System

Abstract

An object of a system according to an embodiment is to predict a distribution state of homecoming customers at the time of occurrence of a natural disaster and provide appropriate train operation information.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a notification unit. The collection unit collects position information of a mobile phone or a smartphone. The analysis unit analyzes the position information collected by the collection unit and predicts a distribution state of the homecoming customers. The providing unit provides the prediction result obtained by the analyzing unit to the railway company. The notification unit notifies the user of information on the train to be thinned out based on the information provided by the provision unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to quickly and accurately grasp the distribution of people returning home in the event of a natural disaster and provide appropriate train operation information.

[0005] The system according to the embodiment aims to predict the distribution of people returning home in the event of a natural disaster and provide appropriate train operation information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a notification unit. The collection unit collects location information from mobile phones and smartphones. The analysis unit analyzes the location information collected by the collection unit and predicts the distribution of people returning home. The provision unit provides the prediction results obtained by the analysis unit to the railway company. The notification unit notifies the user of information about trains that will be thinned out based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the distribution of people returning home in the event of a natural disaster and provide appropriate train operation information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A prediction system according to an embodiment of the present invention predicts the distribution of people returning home and other passengers at Shinkansen stations by using a generation AI to process location data from mobile phone and smartphone users when a natural disaster occurs. The prediction system provides the prediction results to railway companies, which use them to design timetables for reduced service. Furthermore, the prediction system promptly provides information about trains that will be reduced to mobile phone and smartphone users. For example, when a natural disaster occurs, the prediction system collects location data from mobile phone and smartphone users in real time. The prediction system then analyzes this data using a generation AI to predict the distribution of people returning home and other passengers at Shinkansen stations. For example, if a large number of users are concentrated at a specific station, congestion at that station is predicted. The prediction system provides the prediction results to railway companies, which use them to design timetables for reduced service. For example, reduced service at stations where congestion is predicted ensures user safety. Furthermore, the prediction system promptly provides information about reduced trains to mobile phone and smartphone users. This makes it easier for users to know that their designated trains have been reduced. Furthermore, the prediction system provides notifications to users so that they know when their designated trains have been reduced. For example, if a train designated by a user is reduced, that information is sent to a mobile phone or smartphone, allowing the user to respond quickly. This allows the prediction system to predict congestion at Shinkansen stations along the line in the event of a natural disaster, allowing railway companies to design appropriate timetables. Furthermore, since users are provided with information about reduced trains quickly, user convenience is improved.

[0029] The prediction system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a notification unit. The collection unit collects location information from mobile phones and smartphones. The collection unit can collect, for example, GPS data, Wi-Fi location information, cell tower data, and the like. The collection unit can also collect location information in real time. For example, the collection unit periodically updates user location information to obtain the latest data. The analysis unit uses a generation AI to analyze the location information collected by the collection unit and predict the distribution of returning homebound passengers, etc. The analysis unit analyzes the location information using, for example, a machine learning algorithm. For example, the analysis unit predicts the distribution of returning homebound passengers using algorithms such as K-means clustering or random forest. The analysis unit can also use the generation AI to analyze patterns of location information and identify stations where congestion is predicted. The provision unit provides the prediction results obtained by the analysis unit to a railway company. The provision unit can provide the prediction results in the form of, for example, numerical data, graphs, heat maps, and the like. The provision unit can also provide the prediction results to a railway company in real time and use them to design reduced service schedules. The notification unit notifies the user of information about the trains that will be discontinued based on the information provided by the provision unit. The notification unit provides the user with information about the trains that will be discontinued, for example, by using push notification or SMS. The notification unit can also provide an interface or application that allows the user to know that their designated train has been discontinued. For example, when a train designated by the user has been discontinued, the notification unit notifies the user of this information via a mobile phone or smartphone. This allows the prediction system according to the embodiment to predict the distribution of passengers returning home and other travelers at Shinkansen line stations in the event of a natural disaster, enabling railway companies to design appropriate timetables. Furthermore, since information about discontinued trains is provided to the user promptly, user convenience is improved.

[0030] The collection unit can collect location information from mobile phones and smartphones in real time. The collection unit collects, for example, GPS data, Wi-Fi location information, cell tower data, etc. in real time. For example, the collection unit periodically updates user location information to obtain the latest data. In addition, the collection unit can use technology to minimize data update frequency and delay time to collect location information in real time. For example, the collection unit can grasp user location information in real time by increasing the data update frequency. As a result, by collecting location information in real time, the latest distribution status can be grasped. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input location information data collected in real time to a generation AI, which then analyzes the data.

[0031] The analysis unit can analyze location information using a machine learning algorithm and predict the distribution of returning visitors, etc. The analysis unit analyzes the location information using, for example, K-means clustering. For example, the analysis unit divides the location information into clusters and calculates the center point of each cluster to predict the distribution of returning visitors. The analysis unit can also analyze the location information using a random forest. For example, the analysis unit trains a random forest model using location information as a feature to predict the distribution of returning visitors. The analysis unit can also use a generation AI to analyze patterns of location information and identify stations where congestion is predicted. For example, the analysis unit inputs location information data to the generation AI, which analyzes the data and outputs a prediction result. In this way, the use of a machine learning algorithm improves the prediction accuracy of the distribution state. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can analyze location information data using a machine learning algorithm and output a prediction result using the generation AI.

[0032] The providing unit can provide the prediction results to the railway company and use them in designing a timetable for reduced service operations. The providing unit provides the prediction results in the form of, for example, numerical data, graphs, heat maps, etc. For example, the providing unit can provide the prediction results to the railway company as numerical data and use them in designing a timetable for reduced service operations. The providing unit can also visually display the prediction results as graphs or heat maps so that the railway company can easily understand them. For example, the providing unit can display stations where congestion is predicted in a heat map, providing information for the railway company to design an appropriate timetable. As a result, providing the prediction results to the railway company enables appropriate timetable design. Some or all of the above-mentioned 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 output the prediction results as numerical data, graphs, or heat maps using a generation AI and provide them to the railway company.

[0033] The notification unit can provide the user with information about the trains to be withdrawn using push notifications or SMS. The notification unit can provide the user with information about the trains to be withdrawn using push notifications, for example. For example, the notification unit can send a push notification to the user's smartphone to quickly provide the user with information about the trains to be withdrawn. The notification unit can also provide the user with information about the trains to be withdrawn using SMS. For example, the notification unit can send an SMS to the user's mobile phone to provide the user with information about the trains to be withdrawn. In this way, by using push notifications or SMS, the information can be quickly provided to the user. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can generate information about the trains to be withdrawn using a generation AI and provide the information to the user as a push notification or SMS.

[0034] The notification unit can provide an interface or application that allows a user to know that their designated train has been withdrawn. The notification unit, for example, provides an interface that allows a user to know that their designated train has been withdrawn. For example, the notification unit provides an interface that allows a user to check whether a train designated by the user has been withdrawn through a web app or a mobile app. The notification unit can also provide an application that allows a user to know that their designated train has been withdrawn. For example, if a train designated by the user has been withdrawn, the notification unit notifies the user of this information through an application. This makes it easier for the user to know that their designated train has been withdrawn. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can generate information about trains that will be withdrawn using a generation AI and provide the information to the user through an interface or an application.

[0035] The collection unit can analyze the user's past movement history and select the optimal collection method. The collection unit, for example, sets location information collection points based on places the user frequently visited in the past. For example, the collection unit analyzes the user's past movement history, identifies frequently visited places, and prioritizes collecting location information at those places. The collection unit can also analyze the user's past movement patterns and propose an efficient collection route. For example, the collection unit analyzes the user's movement patterns and sets an optimal collection route. The collection unit can also concentrate collection during a specific time period based on the user's past movement history. For example, if the user frequently moves during a specific time period, the collection unit concentrates collection of location information during that time period. This enables efficient collection of location information by selecting the optimal collection method based on the past movement 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 the user's past movement history data into a generation AI, which can select the optimal collection method.

[0036] When collecting location information, the collection unit can filter the location information based on the user's current activity status and areas of interest. For example, if the user is shopping, the collection unit prioritizes collecting location information within a commercial facility. For example, the collection unit analyzes the user's current activity status and determines that the user is shopping, and prioritizes collecting location information within the commercial facility. In addition, if the user is sightseeing, the collection unit can also collect location information around tourist spots. For example, the collection unit analyzes the user's current activity status and determines that the user is sightseeing, and collects location information around tourist spots. In addition, the collection unit can also collect location information along the commuting route if the user is commuting. For example, the collection unit analyzes the user's current activity status and determines that the user is commuting, and collects location information along the commuting route. This makes it possible to collect location information according to the user's activity status and areas of interest. Some or all of the above-described processing by 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 activity status data into a generation AI, and the generation AI can perform filtering.

[0037] When collecting location information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects location information using voice recognition technology. For example, the collection unit analyzes the user's voice input and acquires location information. Furthermore, if the user uses text input, the collection unit can also collect location information using text analysis technology. For example, the collection unit analyzes the user's text input and acquires location information. Furthermore, if the user uses image input, the collection unit can also collect location information using image recognition technology. For example, the collection unit analyzes the user's image input and acquires location information. This enables efficient collection of location information by selecting the optimal collection means depending on the user's input method. 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 input data to a generation AI, which can select the optimal collection means.

[0038] When collecting location information, the collection unit can prioritize collecting highly relevant information by taking the user's geographical location information into consideration. For example, if the user is in an urban area, the collection unit prioritizes collecting information about surrounding commercial facilities and traffic. For example, if the collection unit analyzes the user's geographical location information and determines that the user is in an urban area, it prioritizes collecting information about commercial facilities and traffic. Furthermore, if the user is in a suburban area, the collection unit can prioritize collecting information about the natural environment and tourist spots. For example, if the collection unit analyzes the user's geographical location information and determines that the user is in a suburban area, it prioritizes collecting information about the natural environment and tourist spots. Furthermore, if the user is at a specific event venue, the collection unit can prioritize collecting information related to the event. For example, if the collection unit analyzes the user's geographical location information and determines that the user is at a specific event venue, it prioritizes collecting information related to the event. This enables efficient information collection by prioritized collection of highly relevant information based on 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 AI. For example, the collection unit can input the user's geographical location information data into the generation AI, allowing the generation AI to prioritize collection of highly relevant information.

[0039] When collecting location information, the collection unit can analyze the user's social media activity and collect related information. The collection unit, for example, collects location information of places where the user has checked in on social media. For example, the collection unit analyzes the user's social media activity and collects location information of places where the user has checked in. The collection unit can also analyze the content of the user's social media posts and collect location information of related places. For example, the collection unit analyzes the content of the user's posts and collects location information of related places. The collection unit can also collect location information of related places by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the activities of the user's friends and collects location information of related places. This enables more detailed information to be collected by collecting related information based on the user's social media activity. Some or all of the above-described processing by 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 social media activity data into a generation AI, which then collects related information.

[0040] When collecting location information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit adjusts the location information collection method based on, for example, feedback provided by the user in the past. For example, the collection unit analyzes the user's past feedback and customizes the collection method. The collection unit can also prioritize collecting location information of places the user liked in the past. For example, the collection unit analyzes the user's past feedback and prioritizes collecting location information of places the user liked. The collection unit can also prevent collecting location information of places the user avoided in the past. For example, the collection unit analyzes the user's past feedback and prevents collecting location information of avoided places. This customizes the collection method by reflecting the user's past feedback, enabling more appropriate information collection. Some or all of the above-described processing by 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 past feedback data into a generation AI and use the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the location information. For example, the analysis unit performs a detailed analysis on important location information. For example, the analysis unit evaluates the importance of the location information and performs a detailed data analysis on important location information. The analysis unit can also perform a simplified analysis on less important location information. For example, the analysis unit evaluates the importance of the location information and performs a simplified data analysis on less important location information. The analysis unit can also determine the priority of the analysis based on the importance of the location information. For example, the analysis unit evaluates the importance of the location information and prioritizes analysis of important location information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the location information. 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 importance data of the location information to a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of location information. For example, the analysis unit applies an algorithm for analyzing purchasing behavior to location information of commercial facilities. For example, the analysis unit analyzes the location information of commercial facilities and applies an algorithm for predicting purchasing behavior. The analysis unit can also apply an algorithm for analyzing tourist behavior to location information of tourist spots. For example, the analysis unit analyzes the location information of tourist spots and applies an algorithm for predicting tourist behavior. The analysis unit can also apply an algorithm for analyzing movement patterns to traffic information. For example, the analysis unit analyzes traffic information and applies an algorithm for predicting movement patterns. This enables more accurate analysis by applying an analysis algorithm depending on the category of location information. 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 category data of the location information to a generation AI, which can then apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and optimizes the algorithm parameters. The analysis unit can also correct errors based on the user's past analysis results to improve accuracy. For example, the analysis unit corrects errors based on the past analysis results and improves prediction accuracy. The analysis unit can also optimize the analysis parameters by referring to the user's past analysis results. For example, the analysis unit adjusts parameters based on the past analysis results and provides optimal analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. 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 user's past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the location information was collected. The analysis unit, for example, prioritizes analysis of the most recent location information. For example, the analysis unit evaluates the time when the location information was collected and prioritizes analysis of the most recent location information. The analysis unit can also lower the analysis priority of location information collected earlier. For example, the analysis unit evaluates the time when the location information was collected and lowers the analysis priority of older location information. The analysis unit can also adjust the analysis schedule depending on the time when the location information was collected. For example, the analysis unit evaluates the time when the location information was collected and sets a schedule that prioritizes analysis of the most recent location information. This enables efficient analysis by determining the priority of analysis based on the time when the location information 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 data on the time when the location information was collected into a generation AI, and the generation AI can determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the location information. The analysis unit, for example, prioritizes analysis of highly relevant location information. For example, the analysis unit evaluates the relevance of the location information and prioritizes analysis of highly relevant location information. The analysis unit can also postpone the order of analysis of less relevant location information. For example, the analysis unit evaluates the relevance of the location information and postpones the order of analysis of less relevant location information. The analysis unit can also adjust the analysis schedule according to the relevance of the location information. For example, the analysis unit evaluates the relevance of the location information and sets a schedule to prioritize analysis of highly relevant location information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the location information. 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 relevance data of location information to a generation AI, and the generation AI can adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. For example, the analysis unit evaluates the user's level of expertise and uses detailed technical terms if it determines that the user has technical expertise. The analysis unit can also explain the analysis results in simple terms if the user does not have technical expertise. For example, the analysis unit evaluates the user's level of expertise and selects an appropriate expression method. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI, and the generation AI can adjust the use of technical terms in the analysis.

[0047] The providing unit can adjust the level of detail of the provided information based on the importance of the prediction result when providing the information. The providing unit, for example, provides detailed information for important prediction results. For example, the providing unit evaluates the importance of the prediction result and provides information including detailed data and explanations for important prediction results. The providing unit can also provide simplified information for prediction results with low importance. For example, the providing unit evaluates the importance of the prediction result and provides simplified data and explanations for prediction results with low importance. The providing unit can also determine the priority of the information provided based on the importance of the prediction result. For example, the providing unit evaluates the importance of the prediction result and provides information preferentially for important prediction results. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the prediction result. 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 can input importance data of the prediction result to the generating AI, and the generating AI can adjust the level of detail of the information provided.

[0048] The providing unit can apply different providing methods depending on the category of the prediction result when providing the information. For example, the providing unit provides traffic information in real time. For example, the providing unit provides traffic information in real time, allowing the user to obtain the latest information. The providing unit can also apply a providing method including detailed explanations to tourist information. For example, the providing unit provides tourist information in a format including detailed data and explanations. The providing unit can also apply a providing method including coupons and special offers to commercial facility information. For example, the providing unit provides information on commercial facilities in a format including coupons and special offers. This enables more appropriate information to be provided by applying a providing method depending on the category of the prediction result. 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 category data of the prediction result to the generation AI, and the generation AI can apply an appropriate providing method.

[0049] The providing unit can improve the accuracy of information provision by referring to the user's past provision results when providing information. The providing unit, for example, adjusts the provision method based on the user's past provision results. For example, the providing unit analyzes the user's past provision results and optimizes the provision method. The providing unit can also correct errors based on the user's past provision results to improve accuracy. For example, the providing unit corrects errors based on the past provision results and improves the accuracy of information provision. The providing unit can also optimize the parameters of provision by referring to the user's past provision results. For example, the providing unit adjusts parameters based on the past provision results and provides optimal information. This improves the accuracy of information provision by referring to the user's past provision results. 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 provision result data into a generation AI, which can improve the accuracy of information provision.

[0050] The providing unit can determine the priority of provision based on the collection time of the prediction results at the time of provision. The providing unit, for example, prioritizes providing the most recent prediction results. For example, the providing unit evaluates the collection time of the prediction results and prioritizes providing the most recent prediction results. The providing unit can also lower the priority of provision for prediction results that were collected earlier. For example, the providing unit evaluates the collection time of the prediction results and lowers the priority of provision for older prediction results. The providing unit can also adjust the provision schedule depending on the collection time of the prediction results. For example, the providing unit evaluates the collection time of the prediction results and sets a schedule that prioritizes providing the most recent prediction results. This enables efficient information provision by determining the priority of provision based on the collection time of the prediction results. 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 can input data on the collection time of the prediction results to a generation AI, and the generation AI can determine the priority of provision.

[0051] The providing unit can adjust the order of provision based on the relevance of the prediction results when providing them. The providing unit, for example, prioritizes providing highly relevant prediction results. For example, the providing unit evaluates the relevance of the prediction results and prioritizes providing highly relevant prediction results. The providing unit can also postpone the order of provision of less relevant prediction results. For example, the providing unit evaluates the relevance of the prediction results and postpones the order of provision of less relevant prediction results. The providing unit can also adjust the provision schedule according to the relevance of the prediction results. For example, the providing unit evaluates the relevance of the prediction results and sets a schedule to prioritize providing highly relevant prediction results. This enables efficient information provision by adjusting the order of provision based on the relevance of the prediction results. 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 can input relevance data of the prediction results to a generation AI, and the generation AI can adjust the order of provision.

[0052] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. For example, if the user has technical expertise, the providing unit uses detailed technical terminology. For example, the providing unit evaluates the user's level of expertise and determines that the user has technical expertise, and uses detailed technical terminology. The providing unit can also explain the provided results in simple terms if the user does not have technical expertise. For example, the providing unit evaluates the user's level of expertise and determines that the user does not have technical expertise, and explains the provided results in simple terms. The providing unit can also adjust the way the provided results are expressed according to the user's level of expertise. For example, the providing unit evaluates the user's level of expertise and selects an appropriate way of expression. This enables the provision of information that is easier to understand by adjusting the use of technical terminology provided according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into a generation AI, and the generation AI can adjust the use of technical terminology provided.

[0053] The notification unit can adjust the level of detail of the notification based on the importance of the information at the time of notification. For example, the notification unit provides detailed notification for important information. For example, the notification unit evaluates the importance of the information and provides a notification including detailed data and explanations for important information. The notification unit can also provide a simplified notification for low-importance information. For example, the notification unit evaluates the importance of the information and provides a notification including simplified data and explanations for low-importance information. The notification unit can also determine the priority of notifications according to the importance of the information. For example, the notification unit evaluates the importance of the information and provides a notification of important information with priority. This enables efficient notification by adjusting the level of detail of the notification based on the importance of the information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information importance data to a generation AI, and the generation AI can adjust the level of detail of the notification.

[0054] The notification unit can apply different notification algorithms depending on the category of information when notifying. For example, the notification unit can notify traffic information in real time. For example, the notification unit can notify traffic information in real time, allowing the user to obtain the latest information. The notification unit can also apply a notification method including detailed explanations to tourist information. For example, the notification unit can notify tourist information in a format including detailed data and explanations. The notification unit can also apply a notification method including coupons and special offers to information about commercial facilities. For example, the notification unit can notify information about commercial facilities in a format including coupons and special offers. This allows for more appropriate notifications by applying a notification algorithm depending on the category of information. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input information category data to a generation AI, which can then apply an appropriate notification algorithm.

[0055] The notification unit can improve the accuracy of notifications by referring to the user's past notification results. The notification unit, for example, adjusts the notification method based on the user's past notification results. For example, the notification unit analyzes the user's past notification results and optimizes the notification method. The notification unit can also correct errors based on the user's past notification results to improve accuracy. For example, the notification unit corrects errors based on the past notification results and improves notification accuracy. The notification unit can also optimize notification parameters by referring to the user's past notification results. For example, the notification unit adjusts parameters based on the past notification results and provides optimal notifications. This improves notification accuracy by referring to the user's past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification result data into a generation AI, which can improve notification accuracy.

[0056] The notification unit can determine the priority of notifications based on the time when the information was collected at the time of notification. The notification unit, for example, prioritizes notifying the latest information. For example, the notification unit evaluates the time when the information was collected and prioritizes notifying the latest information. The notification unit can also lower the priority of notifications for information that was collected earlier. For example, the notification unit evaluates the time when the information was collected and lowers the priority of notifications for older information. The notification unit can also adjust the notification schedule according to the time when the information was collected. For example, the notification unit evaluates the time when the information was collected and sets a schedule that prioritizes notifying the latest information. This enables efficient notifications by determining the priority of notifications based on the time when the information was collected. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information collection time data into a generation AI, and the generation AI can determine the priority of notifications.

[0057] The notification unit can adjust the order of notifications based on the relevance of the information at the time of notification. The notification unit, for example, prioritizes notifying highly relevant information. For example, the notification unit evaluates the relevance of the information and prioritizes notifying highly relevant information. The notification unit can also postpone the order of notifications for less relevant information. For example, the notification unit evaluates the relevance of the information and postpones the order of notifications for less relevant information. The notification unit can also adjust the notification schedule according to the relevance of the information. For example, the notification unit evaluates the relevance of the information and sets a schedule to prioritize notifying highly relevant information. This enables efficient notifications by adjusting the order of notifications based on the relevance of the information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information relevance data to a generation AI, and the generation AI can adjust the order of notifications.

[0058] The notification unit can adjust the use of technical terms in the notification according to the user's level of expertise when providing a notification. For example, if the user has technical expertise, the notification unit uses detailed technical terms. For example, the notification unit evaluates the user's level of expertise and determines that the user has technical expertise, and uses detailed technical terms. The notification unit can also explain the notification result in simple terms if the user does not have technical expertise. For example, the notification unit evaluates the user's level of expertise and determines that the user does not have technical expertise, and explains the notification result in simple terms. The notification unit can also adjust the way the notification result is expressed according to the user's level of expertise. For example, the notification unit evaluates the user's level of expertise and selects an appropriate way of expression. This allows for a more easily understandable notification by adjusting the use of technical terms in the notification according to the user's level of expertise. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's level of expertise data into a generation AI, and the generation AI can adjust the use of technical terms in the notification.

[0059] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and optimizes the algorithm parameters. The analysis unit can also correct errors based on the user's past analysis results to improve accuracy. For example, the analysis unit corrects errors based on the past analysis results and improves prediction accuracy. The analysis unit can also optimize the analysis parameters by referring to the user's past analysis results. For example, the analysis unit adjusts parameters based on the past analysis results and provides optimal analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. 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 user's past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0060] The providing unit can determine the priority of provision based on the collection time of the prediction results at the time of provision. The providing unit, for example, prioritizes providing the most recent prediction results. For example, the providing unit evaluates the collection time of the prediction results and prioritizes providing the most recent prediction results. The providing unit can also lower the priority of provision for prediction results that were collected earlier. For example, the providing unit evaluates the collection time of the prediction results and lowers the priority of provision for older prediction results. The providing unit can also adjust the provision schedule depending on the collection time of the prediction results. For example, the providing unit evaluates the collection time of the prediction results and sets a schedule that prioritizes providing the most recent prediction results. This enables efficient information provision by determining the priority of provision based on the collection time of the prediction results. 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 can input data on the collection time of the prediction results to a generation AI, and the generation AI can determine the priority of provision.

[0061] The notification unit can determine the priority of notifications based on the time when the information was collected at the time of notification. The notification unit, for example, prioritizes notifying the latest information. For example, the notification unit evaluates the time when the information was collected and prioritizes notifying the latest information. The notification unit can also lower the priority of notifications for information that was collected earlier. For example, the notification unit evaluates the time when the information was collected and lowers the priority of notifications for older information. The notification unit can also adjust the notification schedule according to the time when the information was collected. For example, the notification unit evaluates the time when the information was collected and sets a schedule that prioritizes notifying the latest information. This enables efficient notifications by determining the priority of notifications based on the time when the information was collected. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information collection time data into a generation AI, and the generation AI can determine the priority of notifications.

[0062] The notification unit can adjust the order of notifications based on the relevance of the information at the time of notification. The notification unit, for example, prioritizes notifying highly relevant information. For example, the notification unit evaluates the relevance of the information and prioritizes notifying highly relevant information. The notification unit can also postpone the order of notifications for less relevant information. For example, the notification unit evaluates the relevance of the information and postpones the order of notifications for less relevant information. The notification unit can also adjust the notification schedule according to the relevance of the information. For example, the notification unit evaluates the relevance of the information and sets a schedule to prioritize notifying highly relevant information. This enables efficient notifications by adjusting the order of notifications based on the relevance of the information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information relevance data to a generation AI, and the generation AI can adjust the order of notifications.

[0063] The notification unit can adjust the use of technical terms in the notification according to the user's level of expertise when providing a notification. For example, if the user has technical expertise, the notification unit uses detailed technical terms. For example, the notification unit evaluates the user's level of expertise and determines that the user has technical expertise, and uses detailed technical terms. The notification unit can also explain the notification result in simple terms if the user does not have technical expertise. For example, the notification unit evaluates the user's level of expertise and determines that the user does not have technical expertise, and explains the notification result in simple terms. The notification unit can also adjust the way the notification result is expressed according to the user's level of expertise. For example, the notification unit evaluates the user's level of expertise and selects an appropriate way of expression. This allows for a more easily understandable notification by adjusting the use of technical terms in the notification according to the user's level of expertise. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's level of expertise data into a generation AI, and the generation AI can adjust the use of technical terms in the notification.

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

[0065] The prediction system may further include a history analysis unit that analyzes the user's past travel history. The history analysis unit analyzes the places and travel patterns that the user has frequently visited in the past and improves the accuracy of the prediction based on this. For example, if the user has frequently used a specific station in the past, the history analysis unit can more accurately predict congestion at that station. The history analysis unit can also predict congestion during a specific time period based on the user's past travel patterns. Furthermore, the history analysis unit can customize the prediction results based on the user's past travel history and provide optimal information for each individual user. This allows the prediction system to make more accurate predictions by utilizing the user's past travel history.

[0066] The collection unit may include an activity analysis unit that analyzes the user's current activity status. The activity analysis unit adjusts the method of collecting location information based on the user's current activity. For example, if the user is shopping, the collection unit may prioritize collecting location information within a commercial facility. If the user is sightseeing, the collection unit may also collect location information around tourist spots. Furthermore, if the user is commuting, the collection unit may also collect location information along the commuting route. This allows the collection unit to collect location information according to the user's activity status.

[0067] The providing unit can adjust the level of detail to be provided based on the importance of the prediction result. For example, detailed information can be provided for important prediction results. Simplified information can be provided for less important prediction results. The providing unit can also determine the priority of provision according to the importance of the prediction results. This allows the providing unit to provide information efficiently based on the importance of the prediction results.

[0068] The collection unit may include a social analysis unit that analyzes the user's social media activities. The social analysis unit analyzes the places where the user has checked in on social media and the content of posts, and collects related location information. For example, the social analysis unit may collect location information of places where the user has checked in on social media. It may also analyze the content of posts by the user and collect location information of related places. This allows the collection unit to collect location information based on the user's social media activities.

[0069] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis algorithm can be adjusted based on the user's past analysis results. The accuracy can also be improved by correcting errors from the user's past analysis results. Furthermore, the analysis parameters can be optimized by referring to the user's past analysis results. This allows the analysis unit to utilize the user's past analysis results to perform more accurate analysis.

[0070] The notification unit can determine the priority of notifications based on the time when the information was collected. For example, the latest information can be given priority in notification. The notification priority of information collected earlier can also be lowered. The notification schedule can also be adjusted depending on the time when the information was collected. This allows the notification unit to provide efficient notifications based on the time when the information was collected.

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

[0072] Step 1: The collection unit collects location information from mobile phones and smartphones. The collection unit can collect, for example, GPS data, Wi-Fi location information, cell tower data, etc. The collection unit can also collect location information in real time. For example, the collection unit periodically updates the user's location information to obtain the latest data. Step 2: The analysis unit uses the generation AI to analyze the location information collected by the collection unit and predict the distribution of returning home visitors, etc. The analysis unit analyzes the location information using, for example, a machine learning algorithm. For example, the analysis unit predicts the distribution of returning home visitors using algorithms such as K-means clustering or random forest. The analysis unit can also use the generation AI to analyze patterns in the location information and identify stations where congestion is predicted. Step 3: The providing unit provides the prediction results obtained by the analysis unit to the railway company. The providing unit can provide the prediction results in the form of numerical data, graphs, heat maps, etc. The providing unit can also provide the prediction results to the railway company in real time and use them to design a timetable for reduced service. Step 4: The notification unit notifies the user of the information about the trains that will be dropped based on the information provided by the provision unit. The notification unit provides the user with the information about the trains that will be dropped, for example, by using push notification or SMS. The notification unit can also provide an interface or application that allows the user to know that their specified train has been dropped. For example, when a train specified by the user has been dropped, the notification unit notifies the user of that information via a mobile phone or smartphone.

[0073] (Example 2) A prediction system according to an embodiment of the present invention predicts the distribution of people returning home and other passengers at Shinkansen stations by using a generation AI to process location data from mobile phone and smartphone users when a natural disaster occurs. The prediction system provides the prediction results to railway companies, which use them to design timetables for reduced service. Furthermore, the prediction system promptly provides information about trains that will be reduced to mobile phone and smartphone users. For example, when a natural disaster occurs, the prediction system collects location data from mobile phone and smartphone users in real time. The prediction system then analyzes this data using a generation AI to predict the distribution of people returning home and other passengers at Shinkansen stations. For example, if a large number of users are concentrated at a specific station, congestion at that station is predicted. The prediction system provides the prediction results to railway companies, which use them to design timetables for reduced service. For example, reduced service at stations where congestion is predicted ensures user safety. Furthermore, the prediction system promptly provides information about reduced trains to mobile phone and smartphone users. This makes it easier for users to know that their designated trains have been reduced. Furthermore, the prediction system provides notifications to users so that they know when their designated trains have been reduced. For example, if a train designated by a user is reduced, that information is sent to a mobile phone or smartphone, allowing the user to respond quickly. This allows the prediction system to predict congestion at Shinkansen stations along the line in the event of a natural disaster, allowing railway companies to design appropriate timetables. Furthermore, since users are provided with information about reduced trains quickly, user convenience is improved.

[0074] The prediction system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a notification unit. The collection unit collects location information from mobile phones and smartphones. The collection unit can collect, for example, GPS data, Wi-Fi location information, cell tower data, and the like. The collection unit can also collect location information in real time. For example, the collection unit periodically updates user location information to obtain the latest data. The analysis unit uses a generation AI to analyze the location information collected by the collection unit and predict the distribution of returning homebound passengers, etc. The analysis unit analyzes the location information using, for example, a machine learning algorithm. For example, the analysis unit predicts the distribution of returning homebound passengers using algorithms such as K-means clustering or random forest. The analysis unit can also use the generation AI to analyze patterns of location information and identify stations where congestion is predicted. The provision unit provides the prediction results obtained by the analysis unit to a railway company. The provision unit can provide the prediction results in the form of, for example, numerical data, graphs, heat maps, and the like. The provision unit can also provide the prediction results to a railway company in real time and use them to design reduced service schedules. The notification unit notifies the user of information about the trains that will be discontinued based on the information provided by the provision unit. The notification unit provides the user with information about the trains that will be discontinued, for example, by using push notification or SMS. The notification unit can also provide an interface or application that allows the user to know that their designated train has been discontinued. For example, when a train designated by the user has been discontinued, the notification unit notifies the user of this information via a mobile phone or smartphone. This allows the prediction system according to the embodiment to predict the distribution of passengers returning home and other travelers at Shinkansen line stations in the event of a natural disaster, enabling railway companies to design appropriate timetables. Furthermore, since information about discontinued trains is provided to the user promptly, user convenience is improved.

[0075] The collection unit can collect location information from mobile phones and smartphones in real time. The collection unit collects, for example, GPS data, Wi-Fi location information, cell tower data, etc. in real time. For example, the collection unit periodically updates user location information to obtain the latest data. In addition, the collection unit can use technology to minimize data update frequency and delay time to collect location information in real time. For example, the collection unit can grasp user location information in real time by increasing the data update frequency. As a result, by collecting location information in real time, the latest distribution status can be grasped. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input location information data collected in real time to a generation AI, which then analyzes the data.

[0076] The analysis unit can analyze location information using a machine learning algorithm and predict the distribution of returning visitors, etc. The analysis unit analyzes the location information using, for example, K-means clustering. For example, the analysis unit divides the location information into clusters and calculates the center point of each cluster to predict the distribution of returning visitors. The analysis unit can also analyze the location information using a random forest. For example, the analysis unit trains a random forest model using location information as a feature to predict the distribution of returning visitors. The analysis unit can also use a generation AI to analyze patterns of location information and identify stations where congestion is predicted. For example, the analysis unit inputs location information data to the generation AI, which analyzes the data and outputs a prediction result. In this way, the use of a machine learning algorithm improves the prediction accuracy of the distribution state. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can analyze location information data using a machine learning algorithm and output a prediction result using the generation AI.

[0077] The providing unit can provide the prediction results to the railway company and use them in designing a timetable for reduced service operations. The providing unit provides the prediction results in the form of, for example, numerical data, graphs, heat maps, etc. For example, the providing unit can provide the prediction results to the railway company as numerical data and use them in designing a timetable for reduced service operations. The providing unit can also visually display the prediction results as graphs or heat maps so that the railway company can easily understand them. For example, the providing unit can display stations where congestion is predicted in a heat map, providing information for the railway company to design an appropriate timetable. As a result, providing the prediction results to the railway company enables appropriate timetable design. Some or all of the above-mentioned 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 output the prediction results as numerical data, graphs, or heat maps using a generation AI and provide them to the railway company.

[0078] The notification unit can provide the user with information about the trains to be withdrawn using push notifications or SMS. The notification unit can provide the user with information about the trains to be withdrawn using push notifications, for example. For example, the notification unit can send a push notification to the user's smartphone to quickly provide the user with information about the trains to be withdrawn. The notification unit can also provide the user with information about the trains to be withdrawn using SMS. For example, the notification unit can send an SMS to the user's mobile phone to provide the user with information about the trains to be withdrawn. In this way, by using push notifications or SMS, the information can be quickly provided to the user. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can generate information about the trains to be withdrawn using a generation AI and provide the information to the user as a push notification or SMS.

[0079] The notification unit can provide an interface or application that allows a user to know that their designated train has been withdrawn. The notification unit, for example, provides an interface that allows a user to know that their designated train has been withdrawn. For example, the notification unit provides an interface that allows a user to check whether a train designated by the user has been withdrawn through a web app or a mobile app. The notification unit can also provide an application that allows a user to know that their designated train has been withdrawn. For example, if a train designated by the user has been withdrawn, the notification unit notifies the user of this information through an application. This makes it easier for the user to know that their designated train has been withdrawn. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can generate information about trains that will be withdrawn using a generation AI and provide the information to the user through an interface or an application.

[0080] The collection unit can estimate the user's emotions and adjust the timing of collecting location information based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of collecting location information to prioritize privacy. For example, if the collection unit estimates the user's emotions and determines that the user is feeling stressed, it sets the frequency of collecting location information to a low level. The collection unit can also increase the frequency of collecting location information to acquire more detailed data if the user is relaxed. For example, if the collection unit estimates the user's emotions and determines that the user is relaxed, it sets the frequency of collecting location information to a high level. The collection unit can also collect location information in real time if the user is in a hurry, enabling a quick response. For example, if the collection unit estimates the user's emotions and determines that the user is in a hurry, it collects location information in real time. This allows for the acquisition of more detailed data while prioritizing privacy by adjusting the timing of collecting location information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to a generation AI, have the generation AI estimate the emotion, and adjust the timing of collecting location information based on the result.

[0081] The collection unit can analyze the user's past movement history and select the optimal collection method. The collection unit, for example, sets location information collection points based on places the user frequently visited in the past. For example, the collection unit analyzes the user's past movement history, identifies frequently visited places, and prioritizes collecting location information at those places. The collection unit can also analyze the user's past movement patterns and propose an efficient collection route. For example, the collection unit analyzes the user's movement patterns and sets an optimal collection route. The collection unit can also concentrate collection during a specific time period based on the user's past movement history. For example, if the user frequently moves during a specific time period, the collection unit concentrates collection of location information during that time period. This enables efficient collection of location information by selecting the optimal collection method based on the past movement 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 the user's past movement history data into a generation AI, which can select the optimal collection method.

[0082] When collecting location information, the collection unit can filter the location information based on the user's current activity status and areas of interest. For example, if the user is shopping, the collection unit prioritizes collecting location information within a commercial facility. For example, the collection unit analyzes the user's current activity status and determines that the user is shopping, and prioritizes collecting location information within the commercial facility. In addition, if the user is sightseeing, the collection unit can also collect location information around tourist spots. For example, the collection unit analyzes the user's current activity status and determines that the user is sightseeing, and collects location information around tourist spots. In addition, the collection unit can also collect location information along the commuting route if the user is commuting. For example, the collection unit analyzes the user's current activity status and determines that the user is commuting, and collects location information along the commuting route. This makes it possible to collect location information according to the user's activity status and areas of interest. Some or all of the above-described processing by 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 activity status data into a generation AI, and the generation AI can perform filtering.

[0083] When collecting location information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects location information using voice recognition technology. For example, the collection unit analyzes the user's voice input and acquires location information. Furthermore, if the user uses text input, the collection unit can also collect location information using text analysis technology. For example, the collection unit analyzes the user's text input and acquires location information. Furthermore, if the user uses image input, the collection unit can also collect location information using image recognition technology. For example, the collection unit analyzes the user's image input and acquires location information. This enables efficient collection of location information by selecting the optimal collection means depending on the user's input method. 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 input data to a generation AI, which can select the optimal collection means.

[0084] The collection unit can estimate the user's emotions and determine the priority of location information to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting location information of safe places. For example, if the collection unit estimates the user's emotions and determines that the user is feeling anxious, it prioritizes collecting location information of safe places. In addition, if the user is excited, the collection unit can prioritize collecting location information of places that the user is likely to be interested in. For example, if the collection unit estimates the user's emotions and determines that the user is excited, it prioritizes collecting location information of places that the user is likely to be interested in. In addition, if the user is tired, the collection unit can prioritize collecting location information of places where the user can take a rest. For example, if the collection unit estimates the user's emotions and determines that the user is tired, it prioritizes collecting location information of places where the user can take a rest. In this way, by prioritizing location information according to the user's emotions, more appropriate information can be collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to a generation AI, have the generation AI estimate the emotion, and determine the priority of location information based on the result.

[0085] When collecting location information, the collection unit can prioritize collecting highly relevant information by taking the user's geographical location information into consideration. For example, if the user is in an urban area, the collection unit prioritizes collecting information about surrounding commercial facilities and traffic. For example, if the collection unit analyzes the user's geographical location information and determines that the user is in an urban area, it prioritizes collecting information about commercial facilities and traffic. Furthermore, if the user is in a suburban area, the collection unit can prioritize collecting information about the natural environment and tourist spots. For example, if the collection unit analyzes the user's geographical location information and determines that the user is in a suburban area, it prioritizes collecting information about the natural environment and tourist spots. Furthermore, if the user is at a specific event venue, the collection unit can prioritize collecting information related to the event. For example, if the collection unit analyzes the user's geographical location information and determines that the user is at a specific event venue, it prioritizes collecting information related to the event. This enables efficient information collection by prioritized collection of highly relevant information based on 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 AI. For example, the collection unit can input the user's geographical location information data into the generation AI, allowing the generation AI to prioritize collection of highly relevant information.

[0086] When collecting location information, the collection unit can analyze the user's social media activity and collect related information. The collection unit, for example, collects location information of places where the user has checked in on social media. For example, the collection unit analyzes the user's social media activity and collects location information of places where the user has checked in. The collection unit can also analyze the content of the user's social media posts and collect location information of related places. For example, the collection unit analyzes the content of the user's posts and collects location information of related places. The collection unit can also collect location information of related places by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the activities of the user's friends and collects location information of related places. This enables more detailed information to be collected by collecting related information based on the user's social media activity. Some or all of the above-described processing by 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 social media activity data into a generation AI, which then collects related information.

[0087] When collecting location information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit adjusts the location information collection method based on, for example, feedback provided by the user in the past. For example, the collection unit analyzes the user's past feedback and customizes the collection method. The collection unit can also prioritize collecting location information of places the user liked in the past. For example, the collection unit analyzes the user's past feedback and prioritizes collecting location information of places the user liked. The collection unit can also prevent collecting location information of places the user avoided in the past. For example, the collection unit analyzes the user's past feedback and prevents collecting location information of avoided places. This customizes the collection method by reflecting the user's past feedback, enabling more appropriate information collection. Some or all of the above-described processing by 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 past feedback data into a generation AI and use the generation AI to customize the collection method.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, if the analysis unit estimates the user's emotions and determines that the user is nervous, it provides the analysis result using a simple, highly visible graph or chart. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit estimates the user's emotions and determines that the user is relaxed, it provides an analysis result including detailed data and explanations. The analysis unit can also provide a concise analysis result if the user is in a hurry. For example, if the analysis unit estimates the user's emotions and determines that the user is in a hurry, it provides a concise analysis result that focuses on the main points. This allows the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into a generation AI, have the generation AI estimate the emotion, and adjust the method of expression of the analysis based on the result.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the location information. For example, the analysis unit performs a detailed analysis on important location information. For example, the analysis unit evaluates the importance of the location information and performs a detailed data analysis on important location information. The analysis unit can also perform a simplified analysis on less important location information. For example, the analysis unit evaluates the importance of the location information and performs a simplified data analysis on less important location information. The analysis unit can also determine the priority of the analysis based on the importance of the location information. For example, the analysis unit evaluates the importance of the location information and prioritizes analysis of important location information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the location information. 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 importance data of the location information to a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the category of location information. For example, the analysis unit applies an algorithm for analyzing purchasing behavior to location information of commercial facilities. For example, the analysis unit analyzes the location information of commercial facilities and applies an algorithm for predicting purchasing behavior. The analysis unit can also apply an algorithm for analyzing tourist behavior to location information of tourist spots. For example, the analysis unit analyzes the location information of tourist spots and applies an algorithm for predicting tourist behavior. The analysis unit can also apply an algorithm for analyzing movement patterns to traffic information. For example, the analysis unit analyzes traffic information and applies an algorithm for predicting movement patterns. This enables more accurate analysis by applying an analysis algorithm depending on the category of location information. 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 category data of the location information to a generation AI, which can then apply an appropriate analysis algorithm.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and optimizes the algorithm parameters. The analysis unit can also correct errors based on the user's past analysis results to improve accuracy. For example, the analysis unit corrects errors based on the past analysis results and improves prediction accuracy. The analysis unit can also optimize the analysis parameters by referring to the user's past analysis results. For example, the analysis unit adjusts parameters based on the past analysis results and provides optimal analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. 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 user's past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the analysis unit estimates the user's emotions and determines that the user is in a hurry, it provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit estimates the user's emotions and determines that the user is relaxed, it provides an analysis result including detailed data and explanations. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the analysis unit estimates the user's emotions and determines that the user is excited, it provides an analysis result using visually stimulating graphs and charts. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into a generation AI, have the generation AI estimate the emotion, and adjust the length of the analysis based on the result.

[0093] During analysis, the analysis unit can determine the priority of analysis based on the time when the location information was collected. The analysis unit, for example, prioritizes analysis of the most recent location information. For example, the analysis unit evaluates the time when the location information was collected and prioritizes analysis of the most recent location information. The analysis unit can also lower the analysis priority of location information collected earlier. For example, the analysis unit evaluates the time when the location information was collected and lowers the analysis priority of older location information. The analysis unit can also adjust the analysis schedule depending on the time when the location information was collected. For example, the analysis unit evaluates the time when the location information was collected and sets a schedule that prioritizes analysis of the most recent location information. This enables efficient analysis by determining the priority of analysis based on the time when the location information 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 data on the time when the location information was collected into a generation AI, and the generation AI can determine the priority of analysis.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the location information. The analysis unit, for example, prioritizes analysis of highly relevant location information. For example, the analysis unit evaluates the relevance of the location information and prioritizes analysis of highly relevant location information. The analysis unit can also postpone the order of analysis of less relevant location information. For example, the analysis unit evaluates the relevance of the location information and postpones the order of analysis of less relevant location information. The analysis unit can also adjust the analysis schedule according to the relevance of the location information. For example, the analysis unit evaluates the relevance of the location information and sets a schedule to prioritize analysis of highly relevant location information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the location information. 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 relevance data of location information to a generation AI, and the generation AI can adjust the order of analysis.

[0095] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. For example, the analysis unit evaluates the user's level of expertise and uses detailed technical terms if it determines that the user has technical expertise. The analysis unit can also explain the analysis results in simple terms if the user does not have technical expertise. For example, the analysis unit evaluates the user's level of expertise and selects an appropriate expression method. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI, and the generation AI can adjust the use of technical terms in the analysis.

[0096] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can prioritize providing reassuring information. For example, if the providing unit estimates the user's emotions and determines that the user is feeling anxious, the providing unit can prioritize providing reassuring information. Furthermore, if the user is excited, the providing unit can prioritize providing information that is likely to be of interest to the user. For example, if the providing unit estimates the user's emotions and determines that the user is excited, the providing unit can prioritize providing information that is likely to be of interest to the user. Furthermore, if the user is tired, the providing unit can prioritize providing information about places where the user can rest. For example, if the providing unit estimates the user's emotions and determines that the user is tired, the providing unit can prioritize providing information about places where the user can rest. In this way, by determining the priority of information to be provided according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to a generating AI, have the generating AI estimate the emotion, and determine the priority of information to be provided based on the result.

[0097] The providing unit can adjust the level of detail of the provided information based on the importance of the prediction result when providing the information. The providing unit, for example, provides detailed information for important prediction results. For example, the providing unit evaluates the importance of the prediction result and provides information including detailed data and explanations for important prediction results. The providing unit can also provide simplified information for prediction results with low importance. For example, the providing unit evaluates the importance of the prediction result and provides simplified data and explanations for prediction results with low importance. The providing unit can also determine the priority of the information provided based on the importance of the prediction result. For example, the providing unit evaluates the importance of the prediction result and provides information preferentially for important prediction results. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the prediction result. 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 can input importance data of the prediction result to the generating AI, and the generating AI can adjust the level of detail of the information provided.

[0098] The providing unit can apply different providing methods depending on the category of the prediction result when providing the information. For example, the providing unit provides traffic information in real time. For example, the providing unit provides traffic information in real time, allowing the user to obtain the latest information. The providing unit can also apply a providing method including detailed explanations to tourist information. For example, the providing unit provides tourist information in a format including detailed data and explanations. The providing unit can also apply a providing method including coupons and special offers to commercial facility information. For example, the providing unit provides information on commercial facilities in a format including coupons and special offers. This enables more appropriate information to be provided by applying a providing method depending on the category of the prediction result. 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 category data of the prediction result to the generation AI, and the generation AI can apply an appropriate providing method.

[0099] The providing unit can improve the accuracy of information provision by referring to the user's past provision results when providing information. The providing unit, for example, adjusts the provision method based on the user's past provision results. For example, the providing unit analyzes the user's past provision results and optimizes the provision method. The providing unit can also correct errors based on the user's past provision results to improve accuracy. For example, the providing unit corrects errors based on the past provision results and improves the accuracy of information provision. The providing unit can also optimize the parameters of provision by referring to the user's past provision results. For example, the providing unit adjusts parameters based on the past provision results and provides optimal information. This improves the accuracy of information provision by referring to the user's past provision results. 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 provision result data into a generation AI, which can improve the accuracy of information provision.

[0100] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. For example, if the providing unit estimates the user's emotions and determines that the user is nervous, it displays information using a simple, highly visible graph or chart. The providing unit can also provide a display method including detailed information if the user is relaxed. For example, if the providing unit estimates the user's emotions and determines that the user is relaxed, it displays information in a format including detailed data and explanations. The providing unit can also provide a display method that focuses on the main points if the user is in a hurry. For example, if the providing unit estimates the user's emotions and determines that the user is in a hurry, it displays information in a concise format that focuses on the main points. This allows for more appropriate information to be provided by adjusting the display method of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to a generating AI, have the generating AI estimate the emotion, and adjust the information display method based on the result.

[0101] The providing unit can determine the priority of provision based on the collection time of the prediction results at the time of provision. The providing unit, for example, prioritizes providing the most recent prediction results. For example, the providing unit evaluates the collection time of the prediction results and prioritizes providing the most recent prediction results. The providing unit can also lower the priority of provision for prediction results that were collected earlier. For example, the providing unit evaluates the collection time of the prediction results and lowers the priority of provision for older prediction results. The providing unit can also adjust the provision schedule depending on the collection time of the prediction results. For example, the providing unit evaluates the collection time of the prediction results and sets a schedule that prioritizes providing the most recent prediction results. This enables efficient information provision by determining the priority of provision based on the collection time of the prediction results. 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 can input data on the collection time of the prediction results to a generation AI, and the generation AI can determine the priority of provision.

[0102] The providing unit can adjust the order of provision based on the relevance of the prediction results when providing them. The providing unit, for example, prioritizes providing highly relevant prediction results. For example, the providing unit evaluates the relevance of the prediction results and prioritizes providing highly relevant prediction results. The providing unit can also postpone the order of provision of less relevant prediction results. For example, the providing unit evaluates the relevance of the prediction results and postpones the order of provision of less relevant prediction results. The providing unit can also adjust the provision schedule according to the relevance of the prediction results. For example, the providing unit evaluates the relevance of the prediction results and sets a schedule to prioritize providing highly relevant prediction results. This enables efficient information provision by adjusting the order of provision based on the relevance of the prediction results. 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 can input relevance data of the prediction results to a generation AI, and the generation AI can adjust the order of provision.

[0103] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. For example, if the user has technical expertise, the providing unit uses detailed technical terminology. For example, the providing unit evaluates the user's level of expertise and determines that the user has technical expertise, and uses detailed technical terminology. The providing unit can also explain the provided results in simple terms if the user does not have technical expertise. For example, the providing unit evaluates the user's level of expertise and determines that the user does not have technical expertise, and explains the provided results in simple terms. The providing unit can also adjust the way the provided results are expressed according to the user's level of expertise. For example, the providing unit evaluates the user's level of expertise and selects an appropriate way of expression. This enables the provision of information that is easier to understand by adjusting the use of technical terminology provided according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into a generation AI, and the generation AI can adjust the use of technical terminology provided.

[0104] The notification unit can estimate the user's emotions and adjust the notification expression method based on the estimated user's emotions. For example, if the user is nervous, the notification unit can provide the notification in a calm tone. For example, if the notification unit estimates the user's emotions and determines that the user is nervous, the notification can be provided in a calm tone. The notification unit can also provide the notification in a bright tone if the user is relaxed. For example, if the notification unit estimates the user's emotions and determines that the user is relaxed, the notification can be provided in a bright tone. The notification unit can also provide a quick and concise notification if the user is in a hurry. For example, if the notification unit estimates the user's emotions and determines that the user is in a hurry, the notification can be provided in a quick and concise notification. This allows for more appropriate notification by adjusting the notification expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's emotional data into the generation AI, have the generation AI estimate the emotion, and adjust the way the notification is expressed based on the results.

[0105] The notification unit can adjust the level of detail of the notification based on the importance of the information at the time of notification. For example, the notification unit provides detailed notification for important information. For example, the notification unit evaluates the importance of the information and provides a notification including detailed data and explanations for important information. The notification unit can also provide a simplified notification for low-importance information. For example, the notification unit evaluates the importance of the information and provides a notification including simplified data and explanations for low-importance information. The notification unit can also determine the priority of notifications according to the importance of the information. For example, the notification unit evaluates the importance of the information and provides a notification of important information with priority. This enables efficient notification by adjusting the level of detail of the notification based on the importance of the information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information importance data to a generation AI, and the generation AI can adjust the level of detail of the notification.

[0106] The notification unit can apply different notification algorithms depending on the category of information when notifying. For example, the notification unit can notify traffic information in real time. For example, the notification unit can notify traffic information in real time, allowing the user to obtain the latest information. The notification unit can also apply a notification method including detailed explanations to tourist information. For example, the notification unit can notify tourist information in a format including detailed data and explanations. The notification unit can also apply a notification method including coupons and special offers to information about commercial facilities. For example, the notification unit can notify information about commercial facilities in a format including coupons and special offers. This allows for more appropriate notifications by applying a notification algorithm depending on the category of information. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input information category data to a generation AI, which can then apply an appropriate notification algorithm.

[0107] The notification unit can improve the accuracy of notifications by referring to the user's past notification results. The notification unit, for example, adjusts the notification method based on the user's past notification results. For example, the notification unit analyzes the user's past notification results and optimizes the notification method. The notification unit can also correct errors based on the user's past notification results to improve accuracy. For example, the notification unit corrects errors based on the past notification results and improves notification accuracy. The notification unit can also optimize notification parameters by referring to the user's past notification results. For example, the notification unit adjusts parameters based on the past notification results and provides optimal notifications. This improves notification accuracy by referring to the user's past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification result data into a generation AI, which can improve notification accuracy.

[0108] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated user's emotions. For example, if the user is in a hurry, the notification unit provides a short and to-the-point notification. For example, if the notification unit estimates the user's emotions and determines that the user is in a hurry, the notification unit provides a short and to-the-point notification. The notification unit can also provide a notification including a detailed explanation if the user is relaxed. For example, if the notification unit estimates the user's emotions and determines that the user is relaxed, the notification unit provides a notification including detailed data and explanation. The notification unit can also provide a visually stimulating notification if the user is excited. For example, if the notification unit estimates the user's emotions and determines that the user is excited, the notification unit provides a visually stimulating notification. This allows for more appropriate notifications by adjusting the length of the notification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input user emotion data into a generation AI, have the generation AI estimate the emotion, and adjust the length of the notification based on the result.

[0109] The notification unit can determine the priority of notifications based on the time when the information was collected at the time of notification. The notification unit, for example, prioritizes notifying the latest information. For example, the notification unit evaluates the time when the information was collected and prioritizes notifying the latest information. The notification unit can also lower the priority of notifications for information that was collected earlier. For example, the notification unit evaluates the time when the information was collected and lowers the priority of notifications for older information. The notification unit can also adjust the notification schedule according to the time when the information was collected. For example, the notification unit evaluates the time when the information was collected and sets a schedule that prioritizes notifying the latest information. This enables efficient notifications by determining the priority of notifications based on the time when the information was collected. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information collection time data into a generation AI, and the generation AI can determine the priority of notifications.

[0110] The notification unit can adjust the order of notifications based on the relevance of the information at the time of notification. The notification unit, for example, prioritizes notifying highly relevant information. For example, the notification unit evaluates the relevance of the information and prioritizes notifying highly relevant information. The notification unit can also postpone the order of notifications for less relevant information. For example, the notification unit evaluates the relevance of the information and postpones the order of notifications for less relevant information. The notification unit can also adjust the notification schedule according to the relevance of the information. For example, the notification unit evaluates the relevance of the information and sets a schedule to prioritize notifying highly relevant information. This enables efficient notifications by adjusting the order of notifications based on the relevance of the information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information relevance data to a generation AI, and the generation AI can adjust the order of notifications.

[0111] The notification unit can adjust the use of technical terms in the notification according to the user's level of expertise when providing a notification. For example, if the user has technical expertise, the notification unit uses detailed technical terms. For example, the notification unit evaluates the user's level of expertise and determines that the user has technical expertise, and uses detailed technical terms. The notification unit can also explain the notification result in simple terms if the user does not have technical expertise. For example, the notification unit evaluates the user's level of expertise and determines that the user does not have technical expertise, and explains the notification result in simple terms. The notification unit can also adjust the way the notification result is expressed according to the user's level of expertise. For example, the notification unit evaluates the user's level of expertise and selects an appropriate way of expression. This allows for a more easily understandable notification by adjusting the use of technical terms in the notification according to the user's level of expertise. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's level of expertise data into a generation AI, and the generation AI can adjust the use of technical terms in the notification.

[0112] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and optimizes the algorithm parameters. The analysis unit can also correct errors based on the user's past analysis results to improve accuracy. For example, the analysis unit corrects errors based on the past analysis results and improves prediction accuracy. The analysis unit can also optimize the analysis parameters by referring to the user's past analysis results. For example, the analysis unit adjusts parameters based on the past analysis results and provides optimal analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. 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 user's past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0113] The providing unit can determine the priority of provision based on the collection time of the prediction results at the time of provision. The providing unit, for example, prioritizes providing the most recent prediction results. For example, the providing unit evaluates the collection time of the prediction results and prioritizes providing the most recent prediction results. The providing unit can also lower the priority of provision for prediction results that were collected earlier. For example, the providing unit evaluates the collection time of the prediction results and lowers the priority of provision for older prediction results. The providing unit can also adjust the provision schedule depending on the collection time of the prediction results. For example, the providing unit evaluates the collection time of the prediction results and sets a schedule that prioritizes providing the most recent prediction results. This enables efficient information provision by determining the priority of provision based on the collection time of the prediction results. 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 can input data on the collection time of the prediction results to a generation AI, and the generation AI can determine the priority of provision.

[0114] The notification unit can determine the priority of notifications based on the time when the information was collected at the time of notification. The notification unit, for example, prioritizes notifying the latest information. For example, the notification unit evaluates the time when the information was collected and prioritizes notifying the latest information. The notification unit can also lower the priority of notifications for information that was collected earlier. For example, the notification unit evaluates the time when the information was collected and lowers the priority of notifications for older information. The notification unit can also adjust the notification schedule according to the time when the information was collected. For example, the notification unit evaluates the time when the information was collected and sets a schedule that prioritizes notifying the latest information. This enables efficient notifications by determining the priority of notifications based on the time when the information was collected. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information collection time data into a generation AI, and the generation AI can determine the priority of notifications.

[0115] The notification unit can adjust the order of notifications based on the relevance of the information at the time of notification. The notification unit, for example, prioritizes notifying highly relevant information. For example, the notification unit evaluates the relevance of the information and prioritizes notifying highly relevant information. The notification unit can also postpone the order of notifications for less relevant information. For example, the notification unit evaluates the relevance of the information and postpones the order of notifications for less relevant information. The notification unit can also adjust the notification schedule according to the relevance of the information. For example, the notification unit evaluates the relevance of the information and sets a schedule to prioritize notifying highly relevant information. This enables efficient notifications by adjusting the order of notifications based on the relevance of the information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information relevance data to a generation AI, and the generation AI can adjust the order of notifications.

[0116] The notification unit can adjust the use of technical terms in the notification according to the user's level of expertise when providing a notification. For example, if the user has technical expertise, the notification unit uses detailed technical terms. For example, the notification unit evaluates the user's level of expertise and determines that the user has technical expertise, and uses detailed technical terms. The notification unit can also explain the notification result in simple terms if the user does not have technical expertise. For example, the notification unit evaluates the user's level of expertise and determines that the user does not have technical expertise, and explains the notification result in simple terms. The notification unit can also adjust the way the notification result is expressed according to the user's level of expertise. For example, the notification unit evaluates the user's level of expertise and selects an appropriate way of expression. This allows for a more easily understandable notification by adjusting the use of technical terms in the notification according to the user's level of expertise. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's level of expertise data into a generation AI, and the generation AI can adjust the use of technical terms in the notification. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and notification unit, described above, 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 location information using GPS data or Wi-Fi location information from the smart device 14, and processes the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the location information using a generation AI and predicts the distribution of returning homebound passengers, etc. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides the prediction results to the railway company. The notification unit, realized, for example, by the control unit 46A of the smart device 14, notifies the user of information about trains that will be thinned out. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and notification unit, described above, 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 location information using GPS data or Wi-Fi location information from the smart glasses 214, and processes the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the location information using a generation AI and predicts the distribution of returning homebound passengers, etc. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides the prediction results to the railway company. The notification unit, realized, for example, by the control unit 46A of the smart glasses 214, notifies the user of information about trains that will be thinned out. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and notification unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects location information using GPS data or Wi-Fi location information from the headset-type terminal 314, and processes the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the location information using a generation AI, and predicts the distribution of returning homebound passengers and the like. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the prediction results to the railway company. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and notifies the user of information about trains that will be thinned out. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects location information using GPS data or Wi-Fi location information of the robot 414, and processes the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the location information using a generation AI, and predicts the distribution of returning homebound passengers, etc. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the prediction results to the railway company. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies the user of information about trains that will be thinned out.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The prediction system may further include a history analysis unit that analyzes the user's past travel history. The history analysis unit analyzes the places and travel patterns that the user has frequently visited in the past and improves the accuracy of the prediction based on this. For example, if the user has frequently used a specific station in the past, the history analysis unit can more accurately predict congestion at that station. The history analysis unit can also predict congestion during a specific time period based on the user's past travel patterns. Furthermore, the history analysis unit can customize the prediction results based on the user's past travel history and provide optimal information for each individual user. This allows the prediction system to make more accurate predictions by utilizing the user's past travel history.

[0119] The collection unit may include an activity analysis unit that analyzes the user's current activity status. The activity analysis unit adjusts the method of collecting location information based on the user's current activity. For example, if the user is shopping, the collection unit may prioritize collecting location information within a commercial facility. If the user is sightseeing, the collection unit may also collect location information around tourist spots. Furthermore, if the user is commuting, the collection unit may also collect location information along the commuting route. This allows the collection unit to collect location information according to the user's activity status.

[0120] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide analysis results that focus on the main points. This allows the analysis unit to provide analysis results that correspond to the user's emotions.

[0121] The providing unit can adjust the level of detail to be provided based on the importance of the prediction result. For example, detailed information can be provided for important prediction results. Simplified information can be provided for less important prediction results. The providing unit can also determine the priority of provision according to the importance of the prediction results. This allows the providing unit to provide information efficiently based on the importance of the prediction results.

[0122] The notification unit can estimate the user's emotions and adjust the way the notification is expressed based on the estimated user's emotions. For example, if the user is nervous, the notification can be given in a calm tone. If the user is relaxed, the notification can be given in a bright tone. If the user is in a hurry, the notification can be given in a quick and concise tone. This allows the notification unit to provide an appropriate notification according to the user's emotions.

[0123] The collection unit may include a social analysis unit that analyzes the user's social media activities. The social analysis unit analyzes the places where the user has checked in on social media and the content of posts, and collects related location information. For example, the social analysis unit may collect location information of places where the user has checked in on social media. It may also analyze the content of posts by the user and collect location information of related places. This allows the collection unit to collect location information based on the user's social media activities.

[0124] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis algorithm can be adjusted based on the user's past analysis results. The accuracy can also be improved by correcting errors from the user's past analysis results. Furthermore, the analysis parameters can be optimized by referring to the user's past analysis results. This allows the analysis unit to utilize the user's past analysis results to perform more accurate analysis.

[0125] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is feeling anxious, reassuring information can be provided preferentially. If the user is excited, information that is likely to interest the user can be provided preferentially. Furthermore, if the user is tired, information about places where the user can take a rest can be provided preferentially. In this way, the providing unit can provide appropriate information according to the user's emotions.

[0126] The notification unit can determine the priority of notifications based on the time when the information was collected. For example, the latest information can be given priority in notification. The notification priority of information collected earlier can also be lowered. The notification schedule can also be adjusted depending on the time when the information was collected. This allows the notification unit to provide efficient notifications based on the time when the information was collected.

[0127] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated user's emotions. For example, if the user is in a hurry, a short and to-the-point notification can be provided. If the user is relaxed, a detailed explanation can be provided. If the user is excited, a visually stimulating notification can be provided. This allows the notification unit to provide appropriate notifications according to the user's emotions.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The collection unit collects location information from mobile phones and smartphones. The collection unit can collect, for example, GPS data, Wi-Fi location information, cell tower data, etc. The collection unit can also collect location information in real time. For example, the collection unit periodically updates the user's location information to obtain the latest data. Step 2: The analysis unit uses the generation AI to analyze the location information collected by the collection unit and predict the distribution of returning home visitors, etc. The analysis unit analyzes the location information using, for example, a machine learning algorithm. For example, the analysis unit predicts the distribution of returning home visitors using algorithms such as K-means clustering or random forest. The analysis unit can also use the generation AI to analyze patterns in the location information and identify stations where congestion is predicted. Step 3: The providing unit provides the prediction results obtained by the analysis unit to the railway company. The providing unit can provide the prediction results in the form of numerical data, graphs, heat maps, etc. The providing unit can also provide the prediction results to the railway company in real time and use them to design a timetable for reduced service. Step 4: The notification unit notifies the user of the information about the trains that will be dropped based on the information provided by the provision unit. The notification unit provides the user with the information about the trains that will be dropped, for example, by using push notification or SMS. The notification unit can also provide an interface or application that allows the user to know that their specified train has been dropped. For example, when a train specified by the user has been dropped, the notification unit notifies the user of that information via a mobile phone or smartphone.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Explanation of symbols]

[0202] 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 location information from mobile phones and smartphones; an analysis unit that analyzes the location information collected by the collection unit and predicts the distribution state of returning visitors; a providing unit that provides a railway company with the prediction result obtained by the analysis unit; a notification unit that notifies a user of information about trains to be thinned out based on the information provided by the provision unit. A system characterized by:

2. The collecting unit Collecting real-time location information from mobile phones and smartphones 2. The system of claim 1.

3. The analysis unit Using machine learning algorithms to analyze location information and predict the distribution of people returning home, etc.

2. The system of claim 1.

4. The providing unit The prediction results will be provided to railway companies and used to design reduced service schedules.

2. The system of claim 1.

5. The notification unit Use push notifications or SMS to inform users of upcoming trains 2. The system of claim 1.

6. The notification unit Provide an interface or application that allows users to know when their designated train has been withdrawn 2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of location information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past movement history and select the optimal collection method 2. The system of claim 1.

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