal receiving method by referring to past receiving history when receiving a subsidy or grant. The receiving unit, for example, refers to past receiving history. For example, the receiving unit selects the optimal receiving method based on receiving methods used by the user in the past. The receiving unit can also provide the optimal receiving method based on the user's past receiving history. For example, the receiving unit analyzes the user's past receiving history and selects the most effective receiving method. Next, the receiving unit selects the optimal receiving method by referring to past receiving history when receiving a subsidy or grant. For example, the receiving unit selects the optimal receiving method based on receiving methods used by the user in the past. The receiving unit can also provide the optimal receiving method based on the user's past receiving history. For example, the receiving unit analyzes the user's past receiving history and selects the most effective receiving method. In this way, the receiving unit can select a more appropriate receiving method by referring to the past receiving history. Some or all of the above-described processing in the receiving unit may be performed, for example, using AI or without AI. For example, the receiving unit can input past receiving history into the generation AI and have the generation AI select the optimal receiving method.

[0056] The receiving unit can select the optimal receiving method by taking into account the user's current location information when receiving a subsidy or grant. The receiving unit, for example, selects the receiving method by taking into account the user's current location information. For example, if the user is in a specific area, the receiving unit provides a receiving method related to that area. The receiving unit can also provide a receiving method close to the user's current location. For example, the receiving unit provides the optimal receiving method based on the user's current location information. Next, the receiving unit selects the optimal receiving method by taking into account the user's current location information when receiving a subsidy or grant. For example, if the user is in a specific area, the receiving unit provides a receiving method related to that area. The receiving unit can also provide a receiving method close to the user's current location. For example, the receiving unit provides the optimal receiving method based on the user's current location information. In this way, the receiving unit can provide a more appropriate receiving method by selecting the optimal receiving method by taking into account the user's current location information. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input the user's current location information into the generation AI and have the generation AI select the optimal receiving method.

[0057] The receiving unit can select the optimal receiving method by taking into account the user's geographical location information when receiving a subsidy or grant. The receiving unit, for example, selects the receiving method by taking into account the user's geographical location information. For example, if the user is in a specific area, the receiving unit provides a receiving method related to that area. The receiving unit can also provide a receiving method close to the user's current location. For example, the receiving unit provides the optimal receiving method based on the user's geographical location information. Next, the receiving unit selects the optimal receiving method by taking into account the user's geographical location information when receiving a subsidy or grant. For example, if the user is in a specific area, the receiving unit provides a receiving method related to that area. The receiving unit can also provide a receiving method close to the user's current location. For example, the receiving unit provides the optimal receiving method based on the user's geographical location information. In this way, the receiving unit can provide a more appropriate receiving method by selecting the optimal receiving method by taking into account the user's geographical location information. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal receiving method.

[0058] The receiving unit can analyze the user's social media activity and provide a relevant receipt method when receiving a grant or subsidy. The receiving unit, for example, analyzes the user's social media activity. For example, the receiving unit analyzes the user's social media posts and provides a relevant receipt method. The receiving unit can also provide an optimal receipt method based on the user's social media interests. For example, the receiving unit analyzes the user's social media activity in real time and provides a relevant receipt method. Next, the receiving unit analyzes the user's social media activity and provides a relevant receipt method when receiving a grant or subsidy. For example, the receiving unit analyzes the user's social media posts and provides a relevant receipt method. The receiving unit can also provide an optimal receipt method based on the user's social media interests. For example, the receiving unit analyzes the user's social media activity in real time and provides a relevant receipt method. In this way, the receiving unit can provide a more appropriate receipt method by analyzing the user's social media activity. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input the user's social media activity data into the generating AI and cause the generating AI to provide the relevant receiving method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The suggestion unit can suggest a means of transportation and a route taking into consideration the user's hobbies and interests. For example, if the user is interested in historical places, the suggestion unit can suggest a route that goes around historical landmarks. If the user likes to enjoy nature, the suggestion unit can also suggest a route that includes natural parks and scenic spots. Furthermore, if the user is interested in gourmet food, the suggestion unit can also suggest a route that includes restaurants where you can enjoy local specialties. In this way, the suggestion unit can provide a more fulfilling travel experience by suggesting a means of transportation and a route based on the user's hobbies and interests.

[0061] The learning unit can learn the user's behavioral patterns while traveling. For example, if the user often listens to music while traveling, the learning unit can learn the user's favorite music genre and provide an environment in which the user can comfortably enjoy music on the transportation mode suggested by the transportation mode suggestion system. If the user often reads while traveling, the learning unit can learn the user's favorite book genre and provide an environment suitable for reading on the transportation mode suggested by the transportation mode suggestion system. Furthermore, if the user prefers to relax while traveling, the learning unit can learn an environment in which the user can relax and provide an environment in which the user can relax on the transportation mode suggested by the transportation mode suggestion system. In this way, the learning unit can learn the user's behavioral patterns while traveling, thereby enabling more personalized suggestions.

[0062] The collection unit can collect feedback on the user's means of transportation and improve the accuracy of suggestions. For example, after the user uses a proposed means of transportation, the collection unit can collect satisfaction and dissatisfaction with that means of transportation as feedback. Also, after the user uses a proposed route, the collection unit can collect an evaluation of the route as feedback. Next, the collection unit improves the accuracy of suggestions based on the collected feedback. For example, if the user expresses high satisfaction with a particular means of transportation, the collection unit can preferentially suggest that means of transportation. Also, if the user expresses dissatisfaction with a particular route, the collection unit can make a suggestion to avoid that route. In this way, the collection unit can improve the accuracy of suggestions by collecting user feedback.

[0063] The transportation suggestion system may further include a safety monitoring unit to ensure the safety of the user during travel. The safety monitoring unit, for example, monitors the user's location information in real time while the user is traveling and detects abnormal behavior or entry into a dangerous area. The safety monitoring unit then notifies the user of the detected abnormality or danger and encourages the user to take appropriate action. For example, if the user enters a dangerous area, the safety monitoring unit issues a warning to the user and suggests changing to a safer route. Furthermore, if the user exhibits abnormal behavior, the safety monitoring unit may notify an emergency contact and encourage a prompt response. In this way, the transportation suggestion system ensures the user's safety during travel, allowing the user to enjoy travel with peace of mind.

[0064] The collection unit collects environmental data during the user's travel, thereby improving the accuracy of suggestions. For example, the collection unit collects environmental data such as temperature, humidity, and noise levels during the user's travel. The collection unit can also collect traffic conditions and weather information during the user's travel. Next, the collection unit improves the accuracy of suggestions based on the collected environmental data. For example, the collection unit proposes the optimal means of transportation and route taking into account the temperature and humidity ranges within which the user can travel comfortably. It can also prioritize the proposal of routes with low noise levels. In this way, the collection unit can provide a more comfortable travel experience by collecting environmental data during the user's travel.

[0065] The collection unit collects energy consumption data during the user's travel, and can improve the accuracy of suggestions. For example, the collection unit collects energy consumption data such as the user's calorie consumption, number of steps, and travel distance during travel. The collection unit can also collect the user's exercise intensity and heart rate during travel. Next, the collection unit improves the accuracy of suggestions based on the collected energy consumption data. For example, if the user desires healthy travel, a means of travel that consumes a large number of calories can be suggested. Also, if the user desires a relaxed travel, a means of travel that consumes a low amount of exercise can be suggested. In this way, the collection unit can provide a healthier and more comfortable travel experience by collecting energy consumption data during the user's travel.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data. The data includes, for example, flight reservation status, train operation information, and road congestion information. The collection unit collects flight reservation status using an airline's API, collects train operation information using a railway company's API, and collects road congestion information using a traffic management system. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, it analyzes trends in airline reservations, predicts train operation patterns, and predicts road congestion. Step 3: The proposal unit proposes transportation methods and routes based on the analysis results obtained by the analysis unit. The proposal is made taking into consideration the shortest distance, shortest time, traffic conditions, etc. For example, the proposal will be based on the shortest distance, the shortest time, or the traffic conditions, and will propose a route that avoids congestion. Step 4: The learning unit learns the user's movement history and preferences. Learning is performed using a machine learning algorithm. For example, preferences are learned based on the user's past movement history, based on survey results, and based on the user's selection history.

[0068] (Example 2) The transportation suggestion system according to an embodiment of the present invention is a flat-rate service that suggests comfortable travel and transportation options for users by avoiding congestion and traffic jams for each mode of transportation, such as airplanes, trains, and cars. This transportation suggestion system aims to minimize the stress, economic losses, and environmental damage caused by congestion and traffic jams. The user fee is a flat rate of 110 yen per month (tax included), with the first three usages free (minimum usage period is one year). In addition, multiple sources of income are expected, such as discount coupons and advertising fees from various companies, and subsidies and grants from national and local governments for mitigating traffic jams. For example, a user selects a transportation mode and inputs a destination. Next, the transportation suggestion system uses AI to collect and analyze congestion and traffic information for each mode of transportation in real time. For example, it collects information on flight reservations, train operation information, and road congestion. Based on this information, it suggests optimal transportation modes and routes for users. Furthermore, the transportation suggestion system learns the user's travel history and preferences and makes optimal suggestions for each individual user. For example, it provides an optimal travel plan for each user, taking into account previously used transportation modes, routes, travel times, and other factors. The transportation suggestion system also provides discount coupons from various companies, which, when used by users, enhances the advertising effectiveness of the companies. For example, by providing discount coupons from specific airlines, railway companies, rental car companies, etc., and using them, the advertising effectiveness of the companies can be enhanced. Furthermore, the transportation suggestion system can receive subsidies and grants from national and local governments for alleviating traffic congestion. For example, alleviating road congestion reduces the environmental impact and can receive subsidies and grants from national and local governments. In this way, the present invention is a flat-rate service that avoids congestion and congestion for each transportation method and suggests comfortable travel and transportation for people, and is characterized by having multiple sources of income. As a result, the transportation suggestion system can avoid congestion and congestion and suggest comfortable travel and transportation for people.

[0069] A transportation suggestion system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a learning unit. The collection unit collects data. Examples of the data include, but are not limited to, flight reservation status, train operation information, and road congestion information. For example, the collection unit collects flight reservation status using an airline's API. The collection unit can also collect train operation information using a railway company's API. The collection unit can also collect road congestion information using a traffic management system. For example, the collection unit obtains real-time reservation status through an airline's API to understand flight congestion. The railway company's API is used to collect train operation schedules and delay information. The road congestion status is analyzed based on sensor data acquired from the traffic management system. The analysis unit analyzes the data collected by the collection unit. For example, the analysis is performed using, but is not limited to, statistical analysis or a machine learning algorithm. For example, the analysis unit analyzes trends in flight reservation status using statistical analysis. The analysis unit can also predict train operation patterns using a machine learning algorithm. The analysis unit can also predict road congestion based on data acquired from the traffic management system. For example, the analysis unit can analyze past reservation data using statistical analysis to identify peak congestion times. It can use a machine learning algorithm to predict the probability of delays from train operation data. It can build a road congestion prediction model based on traffic management system data to predict future congestion conditions. The suggestion unit can propose a means of transportation and a route based on the analysis results obtained by the analysis unit. The suggestion can be made, for example, taking into account the shortest distance, the shortest time, traffic conditions, etc., but is not limited to these examples. For example, the suggestion unit can propose a means of transportation and a route based on the shortest distance. The suggestion unit can also propose a means of transportation and a route based on the shortest time. The suggestion unit can also propose a means of transportation and a route taking traffic conditions into consideration. For example, the suggestion unit can propose a means of transportation and a route based on the shortest distance, providing the user with the most efficient travel plan. It can propose a means of transportation and a route based on the shortest time, minimizing the user's travel time. It can propose a route that avoids congestion by taking traffic conditions into consideration, thereby reducing the user's travel stress.The learning unit learns the user's travel history and preferences. Learning is performed, for example, using a machine learning algorithm, but is not limited to this example. For example, the learning unit learns preferences based on the user's past travel history. The learning unit can also learn the user's preferences based on survey results. The learning unit can also learn preferences based on the user's selection history. For example, the learning unit analyzes the user's past travel history to identify frequently used means of transportation and routes. The learning unit understands the user's travel preferences and requests based on survey results. The learning unit learns the means of transportation preferred for specific time periods and days of the week based on the user's selection history. As a result, the transportation suggestion system according to the embodiment can provide the user with the optimal means of transportation and routes through data collection, analysis, suggestion, and learning.

[0070] The suggestion unit can suggest a means of transportation and a route to the user. The suggestion unit, for example, suggests the optimal means of transportation and a route to the user. For example, the suggestion unit suggests the optimal means of transportation and a route based on the user's current location and destination. The suggestion unit can also suggest the optimal means of transportation and a route taking into account the user's travel history and preferences. For example, the suggestion unit can suggest frequently used means of transportation and a route based on the user's past travel history. The suggestion unit can also suggest a preferred means of transportation for a specific time period or day of the week taking into account the user's preferences. In this way, the suggestion unit can improve the efficiency and comfort of travel by suggesting the optimal means of transportation and a route to the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the user's current location and destination and outputs the optimal means of transportation and a route.

[0071] The learning unit can learn the user's travel history and preferences. The learning unit, for example, learns the user's travel history. For example, the learning unit learns frequently used means of transportation and routes based on the user's past travel history. The learning unit can also learn the user's preferences based on survey results. For example, the learning unit analyzes survey results to understand the user's travel preferences and needs. The learning unit can also learn preferences based on the user's selection history. For example, the learning unit learns the means of transportation preferred during specific time periods or days of the week. This enables the learning unit to learn the user's travel history and preferences, thereby enabling more personalized suggestions. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can perform learning using an AI model that inputs the user's travel history and preferences and outputs learning results.

[0072] The collection unit can collect flight reservation status, train operation information, and road congestion information. The collection unit, for example, collects flight reservation status. For example, the collection unit collects flight reservation status using an airline's API. The collection unit can also use a railway company's API to collect train operation information. For example, the collection unit collects train operation schedules and delay information using the railway company's API. The collection unit can also use a traffic management system to collect road congestion information. For example, the collection unit analyzes road congestion status based on sensor data acquired from the traffic management system. As a result, the collection unit can provide real-time travel information by collecting flight reservation status, train operation information, and road congestion information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input reservation status data acquired through an airline's API into a generation AI and have the generation AI analyze the data.

[0073] The transportation suggestion system includes a providing unit that provides discount coupons from each company. The providing unit provides, for example, discount coupons from a specific airline, railway company, rental car company, etc. For example, the providing unit provides discount coupons from a specific airline, and when a user uses the coupons, the providing unit enhances the advertising effectiveness of the company. The providing unit can also provide discount coupons from a specific railway company, and when a user uses the coupons, the providing unit can enhance the advertising effectiveness of the company. For example, the providing unit provides discount coupons from a specific airline, and when a user uses the coupons, the providing unit enhances the advertising effectiveness of the airline. The providing unit provides discount coupons from a specific railway company, and when a user uses the coupons, the providing unit enhances the advertising effectiveness of the railway company. The providing unit provides discount coupons from a specific rental car company, and when a user uses the coupons, the providing unit enhances the advertising effectiveness of the rental car company. In this way, by providing discount coupons from each company, the providing unit can enhance convenience for users and the advertising effectiveness of the company. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input coupon data provided by companies into a generation AI and cause the generation AI to determine the optimal timing to provide the coupons.

[0074] The transportation suggestion system includes a receiving unit that receives subsidies and grants from national and local governments for alleviating congestion. The receiving unit, for example, reduces environmental impact by alleviating road congestion and receives subsidies and grants from national and local governments. For example, the receiving unit evaluates the effect of alleviating congestion and applies for subsidies and grants based on the results. The receiving unit can also evaluate the effect of reducing environmental impact and apply for subsidies and grants based on the results. For example, the receiving unit evaluates the effect of alleviating road congestion and receives subsidies and grants from national and local governments based on the results. The receiving unit evaluates the effect of reducing environmental impact and receives subsidies and grants from national and local governments based on the results. In this way, the receiving unit can receive financial support by receiving subsidies and grants for alleviating congestion. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit may input evaluation data of the effect of alleviating congestion into a generating AI and have the generating AI create application documents for the subsidies and grants.

[0075] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. Voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotions. The collection unit then adjusts the timing of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can collect detailed data and make more accurate suggestions. For example, if the user is in a hurry, the collection unit quickly collects necessary data and makes immediate suggestions. In this way, the collection unit adjusts the timing of data collection based on the user's emotions, reducing the user's burden and enabling more appropriate data collection. Emotion estimation is realized using an emotion estimation function, for example, using 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0076] The collection unit can analyze past data collection history and select a data collection method. The collection unit, for example, analyzes past data collection history. For example, the collection unit selects the most efficient data collection method based on the past data collection history. The collection unit can also select an optimal data collection method for a specific time period based on the past data collection history. For example, the collection unit analyzes past data collection history to improve the accuracy of data collection. Next, the collection unit selects an optimal data collection method based on the analysis results. For example, the collection unit selects the most efficient data collection method from the past data collection history. The collection unit can also select an optimal data collection method for a specific time period. For example, the collection unit selects an optimal data collection method for a specific time period based on the past data collection history. In this way, the collection unit can improve the efficiency and accuracy of data collection by analyzing the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data collection history to a generation AI and cause the generation AI to select an optimal data collection method.

[0077] The collection unit can perform filtering based on the user's current movement status and areas of interest when collecting data. The collection unit, for example, filters data taking into account the user's current movement status. For example, when the user is moving, the collection unit prioritizes collecting data related to transportation means. The collection unit can also filter data based on the user's areas of interest. For example, the collection unit filters and collects highly relevant data based on the user's areas of interest. Next, the collection unit filters data based on the user's current movement status and areas of interest. For example, the collection unit collects optimal data taking into account the user's current movement status. The collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, when the user is moving, the collection unit prioritizes collecting data related to transportation means. The collection unit filters and collects highly relevant data based on the user's areas of interest. In this way, the collection unit can collect more relevant data by filtering data based on the user's current movement status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current travel status and areas of interest into the generation AI and have the generation AI perform data filtering.

[0078] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Voice analysis technology can be used to analyze the tone and speed of the user's voice to estimate the emotions. Next, the collection unit prioritizes the data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting more important data. Also, if the user is relaxed, the collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the collection unit prioritizes collecting data that can be collected quickly. In this way, the collection unit can prioritize collecting more important data by prioritizing the data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0079] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, collects data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also prioritize collecting data from locations close to the user's current location. For example, the collection unit collects optimal data based on the user's geographical location information. Next, the collection unit collects data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also prioritize collecting data from locations close to the user's current location. For example, the collection unit collects optimal data based on the user's geographical location information. In this way, the collection unit can collect more relevant data by collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without using AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0080] The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit, for example, analyzes the user's social media activities. For example, the collection unit analyzes the user's social media posts and collects related data. The collection unit can also collect optimal data based on the user's social media interests. For example, the collection unit analyzes the user's social media activities in real time and collects related data. Next, the collection unit analyzes the user's social media activities and collects related data. For example, the collection unit analyzes the user's social media posts and collects related data. The collection unit can also collect optimal data based on the user's social media interests. For example, the collection unit analyzes the user's social media activities in real time and collects related data. In this way, the collection unit can collect more relevant data by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to a generation AI and cause the generation AI to collect related data.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Voice analysis technology can be used to analyze the tone and speed of the user's voice and estimate the emotions. The analysis unit then adjusts the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Alternatively, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows the analysis unit to provide more appropriate analysis results by adjusting the presentation method of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, evaluates the importance of the data. For example, the analysis unit evaluates the importance based on the impact score of the data. The analysis unit can also evaluate the importance based on the relevance score of the data. For example, the analysis unit performs a detailed analysis on data with high importance based on the impact score of the data. The analysis unit can also perform a simplified analysis on data with low importance based on the relevance score of the data. Next, the analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a detailed analysis on data with high importance based on the impact score of the data. The analysis unit performs a simplified analysis on data with low importance based on the relevance score of the data. In this way, the analysis unit can perform an efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis department can input the data's impact score and relevance score into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit, for example, classifies data categories. For example, the analysis unit classifies data into categories such as text data, numerical data, and image data. The analysis unit can also apply an optimal analysis algorithm depending on the data category. For example, the analysis unit applies a specific analysis algorithm to text data. The analysis unit can also apply a different analysis algorithm to numerical data. The analysis unit can also apply an optimal analysis algorithm to image data. For example, the analysis unit applies a natural language processing algorithm to text data and a statistical analysis algorithm to numerical data. The analysis unit applies an image recognition algorithm to image data. In this way, the analysis unit can improve the accuracy of the analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Voice analysis technology can be used to analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit then adjusts the length of the analysis based on the estimated user's emotions. For example, the analysis unit can provide a short and concise analysis result if the user is in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can provide a visually stimulating analysis result if the user is excited. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0085] During analysis, the analysis unit can determine the priority of the analysis based on the time when the data was collected. The analysis unit, for example, evaluates the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit determines the priority of the analysis based on the time when the data was collected. Next, the analysis unit determines the priority of the analysis based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit determines the priority of the analysis based on the time when the data was collected. In this way, the analysis unit can perform an analysis that prioritizes the most recent data by determining the priority of the analysis based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected to the generation AI and cause the generation AI to determine the priority of the analysis.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, evaluates the relevance of the data. For example, the analysis unit evaluates the relevance based on the co-occurrence frequency of the data. The analysis unit can also evaluate the relevance based on the correlation of the data. For example, the analysis unit prioritizes analyzing highly relevant data based on the co-occurrence frequency of the data. The analysis unit can also postpone analyzing less relevant data based on the correlation of the data. Next, the analysis unit adjusts the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analyzing highly relevant data. The analysis unit can also postpone analyzing less relevant data. For example, the analysis unit prioritizes analyzing highly relevant data based on the co-occurrence frequency of the data. Postpone less relevant data based on the correlation of the data. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the co-occurrence frequency and correlation of data into the generation AI and have the generation AI adjust the order of analysis.

[0087] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit can estimate the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The suggestion unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. Next, the suggestion unit adjusts the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is nervous, the suggestion unit makes a simple, highly visible suggestion. Also, if the user is relaxed, the suggestion unit can make a detailed suggestion. For example, if the user is in a hurry, the suggestion unit makes a suggestion that focuses on the main points. This allows the suggestion unit to make more appropriate suggestions by adjusting the way the suggestion is expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative 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 suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0088] The suggestion unit can adjust the level of detail of the proposal based on the importance of the means of transportation when making a proposal. The suggestion unit, for example, evaluates the importance of the means of transportation. For example, the suggestion unit evaluates the importance based on the time efficiency of the means of transportation. The suggestion unit can also evaluate the importance based on the cost efficiency of the means of transportation. For example, the suggestion unit makes a detailed proposal for a means of transportation with high importance based on the time efficiency of the means of transportation. The suggestion unit can also make a simplified proposal for a means of transportation with low importance based on the cost efficiency of the means of transportation. Next, the suggestion unit adjusts the level of detail of the proposal based on the importance of the means of transportation. For example, the suggestion unit makes a detailed proposal for a means of transportation with high importance. The suggestion unit can also make a simplified proposal for a means of transportation with low importance. For example, the suggestion unit makes a detailed proposal for a means of transportation with high importance based on the time efficiency of the means of transportation. The suggestion unit makes a simplified proposal for a means of transportation with low importance based on the cost efficiency of the means of transportation. In this way, the suggestion unit can make an efficient proposal by adjusting the level of detail of the proposal based on the importance of the means of transportation. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the time efficiency and cost efficiency of transportation means to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0089] The suggestion unit can apply different proposal algorithms depending on the category of the transportation means when making a proposal. The suggestion unit, for example, classifies the categories of transportation means. For example, the suggestion unit classifies the categories into public transportation means, personal transportation means, etc. The suggestion unit can also apply an optimal proposal algorithm depending on the category of the transportation means. For example, the suggestion unit applies a specific proposal algorithm to public transportation means. The suggestion unit can also apply a different proposal algorithm to personal transportation means. Next, the suggestion unit applies different proposal algorithms depending on the category of the transportation means. For example, the suggestion unit applies a specific proposal algorithm to public transportation means. The suggestion unit can also apply a different proposal algorithm to personal transportation means. For example, the suggestion unit applies a specific proposal algorithm to public transportation means and a different proposal algorithm to personal transportation means. In this way, the suggestion unit can improve the accuracy of the proposal by applying an optimal proposal algorithm depending on the category of the transportation means. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input a category of transportation means into the generation AI and cause the generation AI to apply the optimal suggestion algorithm.

[0090] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit can estimate the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The suggestion unit can also use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. Next, the suggestion unit adjusts the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can make a short and to-the-point suggestion. Alternatively, if the user is relaxed, the suggestion unit can make a detailed suggestion. For example, if the user is excited, the suggestion unit can make a visually stimulating suggestion. This allows the suggestion unit to make more appropriate suggestions by adjusting the length of the suggestion based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative 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 suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0091] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time when the means of transportation will be used. The suggestion unit, for example, evaluates the time when the means of transportation will be used. For example, the suggestion unit determines the priority based on peak hours for the means of transportation. The suggestion unit can also determine the priority based on off-peak hours for the means of transportation. For example, the suggestion unit determines the priority of the proposal based on the time when the means of transportation will be used. Next, the suggestion unit determines the priority of the proposal based on the time when the means of transportation will be used. For example, the suggestion unit prioritizes the most recent means of transportation. The suggestion unit can also postpone means of transportation in the distant future. For example, the suggestion unit determines the priority of the proposal based on the time when the means of transportation will be used. In this way, the suggestion unit can make efficient proposals by determining the priority of the proposal based on the time when the means of transportation will be used. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the time when the means of transportation will be used to a generation AI and cause the generation AI to determine the priority of the proposals.

[0092] The suggestion unit can adjust the order of suggestions based on the relevance of the means of transportation when making suggestions. The suggestion unit, for example, evaluates the relevance of the means of transportation. For example, the suggestion unit evaluates the relevance based on the frequency of use of the means of transportation. The suggestion unit can also evaluate the relevance based on the past usage history of the means of transportation. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the means of transportation. Next, the suggestion unit adjusts the order of suggestions based on the relevance of the means of transportation. For example, the suggestion unit prioritizes suggesting means of transportation with high relevance. The suggestion unit can also postpone suggesting means of transportation with low relevance. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the means of transportation. In this way, the suggestion unit can make efficient suggestions by adjusting the order of suggestions based on the relevance of the means of transportation. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the frequency of use and past usage history of the means of transportation into the generation AI and cause the generation AI to adjust the order of suggestions.

[0093] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit, for example, estimates the user's emotions. For example, the learning unit estimates the user's emotions using facial expression recognition technology. The learning unit can also estimate the user's emotions using voice analysis technology. For example, the learning unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. Voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotions. The learning unit then selects training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit selects detailed training data. Also, if the user is in a hurry, the learning unit can select data that can be learned quickly. For example, if the user is excited, the learning unit selects visually stimulating data. This allows the learning unit to perform more appropriate learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 learning unit may be performed using AI, or may be performed without using AI. For example, the learning unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0094] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, refers to past learning data. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also optimize the learning algorithm based on the past learning data. For example, the learning unit analyzes the past learning data to improve the accuracy of the learning algorithm. Next, the learning unit optimizes the learning algorithm by referring to the past learning data. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also optimize the learning algorithm based on the past learning data. For example, the learning unit analyzes the past learning data to improve the accuracy of the learning algorithm. In this way, the learning unit can improve the accuracy of the learning algorithm by referring to the past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to optimize the learning algorithm.

[0095] During learning, the learning unit can improve the accuracy of the learning based on the user's movement history. The learning unit, for example, refers to the user's movement history. For example, the learning unit optimizes the learning algorithm based on the user's movement history. The learning unit can also improve the accuracy of the learning based on the user's movement history. For example, the learning unit analyzes the user's movement history and improves the accuracy of the learning algorithm. Next, the learning unit improves the accuracy of the learning based on the user's movement history. For example, the learning unit optimizes the learning algorithm based on the user's movement history. The learning unit can also improve the accuracy of the learning based on the user's movement history. For example, the learning unit analyzes the user's movement history and improves the accuracy of the learning algorithm. In this way, the learning unit can improve the accuracy of the learning based on the user's movement history and make more appropriate suggestions. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's movement history to the generation AI and cause the generation AI to optimize the learning algorithm.

[0096] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit, for example, estimates the user's emotions. For example, the learning unit estimates the user's emotions using facial expression recognition technology. The learning unit can also estimate the user's emotions using voice analysis technology. For example, the learning unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. Voice analysis technology is used to analyze the tone and speed of the user's voice and estimate the emotions. The learning unit then adjusts the frequency of learning based on the estimated user emotions. For example, the learning unit increases the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is in a hurry. For example, the learning unit adjusts the frequency of learning when the user is excited. This allows the learning unit to perform more appropriate learning by adjusting the frequency of learning based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 learning unit may be performed using AI, or may be performed without using AI. For example, the learning unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0097] During learning, the learning unit can weight the learning data based on the time when the travel history was submitted. The learning unit, for example, evaluates the time when the travel history was submitted. For example, the learning unit may assign a higher weight to the most recent travel history. The learning unit may also assign a lower weight to older travel histories. For example, the learning unit weights the learning data based on the time when the travel history was submitted. Next, the learning unit weights the learning data based on the time when the travel history was submitted. For example, the learning unit may assign a higher weight to the most recent travel history. The learning unit may also assign a lower weight to older travel histories. For example, the learning unit weights the learning data based on the time when the travel history was submitted. In this way, the learning unit can perform more appropriate learning by weighting the learning data based on the time when the travel history was submitted. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input the time when the travel history was submitted to the generation AI and cause the generation AI to weight the learning data.

[0098] During learning, the learning unit can select learning data based on the relevance of the user's movement history. The learning unit, for example, evaluates the relevance of the user's movement history. For example, the learning unit selects learning data based on the relevance of the movement history. The learning unit can also select learning data based on the relevance of the movement history. For example, the learning unit selects learning data based on the relevance of the movement history. Next, the learning unit selects learning data based on the relevance of the user's movement history. For example, the learning unit prioritizes learning of highly relevant movement history. The learning unit can also postpone learning of less relevant movement history. For example, the learning unit selects learning data based on the relevance of the movement history. In this way, the learning unit can perform more appropriate learning by selecting learning data based on the relevance of the user's movement history. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input the relevance of the user's movement history to a generation AI and cause the generation AI to select learning data.

[0099] The providing unit can estimate the user's emotion and adjust the timing of providing a coupon based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can estimate the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. Voice analysis technology can be used to analyze the tone and speed of the user's voice and estimate the emotion. Next, the providing unit adjusts the timing of providing the coupon based on the estimated user's emotion. For example, if the user is feeling stressed, the providing unit can provide a coupon at a time when the user can relax. The providing unit can also provide a coupon immediately if the user is relaxed. For example, if the user is in a hurry, the providing unit can provide a coupon quickly. This allows the providing unit to adjust the timing of providing the coupon based on the user's emotion, thereby providing more effective coupons. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative 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 providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0100] The providing unit can select an optimal coupon by referring to the user's past coupon usage history when providing a coupon. The providing unit, for example, refers to the user's past coupon usage history. For example, the providing unit selects an optimal coupon based on coupons used by the user in the past. The providing unit can also provide an optimal coupon based on the user's past coupon usage history. For example, the providing unit analyzes the user's past coupon usage history and selects the most effective coupon. Next, the providing unit selects an optimal coupon by referring to the user's past coupon usage history when providing a coupon. For example, the providing unit selects an optimal coupon based on coupons used by the user in the past. The providing unit can also provide an optimal coupon based on the user's past coupon usage history. For example, the providing unit analyzes the user's past coupon usage history and selects the most effective coupon. In this way, the providing unit can provide more effective coupons by referring to the user's past coupon usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past coupon usage history into the generation AI and have the generation AI select the most suitable coupon.

[0101] The providing unit can provide the optimal coupon by taking into consideration the user's current location information when providing a coupon. The providing unit, for example, provides the coupon by taking into consideration the user's current location information. For example, if the user is in a specific area, the providing unit provides a coupon related to that area. The providing unit can also provide coupons for locations close to the user's current location. For example, the providing unit provides the optimal coupon based on the user's current location information. Next, the providing unit provides the optimal coupon by taking into consideration the user's current location information when providing a coupon. For example, if the user is in a specific area, the providing unit provides a coupon related to that area. The providing unit can also provide coupons for locations close to the user's current location. For example, the providing unit provides the optimal coupon based on the user's current location information. This allows the providing unit to provide the optimal coupon by taking into consideration the user's current location information, thereby enabling more effective coupon provision. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the user's current location information to a generation AI and cause the generation AI to provide the optimal coupon.

[0102] The providing unit can estimate the user's emotions and determine the priority of coupon provision based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. Voice analysis technology can be used to analyze the tone and speed of the user's voice and estimate the emotions. Next, the providing unit determines the priority of coupon provision based on the estimated user's emotions. For example, if the user is stressed, the providing unit can prioritize providing coupons with high importance. Also, if the user is relaxed, the providing unit can prioritize providing detailed coupons. For example, if the user is in a hurry, the providing unit can prioritize providing coupons that can be provided quickly. This allows the providing unit to prioritize coupon provision based on the user's emotions, thereby providing more effective coupons. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0103] The providing unit can provide the optimal coupon by taking into consideration the geographical location information of the user when providing a coupon. The providing unit, for example, provides the coupon by taking into consideration the geographical location information of the user. For example, if the user is in a specific area, the providing unit provides a coupon related to that area. The providing unit can also provide coupons for locations close to the user's current location. For example, the providing unit provides the optimal coupon based on the geographical location information of the user. Next, the providing unit provides the optimal coupon by taking into consideration the geographical location information of the user when providing a coupon. For example, if the user is in a specific area, the providing unit provides a coupon related to that area. The providing unit can also provide coupons for locations close to the user's current location. For example, the providing unit provides the optimal coupon based on the geographical location information of the user. This allows the providing unit to provide the optimal coupon by taking into consideration the geographical location information of the user, thereby providing more effective coupon provision. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the user's geographical location information to a generation AI and cause the generation AI to provide the optimal coupon.

[0104] The providing unit can analyze the user's social media activity and provide a relevant coupon when providing a coupon. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit analyzes the user's social media posts and provides a relevant coupon. The providing unit can also provide an optimal coupon based on the user's social media interests. For example, the providing unit analyzes the user's social media activity in real time and provides a relevant coupon. Next, the providing unit analyzes the user's social media activity and provides a relevant coupon when providing a coupon. For example, the providing unit analyzes the user's social media posts and provides a relevant coupon. The providing unit can also provide an optimal coupon based on the user's social media interests. For example, the providing unit analyzes the user's social media activity in real time and provides a relevant coupon. In this way, the providing unit can provide more effective coupons by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide a relevant coupon.

[0105] The receiving unit can estimate the user's emotions and adjust the method of receiving the subsidy or grant based on the estimated user's emotions. The receiving unit, for example, estimates the user's emotions. For example, the receiving unit can estimate the user's emotions using facial expression recognition technology. The receiving unit can also estimate the user's emotions using voice analysis technology. For example, the receiving unit can capture the user's facial expressions with a camera and estimate the emotion using a facial expression recognition algorithm. Voice analysis technology can be used to analyze the tone and speed of the user's voice to estimate the emotion. The receiving unit then adjusts the method of receiving the subsidy or grant based on the estimated user's emotions. For example, the receiving unit can provide a simple method of receiving the subsidy or grant if the user is stressed. The receiving unit can also provide a detailed method of receiving the subsidy or grant if the user is relaxed. For example, the receiving unit can provide a method for quickly receiving the subsidy or grant if the user is in a hurry. This allows the receiving unit to adjust the method of receiving the subsidy or grant based on the user's emotions, thereby enabling more appropriate receipt. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 receiving unit may be performed using AI, or may be performed without using AI. For example, the receiving unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0106] The receiving unit can select the optimal receiving method by referring to past receiving history when receiving a subsidy or grant. The receiving unit, for example, refers to past receiving history. For example, the receiving unit selects the optimal receiving method based on receiving methods used by the user in the past. The receiving unit can also provide the optimal receiving method based on the user's past receiving history. For example, the receiving unit analyzes the user's past receiving history and selects the most effective receiving method. Next, the receiving unit selects the optimal receiving method by referring to past receiving history when receiving a subsidy or grant. For example, the receiving unit selects the optimal receiving method based on receiving methods used by the user in the past. The receiving unit can also provide the optimal receiving method based on the user's past receiving history. For example, the receiving unit analyzes the user's past receiving history and selects the most effective receiving method. In this way, the receiving unit can select a more appropriate receiving method by referring to the past receiving history. Some or all of the above-described processing in the receiving unit may be performed, for example, using AI or without AI. For example, the receiving unit can input past receiving history into the generation AI and have the generation AI select the optimal receiving method.

[0107] The receiving unit can select the optimal receiving method by taking into account the user's current location information when receiving a subsidy or grant. The receiving unit, for example, selects the receiving method by taking into account the user's current location information. For example, if the user is in a specific area, the receiving unit provides a receiving method related to that area. The receiving unit can also provide a receiving method close to the user's current location. For example, the receiving unit provides the optimal receiving method based on the user's current location information. Next, the receiving unit selects the optimal receiving method by taking into account the user's current location information when receiving a subsidy or grant. For example, if the user is in a specific area, the receiving unit provides a receiving method related to that area. The receiving unit can also provide a receiving method close to the user's current location. For example, the receiving unit provides the optimal receiving method based on the user's current location information. In this way, the receiving unit can provide a more appropriate receiving method by selecting the optimal receiving method by taking into account the user's current location information. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input the user's current location information into the generation AI and have the generation AI select the optimal receiving method.

[0108] The receiving unit can estimate the user's emotions and determine the priority order for receiving subsidies and grants based on the estimated user's emotions. The receiving unit, for example, estimates the user's emotions. For example, the receiving unit estimates the user's emotions using facial expression recognition technology. The receiving unit can also estimate the user's emotions using voice analysis technology. For example, the receiving unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression recognition algorithm. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotion. Next, the receiving unit determines the priority order for receiving subsidies and grants based on the estimated user's emotions. For example, if the user is feeling stressed, the receiving unit can prioritize receiving subsidies and grants that are more important. Also, if the user is relaxed, the receiving unit can prioritize receiving detailed subsidies and grants. For example, if the user is in a hurry, the receiving unit can prioritize receiving subsidies and grants that can be received quickly. In this way, the receiving unit can more appropriately receive subsidies and grants by determining the priority order for receiving subsidies and grants based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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 receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the receiving unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0109] The receiving unit can select the optimal receiving method by taking into account the user's geographical location information when receiving a subsidy or grant. The receiving unit, for example, selects the receiving method by taking into account the user's geographical location information. For example, if the user is in a specific area, the receiving unit provides a receiving method related to that area. The receiving unit can also provide a receiving method close to the user's current location. For example, the receiving unit provides the optimal receiving method based on the user's geographical location information. Next, the receiving unit selects the optimal receiving method by taking into account the user's geographical location information when receiving a subsidy or grant. For example, if the user is in a specific area, the receiving unit provides a receiving method related to that area. The receiving unit can also provide a receiving method close to the user's current location. For example, the receiving unit provides the optimal receiving method based on the user's geographical location information. In this way, the receiving unit can provide a more appropriate receiving method by selecting the optimal receiving method by taking into account the user's geographical location information. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal receiving method.

[0110] The receiving unit can analyze the user's social media activity and provide a relevant receipt method when receiving a grant or subsidy. The receiving unit, for example, analyzes the user's social media activity. For example, the receiving unit analyzes the user's social media posts and provides a relevant receipt method. The receiving unit can also provide an optimal receipt method based on the user's social media interests. For example, the receiving unit analyzes the user's social media activity in real time and provides a relevant receipt method. Next, the receiving unit analyzes the user's social media activity and provides a relevant receipt method when receiving a grant or subsidy. For example, the receiving unit analyzes the user's social media posts and provides a relevant receipt method. The receiving unit can also provide an optimal receipt method based on the user's social media interests. For example, the receiving unit analyzes the user's social media activity in real time and provides a relevant receipt method. In this way, the receiving unit can provide a more appropriate receipt method by analyzing the user's social media activity. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input the user's social media activity data into the generating AI and cause the generating AI to provide the relevant receiving method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, learning unit, provision unit, reception unit, and emotion estimation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects flight reservation status, train operation information, and road congestion information using the communication I / F 44 of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The suggestion unit proposes the optimal means of transportation and route using the specific processing unit 290 of the data processing device 12. The learning unit learns the user's travel history and preferences using the specific processing unit 290 of the data processing device 12. The provision unit provides discount coupons using the control unit 46A of the smart device 14. The reception unit evaluates the effect of congestion mitigation using the specific processing unit 290 of the data processing device 12 and receives subsidies or grants. The emotion estimation unit estimates the user's emotion using the camera 42 and microphone 38B of the smart device 14, and the provision unit adjusts the timing of coupon provision based on the emotion. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, learning unit, provision unit, reception unit, and emotion estimation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects flight reservation status, train operation information, and road congestion information using the communication I / F 44 of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The suggestion unit proposes optimal transportation means and routes using the specific processing unit 290 of the data processing device 12. The learning unit learns the user's travel history and preferences using the specific processing unit 290 of the data processing device 12. The provision unit provides discount coupons using the control unit 46A of the smart glasses 214. The reception unit evaluates the effect of congestion mitigation using the specific processing unit 290 of the data processing device 12 and receives subsidies or grants. The emotion estimation unit estimates the user's emotion using the camera 42 and microphone 238 of the smart glasses 214, and the provision unit adjusts the timing of coupon provision based on the emotion. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, learning unit, provision unit, reception unit, and emotion estimation unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects flight reservation status, train operation information, and road congestion information using the communication I / F 44 of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The suggestion unit proposes optimal transportation means and routes using the specific processing unit 290 of the data processing device 12. The learning unit learns the user's travel history and preferences using the specific processing unit 290 of the data processing device 12. The provision unit provides discount coupons using the control unit 46A of the headset terminal 314. The reception unit evaluates the effect of congestion mitigation using the specific processing unit 290 of the data processing device 12 and receives subsidies or grants. The emotion estimation unit estimates the user's emotion using the camera 42 and microphone 238 of the headset terminal 314, and the provision unit adjusts the timing of coupon provision based on the emotion. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, learning unit, provision unit, reception unit, and emotion estimation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects flight reservation status, train operation information, and road congestion information using the communication I / F 44 of the robot 414. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12. The suggestion unit proposes the optimal means of transportation and route using the specific processing unit 290 of the data processing device 12. The learning unit learns the user's travel history and preferences using the specific processing unit 290 of the data processing device 12. The provision unit provides discount coupons using the control unit 46A of the robot 414. The reception unit evaluates the effect of congestion mitigation using the specific processing unit 290 of the data processing device 12 and receives subsidies or grants. The emotion estimation unit estimates the user's emotion using the camera 42 and microphone 238 of the robot 414, and the provision unit adjusts the timing of coupon provision based on the emotion.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The transportation means suggestion system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit collects vital data, such as the user's heart rate, blood pressure, and body temperature, and monitors the user's health condition in real time. The health monitoring unit then evaluates the user's health condition based on the collected vital data. For example, if the heart rate is abnormally high, the user may be feeling stressed, and the transportation means suggestion system can suggest transportation means that will reduce stress. Also, if the blood pressure is high, the transportation means suggestion system can suggest transportation means that will help the user relax. In this way, the transportation means suggestion system can support the user's health by suggesting the optimal transportation means based on the user's health condition.

[0113] The suggestion unit can suggest a means of transportation and a route taking into consideration the user's hobbies and interests. For example, if the user is interested in historical places, the suggestion unit can suggest a route that goes around historical landmarks. If the user likes to enjoy nature, the suggestion unit can also suggest a route that includes natural parks and scenic spots. Furthermore, if the user is interested in gourmet food, the suggestion unit can also suggest a route that includes restaurants where you can enjoy local specialties. In this way, the suggestion unit can provide a more fulfilling travel experience by suggesting a means of transportation and a route based on the user's hobbies and interests.

[0114] The learning unit can learn the user's behavioral patterns while traveling. For example, if the user often listens to music while traveling, the learning unit can learn the user's favorite music genre and provide an environment in which the user can comfortably enjoy music on the transportation mode suggested by the transportation mode suggestion system. If the user often reads while traveling, the learning unit can learn the user's favorite book genre and provide an environment suitable for reading on the transportation mode suggested by the transportation mode suggestion system. Furthermore, if the user prefers to relax while traveling, the learning unit can learn an environment in which the user can relax and provide an environment in which the user can relax on the transportation mode suggested by the transportation mode suggestion system. In this way, the learning unit can learn the user's behavioral patterns while traveling, thereby enabling more personalized suggestions.

[0115] The collection unit can collect feedback on the user's means of transportation and improve the accuracy of suggestions. For example, after the user uses a proposed means of transportation, the collection unit can collect satisfaction and dissatisfaction with that means of transportation as feedback. Also, after the user uses a proposed route, the collection unit can collect an evaluation of the route as feedback. Next, the collection unit improves the accuracy of suggestions based on the collected feedback. For example, if the user expresses high satisfaction with a particular means of transportation, the collection unit can preferentially suggest that means of transportation. Also, if the user expresses dissatisfaction with a particular route, the collection unit can make a suggestion to avoid that route. In this way, the collection unit can improve the accuracy of suggestions by collecting user feedback.

[0116] The providing unit can estimate the user's emotions and adjust the content of the coupon based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide coupons for relaxing services or products. Also, if the user is relaxed, the providing unit can provide coupons for active experiences or products. For example, if the user is in a hurry, the providing unit can provide coupons for services that can be used quickly. In this way, the providing unit can provide more effective coupons by adjusting the content of the coupon based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0117] The transportation suggestion system may further include a safety monitoring unit to ensure the safety of the user during travel. The safety monitoring unit, for example, monitors the user's location information in real time while the user is traveling and detects abnormal behavior or entry into a dangerous area. The safety monitoring unit then notifies the user of the detected abnormality or danger and encourages the user to take appropriate action. For example, if the user enters a dangerous area, the safety monitoring unit issues a warning to the user and suggests changing to a safer route. Furthermore, if the user exhibits abnormal behavior, the safety monitoring unit may notify an emergency contact and encourage a prompt response. In this way, the transportation suggestion system ensures the user's safety during travel, allowing the user to enjoy travel with peace of mind.

[0118] The collection unit can estimate the user's emotions and adjust the content of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data that causes stress and provide it to the suggestion unit. Also, if the user is relaxed, the collection unit can prioritize collecting data that helps maintain relaxation. For example, if the user is in a hurry, the collection unit prioritizes collecting data that can be collected quickly. This allows the collection unit to adjust the content of data collection based on the user's emotions, thereby enabling more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0119] The collection unit collects environmental data during the user's travel, thereby improving the accuracy of suggestions. For example, the collection unit collects environmental data such as temperature, humidity, and noise levels during the user's travel. The collection unit can also collect traffic conditions and weather information during the user's travel. Next, the collection unit improves the accuracy of suggestions based on the collected environmental data. For example, the collection unit proposes the optimal means of transportation and route taking into account the temperature and humidity ranges within which the user can travel comfortably. It can also prioritize the proposal of routes with low noise levels. In this way, the collection unit can provide a more comfortable travel experience by collecting environmental data during the user's travel.

[0120] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can collect detailed data and provide more accurate suggestions. For example, if the user is in a hurry, the collection unit can quickly collect necessary data and provide immediate suggestions. This allows the collection unit to adjust the frequency of data collection based on the user's emotions, thereby reducing the user's burden and enabling more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0121] The collection unit collects energy consumption data during the user's travel, and can improve the accuracy of suggestions. For example, the collection unit collects energy consumption data such as the user's calorie consumption, number of steps, and travel distance during travel. The collection unit can also collect the user's exercise intensity and heart rate during travel. Next, the collection unit improves the accuracy of suggestions based on the collected energy consumption data. For example, if the user desires healthy travel, a means of travel that consumes a large number of calories can be suggested. Also, if the user desires a relaxed travel, a means of travel that consumes a low amount of exercise can be suggested. In this way, the collection unit can provide a healthier and more comfortable travel experience by collecting energy consumption data during the user's travel.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The collection unit collects data. The data includes, for example, flight reservation status, train operation information, and road congestion information. The collection unit collects flight reservation status using an airline's API, collects train operation information using a railway company's API, and collects road congestion information using a traffic management system. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, it analyzes trends in airline reservations, predicts train operation patterns, and predicts road congestion. Step 3: The proposal unit proposes transportation methods and routes based on the analysis results obtained by the analysis unit. The proposal is made taking into consideration the shortest distance, shortest time, traffic conditions, etc. For example, the proposal will be based on the shortest distance, the shortest time, or the traffic conditions, and will propose a route that avoids congestion. Step 4: The learning unit learns the user's movement history and preferences. Learning is performed using a machine learning algorithm. For example, preferences are learned based on the user's past movement history, based on survey results, and based on the user's selection history.

[0124] 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.

[0125] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The 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.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 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.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the 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.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 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 collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that suggests means of transportation and routes based on the analysis results obtained by the analysis unit; A learning unit that learns the user's movement history and preferences. A system characterized by:

2. The proposal unit Suggest transportation options and routes to users 2. The system of claim 1.

3. The learning unit Learn user movement history and preferences 2. The system of claim 1.

4. The collecting unit Collecting flight reservation status, train operation information, and road congestion information 2. The system of claim 1.

5. It has a provision department that provides discount coupons from various companies.

2. The system of claim 1.

6. Equipping the facility with a receiving department that receives subsidies and grants from national and local governments for easing traffic congestion 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze past data collection history and select data collection methods 2. The system of claim 1.

9. The collecting unit Filtering data at the time of collection based on the user's current mobility and interests 2. The system of claim 1.

10. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

Images