system

Abstract

The system according to the embodiment aims to provide appropriate suggestions to users based on delays and congestion levels of public transport. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a presentation unit. The collection unit collects delay information or congestion information for transportation facilities. The analysis unit analyzes the information collected by the collection unit to analyze the delay time or congestion level. The presentation unit presents suggestions generated by the analysis unit to a user.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately collect information on delays and congestion in transportation in real time and provide appropriate suggestions to users, so there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate suggestions to users based on delays and congestion levels of public transport. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a presentation unit. The collection unit collects delay information or congestion information for transportation facilities. The analysis unit analyzes the information collected by the collection unit to analyze the delay time or congestion level. The presentation unit presents suggestions generated by the analysis unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate suggestions to the user based on the delays and congestion of public transport. [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) In an embodiment of the present invention, a telecommuting recommendation system uses a generation AI to recommend telecommuting and present the recommendation to a user when a delay exceeds a certain time or congestion exceeds a specific level. This telecommuting recommendation system collects information about public transport delays and congestion levels, analyzes the collected information, generates a recommendation to telecommute when a delay exceeds a certain time or congestion exceeds a specific level, and presents the generated recommendation to the user. For example, the telecommuting recommendation system includes a collection unit that collects information about public transport delays and congestion levels. The collection unit uses sensors and data sources that acquire real-time information about public transport operation and congestion levels. Next, an analysis unit is provided that analyzes the information collected by the collection unit. The analysis unit uses a generation AI to analyze the delay time and congestion levels and generate a recommendation to telecommute when a threshold is exceeded. Finally, a presentation unit is provided that presents the generated recommendation to the user. The presentation unit notifies the user via a smartphone app or email. This system allows users to reduce stress caused by public transport delays and congestion and work more efficiently. In addition, by utilizing the user's past behavioral data and customizing the content of the suggestions, the telecommuting recommendation system can provide more personalized suggestions, helping users reduce stress caused by delays and congestion on public transport and enabling them to work more efficiently.

[0029] A telecommuting recommendation system according to an embodiment includes a collection unit, an analysis unit, and a presentation unit. The collection unit collects delay information or congestion information for public transportation. The collection unit uses, for example, sensors or data sources that acquire public transportation operation information and congestion levels in real time. For example, the collection unit installs sensors that monitor the operation status of public transportation to acquire public transportation operation information in real time. The collection unit can also use cameras or sensors installed at stations, bus stops, etc. to acquire congestion levels in real time. The analysis unit analyzes the information collected by the collection unit and analyzes the delay time or congestion level. The analysis unit analyzes the delay time or congestion level using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes the delay time and congestion level based on the collected information. The analysis unit generates a suggestion recommending telecommuting, for example, when the delay time exceeds a certain time or the congestion level exceeds a specific level. The presentation unit presents the suggestion generated by the analysis unit to a user. The presentation unit notifies the user, for example, via a smartphone app or email. For example, the presentation unit displays a pop-up notification within the smartphone app to notify the user. The presentation unit can also send the proposal to the user's email address to notify the user via email. This allows the telecommuting recommendation system according to the embodiment to reduce stress caused by transportation delays and congestion and enable users to work efficiently. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input information collected by the collection unit into the generation AI and cause the generation AI to analyze delay times and congestion levels. Some or all of the above-described processing in the presentation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the presentation unit may cause the generation AI to generate proposal content in order to present the proposal generated by the generation AI to the user.

[0030] The collection unit can acquire transportation operation information or congestion levels in real time. For example, the collection unit installs sensors that monitor the operation status of transportation facilities to acquire transportation operation information in real time. For example, the collection unit can use sensors installed at stations to monitor train operation status. The collection unit can also use sensors installed at bus stops to monitor bus operation status. Furthermore, the collection unit can use cameras or sensors installed at stations, bus stops, etc. to acquire congestion levels in real time. For example, the collection unit can use cameras installed at stations to monitor station congestion levels. The collection unit can also use cameras installed at bus stops to monitor bus stop congestion levels. This enables proposals based on the latest information by acquiring transportation operation information and congestion levels in real time. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input transportation operation information and congestion level information to a generation AI and cause the generation AI to collect information.

[0031] The analysis unit can generate a suggestion recommending telecommuting when a delay time or congestion level exceeds a threshold. The analysis unit can generate a suggestion recommending telecommuting when a delay time exceeds a certain time. For example, the analysis unit can generate a suggestion recommending telecommuting when a delay time exceeds 30 minutes. The analysis unit can also generate a suggestion recommending telecommuting when congestion level exceeds a specific level. For example, the analysis unit can generate a suggestion recommending telecommuting when congestion level exceeds 80%. This allows the user to make an appropriate decision by generating a suggestion recommending telecommuting when a delay time or congestion level exceeds a threshold. Some or all of the above-described processing by the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input information about the delay time and congestion level into the generation AI and cause the generation AI to generate a suggestion.

[0032] The presentation unit can notify the user via a smartphone app or email. For example, the presentation unit can display a pop-up notification within the smartphone app to notify the user via the app. For example, the presentation unit can display a suggestion recommending telecommuting as a pop-up notification within the app. The presentation unit can also send the suggestion to the user's email address to notify the user via email. For example, the presentation unit can send the suggestion recommending telecommuting to the user's email address. This allows the user to quickly receive the suggestion by notifying the user via the smartphone app or email. Some or all of the above-described processing by the presentation unit can be performed using, or without, a generation AI. For example, the presentation unit can cause the generation AI to generate notification content to notify the user of the suggestion generated by the generation AI.

[0033] The analysis unit can customize the content of the suggestions by utilizing the user's past behavioral data. The analysis unit can customize the content of the suggestions by utilizing, for example, the user's past commuting history. For example, the analysis unit can customize the content of the suggestions based on delay information and congestion information for public transportation used by the user in the past. The analysis unit can also customize the content of the suggestions by utilizing the user's past choices. For example, the analysis unit can customize the content of the suggestions based on the user's history of selecting telecommuting in the past. This makes it possible to provide more personalized suggestions by utilizing the user's past behavioral data. Some or all of the above-described processing by the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past behavioral data into the generation AI and have the generation AI customize the suggestions.

[0034] When collecting transportation delay information or congestion information, the collection unit can select an optimal collection method based on the user's past commuting patterns. For example, the collection unit prioritizes collecting delay information for transportation modes used by the user in the past. For example, the collection unit can prioritize collecting delay information for trains used by the user in the past. The collection unit can also collect the most relevant information based on the user's commuting time zone. For example, the collection unit can prioritize collecting delay information for morning rush hour based on the user's commuting time zone. Furthermore, the collection unit can analyze the user's past commuting patterns and determine the optimal collection timing. For example, the collection unit can determine the optimal collection timing based on the user's past commuting patterns. This allows more relevant information to be collected by taking the user's past commuting patterns into consideration. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past commuting patterns into the generation AI and cause the generation AI to select the optimal collection method.

[0035] When collecting transportation delay information or congestion information, the collection unit can improve the accuracy of collection based on specific events and weather conditions. For example, when a large-scale event is held, the collection unit can focus on collecting delay information on transportation in the surrounding area. For example, when a sporting event or concert is held, the collection unit can focus on collecting delay information on transportation in the surrounding area. The collection unit can also collect detailed information on the operation status of transportation in bad weather. For example, the collection unit can collect detailed information on the operation status of transportation in bad weather such as rain, snow, or typhoons. Furthermore, the collection unit can collect congestion information during specific seasons and holidays to improve accuracy. For example, the collection unit can collect congestion information during specific seasons and holidays such as the New Year holidays and Golden Week to improve accuracy. This improves the accuracy of collection by taking specific events and weather conditions into consideration. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input information about specific events or weather conditions into the generation AI, allowing the generation AI to improve the accuracy of collection.

[0036] When collecting delay information or congestion information for transportation facilities, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. The collection unit, for example, prioritizes collecting delay information for transportation facilities closest to the user's current location. For example, the collection unit can prioritize collecting delay information for stations closest to the user's current location. The collection unit can also prioritize collecting congestion information for transportation facilities on the user's commute route. For example, the collection unit can prioritize collecting congestion information for trains and buses on the user's commute route. Furthermore, the collection unit can also prioritize collecting information for transportation facilities related to the user's destination. For example, the collection unit can prioritize collecting delay information and congestion information for trains and buses related to the user's destination. This allows more relevant information to be collected by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0037] When collecting transportation delay information and congestion information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit prioritizes collecting information about transportation mentioned by the user on social media. For example, the collection unit can prioritize collecting train and bus delay information mentioned by the user on social media. The collection unit can also collect information about transportation of interest from the user's social media posts. For example, the collection unit can collect congestion information for specific lines and stations from the user's social media posts. Furthermore, the collection unit can also collect information about transportation shared by the user's followers and friends. For example, the collection unit can collect train and bus delay information and congestion information shared by the user's followers and friends. This allows for more relevant information to be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit may input data about the user's social media activities into the generation AI and cause the generation AI to collect related information.

[0038] The analysis unit can optimize the analysis algorithm by referring to past data when analyzing delay times and congestion levels. The analysis unit, for example, predicts a current delay time based on past delay data. For example, the analysis unit can predict a current delay time based on past train delay data. The analysis unit can also analyze a current congestion level by referring to past congestion data. For example, the analysis unit can analyze a current congestion level by referring to past station congestion data. Furthermore, the analysis unit can improve the accuracy of the analysis algorithm using past data. For example, the analysis unit can improve the accuracy of the analysis algorithm by using past delay data and congestion data. Thus, by referring to past data, the accuracy of the analysis algorithm is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0039] The analysis unit can apply different analysis methods to specific transportation modes or routes when analyzing delay times or congestion levels. For example, the analysis unit can analyze the delay patterns of a specific route and apply the optimal method. For example, the analysis unit can analyze the delay patterns of a specific railway route and apply the optimal method. The analysis unit can also use different congestion analysis methods for each transportation mode. For example, the analysis unit can use different congestion analysis methods for each transportation mode, such as trains and buses. Furthermore, the analysis unit can customize the analysis method taking into account the characteristics of each route. For example, the analysis unit can customize the analysis method taking into account the characteristics of a specific railway route or bus route. This improves the accuracy of the analysis by applying different analysis methods to specific transportation modes or routes. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data for a specific transportation mode or route into the generation AI and cause the generation AI to apply the analysis method.

[0040] When analyzing delay times and congestion levels, the analysis unit can determine analysis priorities based on the user's commute time zone. For example, the analysis unit prioritizes analysis of information most relevant to the user's commute time zone. For example, the analysis unit can prioritize analysis of morning rush hour delay information based on the user's commute time zone. The analysis unit can also prioritize analysis of delay times based on the user's commute time zone. For example, the analysis unit can prioritize analysis of train delay times based on the user's commute time zone. Furthermore, the analysis unit can also prioritize analysis of congestion levels based on the user's commute time zone. For example, the analysis unit can prioritize analysis of station congestion levels based on the user's commute time zone. This allows for more relevant information to be provided by determining analysis priorities based on the user's commute time zone. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data about the user's commute time zone into the generation AI and have the generation AI determine the analysis priorities.

[0041] The analysis unit can improve the accuracy of the analysis by referring to related news and event information when analyzing delay times and congestion levels. The analysis unit can, for example, refer to news related to transportation to improve the accuracy of the delay time analysis. For example, the analysis unit can refer to news about transportation strikes or accidents to improve the accuracy of the delay time analysis. The analysis unit can also improve the accuracy of the congestion level analysis by referring to information about large-scale events. For example, the analysis unit can improve the accuracy of the congestion level analysis by referring to information about sporting events or concerts. Furthermore, the analysis unit can adjust the analysis algorithm based on the news and event information. For example, the analysis unit can adjust the analysis algorithm based on news and event information related to transportation. As a result, the accuracy of the analysis is improved by referring to related news and event information. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input news and event information into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] When presenting a proposal, the presentation unit can select an optimal presentation method by referring to the user's past behavioral data. For example, the presentation unit can prioritize a proposal format that the user has previously preferred. For example, the presentation unit can prioritize a notification format that the user has previously preferred. The presentation unit can also present a proposal at an optimal timing based on the user's past behavioral data. For example, the presentation unit can present a telecommuting proposal at an optimal timing based on the user's past behavioral data. Furthermore, the presentation unit can also make highly relevant proposals by referring to the user's past selection history. For example, the presentation unit can make highly relevant telecommuting proposals by referring to the user's past selection history. In this way, by referring to the user's past behavioral data, more relevant proposals can be provided. Some or all of the above-described processing by the presentation unit can be performed using, or without, a generation AI. For example, the presentation unit can input the user's past behavioral data into the generation AI and cause the generation AI to select an optimal presentation method.

[0043] When presenting a proposal, the presentation unit can select an optimal display format based on the user's device information. For example, if the user is using a smartphone, the presentation unit provides a display format tailored to the screen size. For example, if the user is using a smartphone, the presentation unit can display the telecommuting proposal using text and icons tailored to the screen size. Furthermore, if the user is using a tablet, the presentation unit can provide a display format optimized for a large screen. For example, if the user is using a tablet, the presentation unit can display the telecommuting proposal using graphs and text optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can provide a concise and highly visible display format. For example, if the user is using a smartwatch, the presentation unit can display the telecommuting proposal using concise and highly visible icons and text. This allows the proposal to be provided in a more appropriate display format by taking the user's device information into consideration. Some or all of the above-described processing by the presentation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the presentation unit may input the user's device information into the generation AI and cause the generation AI to select an optimal display format.

[0044] When presenting a proposal, the presentation unit can provide an optimal proposal based on the user's geographical location information. The presentation unit, for example, makes a proposal for telecommuting closest to the user's current location. For example, the presentation unit can prioritize displaying a proposal for telecommuting closest to the user's current location. The presentation unit can also make an optimal proposal based on information on the user's commute route. For example, the presentation unit can make an optimal telecommuting proposal based on delay information and congestion information for public transportation on the user's commute route. Furthermore, the presentation unit can also make a proposal related to the user's destination. For example, the presentation unit can make an optimal telecommuting proposal based on delay information and congestion information for public transportation related to the user's destination. This allows for more relevant proposals to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing by the presentation unit may be performed using, or without, a generation AI. For example, the presentation unit can input the user's geographical location information to the generation AI and cause the generation AI to provide the optimal proposal.

[0045] When presenting suggestions, the presentation unit can analyze the user's social media activity and provide relevant suggestions. The presentation unit can make suggestions based on, for example, content mentioned by the user on social media. For example, the presentation unit can suggest telecommuting based on delay information and congestion information for public transportation mentioned by the user on social media. The presentation unit can also make suggestions of interest based on the user's social media posts. For example, the presentation unit can suggest telecommuting based on information about specific lines and stations from the user's social media posts. Furthermore, the presentation unit can make suggestions based on information shared by the user's followers and friends. For example, the presentation unit can suggest telecommuting based on delay information and congestion information for public transportation shared by the user's followers and friends. This allows for more relevant suggestions to be provided by analyzing the user's social media activity. Some or all of the above-described processing by the presentation unit can be performed using, or without, a generation AI. For example, the presentation unit can input data about the user's social media activity into the generation AI and cause the generation AI to provide relevant suggestions.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The collection unit can adjust the frequency of collection of transportation delay information and congestion information based on the user's past commuting patterns. For example, it can prioritize collection of information on routes where the user has frequently experienced delays in the past. It can also prioritize collection of information for specific time periods based on the user's commuting time. Furthermore, it can prioritize collection of information on specific transportation modes based on the user's past selection history. This allows more relevant information to be collected by taking the user's past commuting patterns into consideration.

[0048] The presentation unit can adjust the display format of the suggestions based on the user's device information. For example, if the user is using a smartphone, a simple display format tailored to the screen size can be provided. If the user is using a tablet, a detailed display format optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display format can be provided. This allows suggestions to be provided in a more appropriate display format by taking the user's device information into consideration.

[0049] When collecting transportation delay information and congestion information, the collection unit can improve the accuracy of collection based on specific events and weather conditions. For example, when a large-scale event is held, it can focus on collecting delay information for transportation in the surrounding area. It can also collect detailed information on the operation status of transportation during bad weather. It can also collect congestion information during specific seasons and holidays to improve accuracy. This improves the accuracy of collection by taking specific events and weather conditions into consideration.

[0050] When collecting delay information and congestion information for transportation facilities, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, it can prioritize collecting delay information for transportation facilities closest to the user's current location. It can also prioritize collecting congestion information for transportation facilities on the user's commute route. It can also prioritize collecting information for transportation facilities related to the user's destination. In this way, more relevant information can be collected by taking the user's geographical location information into consideration.

[0051] When presenting suggestions, the suggestion unit can analyze the user's social media activity to provide relevant suggestions. For example, suggestions can be made based on what the user has mentioned on social media. Also, suggestions that are of interest to the user can be made based on the content of the user's social media posts. Furthermore, suggestions can be made based on information shared by the user's followers and friends. Thus, by analyzing the user's social media activity, more relevant suggestions can be provided.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The collection unit collects information on delays or congestion levels of transportation. The collection unit uses sensors and data sources that obtain transportation operation information and congestion levels in real time. For example, sensors that monitor the operation status of transportation systems, and cameras and sensors installed at stations and bus stops, etc., are used. Step 2: The analysis unit analyzes the information collected by the collection unit and analyzes delay times or congestion levels. The analysis unit can analyze delay times and congestion levels using generation AI. For example, if the delay time exceeds a certain time or the congestion level exceeds a specific level, it generates a suggestion recommending telecommuting. Step 3: The presentation unit presents the suggestions generated by the analysis unit to the user. The presentation unit notifies the user via a smartphone app or email. For example, the presentation unit can display a pop-up notification within the app or send the suggestions to the user's email address.

[0054] (Example 2) In an embodiment of the present invention, a telecommuting recommendation system uses a generation AI to recommend telecommuting and present the recommendation to a user when a delay exceeds a certain time or congestion exceeds a specific level. This telecommuting recommendation system collects information about public transport delays and congestion levels, analyzes the collected information, generates a recommendation to telecommute when a delay exceeds a certain time or congestion exceeds a specific level, and presents the generated recommendation to the user. For example, the telecommuting recommendation system includes a collection unit that collects information about public transport delays and congestion levels. The collection unit uses sensors and data sources that acquire real-time information about public transport operation and congestion levels. Next, an analysis unit is provided that analyzes the information collected by the collection unit. The analysis unit uses a generation AI to analyze the delay time and congestion levels and generate a recommendation to telecommute when a threshold is exceeded. Finally, a presentation unit is provided that presents the generated recommendation to the user. The presentation unit notifies the user via a smartphone app or email. This system allows users to reduce stress caused by public transport delays and congestion and work more efficiently. In addition, by utilizing the user's past behavioral data and customizing the content of the suggestions, the telecommuting recommendation system can provide more personalized suggestions, helping users reduce stress caused by delays and congestion on public transport and enabling them to work more efficiently.

[0055] A telecommuting recommendation system according to an embodiment includes a collection unit, an analysis unit, and a presentation unit. The collection unit collects delay information or congestion information for public transportation. The collection unit uses, for example, sensors or data sources that acquire public transportation operation information and congestion levels in real time. For example, the collection unit installs sensors that monitor the operation status of public transportation to acquire public transportation operation information in real time. The collection unit can also use cameras or sensors installed at stations, bus stops, etc. to acquire congestion levels in real time. The analysis unit analyzes the information collected by the collection unit and analyzes the delay time or congestion level. The analysis unit analyzes the delay time or congestion level using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes the delay time and congestion level based on the collected information. The analysis unit generates a suggestion recommending telecommuting, for example, when the delay time exceeds a certain time or the congestion level exceeds a specific level. The presentation unit presents the suggestion generated by the analysis unit to a user. The presentation unit notifies the user, for example, via a smartphone app or email. For example, the presentation unit displays a pop-up notification within the smartphone app to notify the user. The presentation unit can also send the proposal to the user's email address to notify the user via email. This allows the telecommuting recommendation system according to the embodiment to reduce stress caused by transportation delays and congestion and enable users to work efficiently. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input information collected by the collection unit into the generation AI and cause the generation AI to analyze delay times and congestion levels. Some or all of the above-described processing in the presentation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the presentation unit may cause the generation AI to generate proposal content in order to present the proposal generated by the generation AI to the user.

[0056] The collection unit can acquire transportation operation information or congestion levels in real time. For example, the collection unit installs sensors that monitor the operation status of transportation facilities to acquire transportation operation information in real time. For example, the collection unit can use sensors installed at stations to monitor train operation status. The collection unit can also use sensors installed at bus stops to monitor bus operation status. Furthermore, the collection unit can use cameras or sensors installed at stations, bus stops, etc. to acquire congestion levels in real time. For example, the collection unit can use cameras installed at stations to monitor station congestion levels. The collection unit can also use cameras installed at bus stops to monitor bus stop congestion levels. This enables proposals based on the latest information by acquiring transportation operation information and congestion levels in real time. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input transportation operation information and congestion level information to a generation AI and cause the generation AI to collect information.

[0057] The analysis unit can generate a suggestion recommending telecommuting when a delay time or congestion level exceeds a threshold. The analysis unit can generate a suggestion recommending telecommuting when a delay time exceeds a certain time. For example, the analysis unit can generate a suggestion recommending telecommuting when a delay time exceeds 30 minutes. The analysis unit can also generate a suggestion recommending telecommuting when congestion level exceeds a specific level. For example, the analysis unit can generate a suggestion recommending telecommuting when congestion level exceeds 80%. This allows the user to make an appropriate decision by generating a suggestion recommending telecommuting when a delay time or congestion level exceeds a threshold. Some or all of the above-described processing by the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input information about the delay time and congestion level into the generation AI and cause the generation AI to generate a suggestion.

[0058] The presentation unit can notify the user via a smartphone app or email. For example, the presentation unit can display a pop-up notification within the smartphone app to notify the user via the app. For example, the presentation unit can display a suggestion recommending telecommuting as a pop-up notification within the app. The presentation unit can also send the suggestion to the user's email address to notify the user via email. For example, the presentation unit can send the suggestion recommending telecommuting to the user's email address. This allows the user to quickly receive the suggestion by notifying the user via the smartphone app or email. Some or all of the above-described processing by the presentation unit can be performed using, or without, a generation AI. For example, the presentation unit can cause the generation AI to generate notification content to notify the user of the suggestion generated by the generation AI.

[0059] The analysis unit can customize the content of the suggestions by utilizing the user's past behavioral data. The analysis unit can customize the content of the suggestions by utilizing, for example, the user's past commuting history. For example, the analysis unit can customize the content of the suggestions based on delay information and congestion information for public transportation used by the user in the past. The analysis unit can also customize the content of the suggestions by utilizing the user's past choices. For example, the analysis unit can customize the content of the suggestions based on the user's history of selecting telecommuting in the past. This makes it possible to provide more personalized suggestions by utilizing the user's past behavioral data. Some or all of the above-described processing by the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past behavioral data into the generation AI and have the generation AI customize the suggestions.

[0060] The collection unit can estimate the user's emotions and adjust the timing of collecting transportation delay information and congestion information based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can frequently collect transportation delay information and congestion information. For example, when the user is feeling stressed, the collection unit can collect transportation delay information and congestion information every five minutes. Furthermore, when the user is relaxed, the collection unit can reduce the collection frequency and collect only necessary information. For example, when the user is relaxed, the collection unit can collect transportation delay information and congestion information every 30 minutes. Furthermore, when the user is in a hurry, the collection unit can collect information in real time and provide it immediately. For example, when the user is in a hurry, the collection unit can collect transportation delay information and congestion information in real time and provide it immediately. This allows the timing of information collection to be adjusted according to the user's emotions, thereby providing more appropriate information. 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, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user emotion data into the generation AI and cause the generation AI to adjust the timing of information collection.

[0061] When collecting transportation delay information or congestion information, the collection unit can select an optimal collection method based on the user's past commuting patterns. For example, the collection unit prioritizes collecting delay information for transportation modes used by the user in the past. For example, the collection unit can prioritize collecting delay information for trains used by the user in the past. The collection unit can also collect the most relevant information based on the user's commuting time zone. For example, the collection unit can prioritize collecting delay information for morning rush hour based on the user's commuting time zone. Furthermore, the collection unit can analyze the user's past commuting patterns and determine the optimal collection timing. For example, the collection unit can determine the optimal collection timing based on the user's past commuting patterns. This allows more relevant information to be collected by taking the user's past commuting patterns into consideration. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past commuting patterns into the generation AI and cause the generation AI to select the optimal collection method.

[0062] When collecting transportation delay information or congestion information, the collection unit can improve the accuracy of collection based on specific events and weather conditions. For example, when a large-scale event is held, the collection unit can focus on collecting delay information on transportation in the surrounding area. For example, when a sporting event or concert is held, the collection unit can focus on collecting delay information on transportation in the surrounding area. The collection unit can also collect detailed information on the operation status of transportation in bad weather. For example, the collection unit can collect detailed information on the operation status of transportation in bad weather such as rain, snow, or typhoons. Furthermore, the collection unit can collect congestion information during specific seasons and holidays to improve accuracy. For example, the collection unit can collect congestion information during specific seasons and holidays such as the New Year holidays and Golden Week to improve accuracy. This improves the accuracy of collection by taking specific events and weather conditions into consideration. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input information about specific events or weather conditions into the generation AI, allowing the generation AI to improve the accuracy of collection.

[0063] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is stressed, the collection unit prioritizes collecting delay information. For example, when the user is stressed, the collection unit can prioritize collecting train delay information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting congestion information. For example, when the user is relaxed, the collection unit can prioritize collecting station congestion information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the most important information in real time. For example, when the user is in a hurry, the collection unit can prioritize collecting transportation delay information and congestion information in real time. This allows the priority of information to be determined according to the user's emotions, thereby prioritizing the collection of more important information. 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, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the information.

[0064] When collecting delay information or congestion information for transportation facilities, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. The collection unit, for example, prioritizes collecting delay information for transportation facilities closest to the user's current location. For example, the collection unit can prioritize collecting delay information for stations closest to the user's current location. The collection unit can also prioritize collecting congestion information for transportation facilities on the user's commute route. For example, the collection unit can prioritize collecting congestion information for trains and buses on the user's commute route. Furthermore, the collection unit can also prioritize collecting information for transportation facilities related to the user's destination. For example, the collection unit can prioritize collecting delay information and congestion information for trains and buses related to the user's destination. This allows more relevant information to be collected by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0065] When collecting transportation delay information and congestion information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit prioritizes collecting information about transportation mentioned by the user on social media. For example, the collection unit can prioritize collecting train and bus delay information mentioned by the user on social media. The collection unit can also collect information about transportation of interest from the user's social media posts. For example, the collection unit can collect congestion information for specific lines and stations from the user's social media posts. Furthermore, the collection unit can also collect information about transportation shared by the user's followers and friends. For example, the collection unit can collect train and bus delay information and congestion information shared by the user's followers and friends. This allows for more relevant information to be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit may input data about the user's social media activities into the generation AI and cause the generation AI to collect related information.

[0066] The analysis unit can estimate the user's emotions and adjust the analysis method for delay times and congestion levels based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit performs a detailed analysis of delay times. For example, if the user is feeling stressed, the analysis unit can analyze train delay times in detail. The analysis unit can also prioritize congestion level analysis if the user is relaxed. For example, if the user is relaxed, the analysis unit can prioritize analyzing station congestion levels. Furthermore, the analysis unit can quickly provide analysis results if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can provide analysis results for delay times and congestion levels in real time. This allows for adjusting the analysis method according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the analysis method.

[0067] The analysis unit can optimize the analysis algorithm by referring to past data when analyzing delay times and congestion levels. The analysis unit, for example, predicts a current delay time based on past delay data. For example, the analysis unit can predict a current delay time based on past train delay data. The analysis unit can also analyze a current congestion level by referring to past congestion data. For example, the analysis unit can analyze a current congestion level by referring to past station congestion data. Furthermore, the analysis unit can improve the accuracy of the analysis algorithm using past data. For example, the analysis unit can improve the accuracy of the analysis algorithm by using past delay data and congestion data. Thus, by referring to past data, the accuracy of the analysis algorithm is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0068] The analysis unit can apply different analysis methods to specific transportation modes or routes when analyzing delay times or congestion levels. For example, the analysis unit can analyze the delay patterns of a specific route and apply the optimal method. For example, the analysis unit can analyze the delay patterns of a specific railway route and apply the optimal method. The analysis unit can also use different congestion analysis methods for each transportation mode. For example, the analysis unit can use different congestion analysis methods for each transportation mode, such as trains and buses. Furthermore, the analysis unit can customize the analysis method taking into account the characteristics of each route. For example, the analysis unit can customize the analysis method taking into account the characteristics of a specific railway route or bus route. This improves the accuracy of the analysis by applying different analysis methods to specific transportation modes or routes. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data for a specific transportation mode or route into the generation AI and cause the generation AI to apply the analysis method.

[0069] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is nervous, the analysis unit can display delay information and congestion information using simple graphs or icons. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is relaxed, the analysis unit can display delay information and congestion information using detailed text or graphs. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit can concisely display the main points of delay information and congestion information. This allows for more appropriate information to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved 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 analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the display method.

[0070] When analyzing delay times and congestion levels, the analysis unit can determine analysis priorities based on the user's commute time zone. For example, the analysis unit prioritizes analysis of information most relevant to the user's commute time zone. For example, the analysis unit can prioritize analysis of morning rush hour delay information based on the user's commute time zone. The analysis unit can also prioritize analysis of delay times based on the user's commute time zone. For example, the analysis unit can prioritize analysis of train delay times based on the user's commute time zone. Furthermore, the analysis unit can also prioritize analysis of congestion levels based on the user's commute time zone. For example, the analysis unit can prioritize analysis of station congestion levels based on the user's commute time zone. This allows for more relevant information to be provided by determining analysis priorities based on the user's commute time zone. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data about the user's commute time zone into the generation AI and have the generation AI determine the analysis priorities.

[0071] The analysis unit can improve the accuracy of the analysis by referring to related news and event information when analyzing delay times and congestion levels. The analysis unit can, for example, refer to news related to transportation to improve the accuracy of the delay time analysis. For example, the analysis unit can refer to news about transportation strikes or accidents to improve the accuracy of the delay time analysis. The analysis unit can also improve the accuracy of the congestion level analysis by referring to information about large-scale events. For example, the analysis unit can improve the accuracy of the congestion level analysis by referring to information about sporting events or concerts. Furthermore, the analysis unit can adjust the analysis algorithm based on the news and event information. For example, the analysis unit can adjust the analysis algorithm based on news and event information related to transportation. As a result, the accuracy of the analysis is improved by referring to related news and event information. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input news and event information into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0072] The presentation unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user emotions. For example, if the user is feeling stressed, the presentation unit can provide simple and intuitive suggestions. For example, if the user is feeling stressed, the presentation unit can display a telecommuting suggestion using simple text or an icon. Furthermore, if the user is relaxed, the presentation unit can provide suggestions including detailed information. For example, if the user is relaxed, the presentation unit can display a telecommuting suggestion using detailed text or a graph. Furthermore, if the user is in a hurry, the presentation unit can provide quick and concise suggestions. For example, if the user is in a hurry, the presentation unit can display a telecommuting suggestion using concise text. This allows for more appropriate suggestions to be provided by adjusting the way the suggestions are presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the presentation unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the proposed expression method.

[0073] When presenting a proposal, the presentation unit can select an optimal presentation method by referring to the user's past behavioral data. For example, the presentation unit can prioritize a proposal format that the user has previously preferred. For example, the presentation unit can prioritize a notification format that the user has previously preferred. The presentation unit can also present a proposal at an optimal timing based on the user's past behavioral data. For example, the presentation unit can present a telecommuting proposal at an optimal timing based on the user's past behavioral data. Furthermore, the presentation unit can also make highly relevant proposals by referring to the user's past selection history. For example, the presentation unit can make highly relevant telecommuting proposals by referring to the user's past selection history. In this way, by referring to the user's past behavioral data, more relevant proposals can be provided. Some or all of the above-described processing by the presentation unit can be performed using, or without, a generation AI. For example, the presentation unit can input the user's past behavioral data into the generation AI and cause the generation AI to select an optimal presentation method.

[0074] When presenting a proposal, the presentation unit can select an optimal display format based on the user's device information. For example, if the user is using a smartphone, the presentation unit provides a display format tailored to the screen size. For example, if the user is using a smartphone, the presentation unit can display the telecommuting proposal using text and icons tailored to the screen size. Furthermore, if the user is using a tablet, the presentation unit can provide a display format optimized for a large screen. For example, if the user is using a tablet, the presentation unit can display the telecommuting proposal using graphs and text optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can provide a concise and highly visible display format. For example, if the user is using a smartwatch, the presentation unit can display the telecommuting proposal using concise and highly visible icons and text. This allows the proposal to be provided in a more appropriate display format by taking the user's device information into consideration. Some or all of the above-described processing by the presentation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the presentation unit may input the user's device information into the generation AI and cause the generation AI to select an optimal display format.

[0075] The presentation unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the presentation unit prioritizes suggestions for telecommuting. For example, if the user is feeling stressed, the presentation unit can display suggestions for telecommuting as a top priority. The presentation unit can also prioritize other suggestions if the user is relaxed. For example, if the user is relaxed, the presentation unit can prioritize suggestions for alternative routes. Furthermore, if the user is in a hurry, the presentation unit can prioritize suggestions that can be quickly implemented. For example, if the user is in a hurry, the presentation unit can prioritize suggestions for telecommuting that can be quickly implemented. In this way, by prioritizing suggestions according to the user's emotions, more important suggestions can be provided preferentially. 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 presentation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the presentation unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of suggestions.

[0076] When presenting a proposal, the presentation unit can provide an optimal proposal based on the user's geographical location information. The presentation unit, for example, makes a proposal for telecommuting closest to the user's current location. For example, the presentation unit can prioritize displaying a proposal for telecommuting closest to the user's current location. The presentation unit can also make an optimal proposal based on information on the user's commute route. For example, the presentation unit can make an optimal telecommuting proposal based on delay information and congestion information for public transportation on the user's commute route. Furthermore, the presentation unit can also make a proposal related to the user's destination. For example, the presentation unit can make an optimal telecommuting proposal based on delay information and congestion information for public transportation related to the user's destination. This allows for more relevant proposals to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing by the presentation unit may be performed using, or without, a generation AI. For example, the presentation unit can input the user's geographical location information to the generation AI and cause the generation AI to provide the optimal proposal.

[0077] When presenting suggestions, the presentation unit can analyze the user's social media activity and provide relevant suggestions. The presentation unit can make suggestions based on, for example, content mentioned by the user on social media. For example, the presentation unit can suggest telecommuting based on delay information and congestion information for public transportation mentioned by the user on social media. The presentation unit can also make suggestions of interest based on the user's social media posts. For example, the presentation unit can suggest telecommuting based on information about specific lines and stations from the user's social media posts. Furthermore, the presentation unit can make suggestions based on information shared by the user's followers and friends. For example, the presentation unit can suggest telecommuting based on delay information and congestion information for public transportation shared by the user's followers and friends. This allows for more relevant suggestions to be provided by analyzing the user's social media activity. Some or all of the above-described processing by the presentation unit can be performed using, or without, a generation AI. For example, the presentation unit can input data about the user's social media activity into the generation AI and cause the generation AI to provide relevant suggestions. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and presentation 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 transportation delay information and congestion level information using the camera 42 and sensors of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The presentation unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the user of the generated suggestions via a smartphone app or email. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and presentation 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 transportation delay information and congestion level information using the camera 42 and sensors of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The presentation unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the user of the generated suggestions via a smartphone app or email. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and presentation 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 transportation delay information and congestion level information using the camera 42 or sensors of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The presentation unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the user of the generated suggestions via a smartphone app or email. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and presentation 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 transportation delay information and congestion level information using the camera 42 and sensors of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The presentation unit is realized, for example, by the control unit 46A of the robot 414, and notifies the user of the generated suggestions via a smartphone app or email.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The analysis unit can estimate the user's emotions and adjust the timing of making a telecommuting suggestion based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can immediately generate a telecommuting suggestion and notify the suggestion unit. Also, if the user is relaxed, the analysis unit can delay the generation of the suggestion and make the suggestion at a time when the user is more likely to accept it. Furthermore, if the user is in a hurry, the analysis unit can quickly generate the suggestion and immediately notify the suggestion unit. In this way, by adjusting the timing of the suggestion according to the user's emotions, more effective suggestions can be made.

[0080] The collection unit can adjust the frequency of collection of transportation delay information and congestion information based on the user's past commuting patterns. For example, it can prioritize collection of information on routes where the user has frequently experienced delays in the past. It can also prioritize collection of information for specific time periods based on the user's commuting time. Furthermore, it can prioritize collection of information on specific transportation modes based on the user's past selection history. This allows more relevant information to be collected by taking the user's past commuting patterns into consideration.

[0081] The analysis unit can estimate the user's emotions and customize the content of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can emphasize the suggestion of working from home and omit detailed explanations. If the user is relaxed, the analysis unit can suggest a relaxing environment in addition to suggesting working from home. Furthermore, if the user is in a hurry, the analysis unit can generate a suggestion that can be quickly addressed and immediately notify the presentation unit. This allows for more effective suggestions to be made by customizing the content of the suggestions according to the user's emotions.

[0082] The presentation unit can adjust the display format of the suggestions based on the user's device information. For example, if the user is using a smartphone, a simple display format tailored to the screen size can be provided. If the user is using a tablet, a detailed display format optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display format can be provided. This allows suggestions to be provided in a more appropriate display format by taking the user's device information into consideration.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method according to the user's emotions, more appropriate information can be provided.

[0084] When collecting transportation delay information and congestion information, the collection unit can improve the accuracy of collection based on specific events and weather conditions. For example, when a large-scale event is held, it can focus on collecting delay information for transportation in the surrounding area. It can also collect detailed information on the operation status of transportation during bad weather. It can also collect congestion information during specific seasons and holidays to improve accuracy. This improves the accuracy of collection by taking specific events and weather conditions into consideration.

[0085] The analysis unit can estimate the user's emotions and adjust the analysis method for delay time and congestion level based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can perform a detailed analysis of delay time. Also, if the user is relaxed, the analysis unit can prioritize congestion level analysis. Furthermore, if the user is in a hurry, the analysis results can be provided quickly. In this way, by adjusting the analysis method according to the user's emotions, more appropriate analysis results can be provided.

[0086] When collecting delay information and congestion information for transportation facilities, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, it can prioritize collecting delay information for transportation facilities closest to the user's current location. It can also prioritize collecting congestion information for transportation facilities on the user's commute route. It can also prioritize collecting information for transportation facilities related to the user's destination. In this way, more relevant information can be collected by taking the user's geographical location information into consideration.

[0087] The presentation unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion of working from home can be prioritized. If the user is relaxed, other suggestions can be prioritized. Furthermore, if the user is in a hurry, suggestions that can be quickly addressed can be prioritized. In this way, by determining the priority of suggestions according to the user's emotions, more important suggestions can be provided preferentially.

[0088] When presenting suggestions, the suggestion unit can analyze the user's social media activity to provide relevant suggestions. For example, suggestions can be made based on what the user has mentioned on social media. Also, suggestions that are of interest to the user can be made based on the content of the user's social media posts. Furthermore, suggestions can be made based on information shared by the user's followers and friends. Thus, by analyzing the user's social media activity, more relevant suggestions can be provided.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The collection unit collects information on delays or congestion levels of transportation. The collection unit uses sensors and data sources that obtain transportation operation information and congestion levels in real time. For example, sensors that monitor the operation status of transportation systems, and cameras and sensors installed at stations and bus stops, etc., are used. Step 2: The analysis unit analyzes the information collected by the collection unit and analyzes delay times or congestion levels. The analysis unit can analyze delay times and congestion levels using generation AI. For example, if the delay time exceeds a certain time or the congestion level exceeds a specific level, it generates a suggestion recommending telecommuting. Step 3: The presentation unit presents the suggestions generated by the analysis unit to the user. The presentation unit notifies the user via a smartphone app or email. For example, the presentation unit can display a pop-up notification within the app or send the suggestions to the user's email address.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0093] 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.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the 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.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] The data processing system 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.

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

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] [Explanation of symbols]

[0163] 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 delay information or congestion information of transportation facilities; an analysis unit that analyzes the information collected by the collection unit and analyzes a delay time or a congestion degree; a presentation unit that presents the proposal generated by the analysis unit to a user. A system characterized by:

2. The collecting unit Get real-time information on public transport schedules or congestion levels 2. The system of claim 1.

3. The analysis unit Generate work-from-home suggestions when delay or congestion thresholds are exceeded 2. The system of claim 1.

4. The presentation unit Notify users via smartphone app or email 2. The system of claim 1.

5. The analysis unit Leverage user behavior data to customize recommendations 2. The system of claim 1.

6. The collecting unit Estimates user emotions and adjusts the timing of collecting information on public transport delays and congestion levels based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting delay information or congestion information of public transport, the optimal collection method is selected based on the user's past commuting patterns.

2. The system of claim 1.

8. The collecting unit Improve the accuracy of collection of transit delay or congestion information based on specific events or weather conditions 2. The system of claim 1.

Images