system

Abstract

An object of the system according to the embodiment is to effectively introduce the attractiveness of a region to attract customers and to aggregate and summarize negative opinions to be used for problem recognition and improvement.SOLUTION: A system according to an embodiment includes an information collection unit, an editing unit, a counting unit, and a cooperation unit. The information collecting unit collects articles posted on a local bulletin board, an SNS, or a blog. The editing unit automatically edits the article collected by the information collecting unit. The aggregation unit aggregates and summarizes the negative opinions based on the articles edited by the editing unit. The cooperation unit cooperates with the car navigation or the train transfer application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, collecting and editing information to effectively introduce the attractions of a region and attract customers was time-consuming, and the aggregation and summarization of negative opinions was also inefficient.

[0005] The system of the embodiment aims to effectively introduce the attractions of the region, leading to customer attraction, as well as to collect and summarize negative opinions to help identify issues and make improvements. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an editing unit, a tallying unit, and a linking unit. The information collection unit collects articles posted in local newsletters, social media, or blogs. The editing unit automatically edits the articles collected by the information collection unit. The tallying unit tally and summarizes negative opinions based on the articles edited by the editing unit. The linking unit links with a car navigation system or train transfer app. [Effects of the Invention]

[0007] The system according to the embodiment can effectively introduce the attractions of a region, leading to increased customer numbers, and can also aggregate and summarize negative opinions to help identify issues and make improvements. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The local introduction system according to an embodiment of the present invention collects articles posted in local newsletters, social media, and blogs, and uses a generation AI to automatically edit and introduce the local area. This allows the local introduction system to effectively introduce the area's attractions and promote customer attraction.

[0029] A local introduction system according to an embodiment includes an information collection unit, an editing unit, a tallying unit, and a linking unit. The information collection unit collects articles posted in local newsletters, social media, or blogs. For example, the information collection unit collects newsletters issued by local governments. The information collection unit can also collect articles from social media sites such as Facebook and Twitter. The information collection unit can also collect articles from personal blogs and corporate blogs. The editing unit automatically edits the articles collected by the information collection unit. For example, the editing unit can use a generation AI to summarize the articles and generate an attractive introduction. The editing unit can also use the generation AI to organize the content of the articles and edit them into an easy-to-read format. The editing unit can also use the generation AI to extract important points from the articles and generate a concise introduction. The tallying unit tally and summarizes negative opinions based on the articles edited by the editing unit. For example, the tallying unit can extract negative opinions from comments on social media or blogs and tally them. The tallying unit can also summarize the negative opinions and present them as challenges. The aggregation unit can also analyze the frequency and trends of negative opinions and create detailed reports. The linking unit links with a car navigation system or a train transfer app. For example, the linking unit links with a car navigation system to acquire destinations and estimated arrival times. The linking unit can also link with a train transfer app to suggest optimal transfer routes. The linking unit can also suggest optimal sightseeing routes based on the user's current location and destination. This allows the regional introduction system according to the embodiment to effectively introduce the attractions of a region and promote visitor numbers. For example, by compiling and introducing information about regional tourist spots and events in an attractive manner, it is possible to attract tourists and encourage them to visit. Furthermore, by aggregating and summarizing negative opinions, regional issues can be clarified and improvement measures can be implemented. Furthermore, by linking with a car navigation system or a train transfer app, it is possible to suggest optimal sightseeing routes and visiting spots to users, improving convenience.

[0030] The editorial department can automatically research the history and cultural background of a region and generate an in-depth introduction based on that. For example, the editorial department can have the generation AI automatically research the historical events and cultural background of a region and generate an introduction based on that. For example, detailed information about traditional festivals and historical buildings in the region can be included. The editorial department can also have the generation AI collect information about local cultural assets and customs and generate an introduction based on that. For example, the generation AI can generate an introduction about traditional crafts and local customs in the region. The editorial department can also have the generation AI generate a story-like introduction based on the history and cultural background of the region. For example, the generation AI can generate an introduction that weaves in historical episodes and cultural background of the region. This makes it possible to generate an in-depth introduction based on the history and cultural background of the region.

[0031] The editorial department can generate the most appropriate introduction text for each season, taking into account local seasonal events and local specialties. For example, the editorial department has the generation AI collect information on local seasonal events and generate an introduction text based on that information. For example, the editorial department can introduce seasonal events such as spring cherry blossom festivals and summer fireworks displays. The editorial department can also have the generation AI collect information on local specialties and generate an introduction text based on that information. For example, the editorial department can generate an introduction text about agricultural products and crafts unique to the region. The editorial department can also have the generation AI generate an introduction text that combines seasonal events and local specialties. For example, the editorial department can generate an introduction text that combines the spring cherry blossom festival and local specialties. This makes it possible to generate an introduction text that takes into account seasonal events and local specialties.

[0032] The editorial department can automatically generate an audio guide for a region and make it available for tourists to listen to on their smartphones. For example, the editorial department has the generation AI collect information about local tourist spots and historical background, and automatically generate an audio guide based on that information. For example, the editorial department can provide an audio guide that includes detailed explanations of tourist spots and historical episodes. The editorial department can also have the generation AI collect information about local events, and generate an audio guide based on that information. For example, the editorial department can provide an audio guide that includes information about local festivals and events. The editorial department can also have the generation AI generate an audio guide tailored to the interests of tourists. For example, the editorial department can provide an audio guide for natural parks to tourists who are interested in natural landscapes. This allows the automatic generation of audio guides that tourists can listen to on their smartphones.

[0033] The editorial department can generate 3D models of local tourist attractions and provide virtual tours. For example, the editorial department has the generation AI collect photos and map data of local tourist attractions and generate 3D models based on that. For example, it can create 3D models of historical buildings and natural landscapes and provide virtual tours. The editorial department can also have the generation AI collect detailed information about local tourist attractions and generate virtual tours based on that information. For example, it can provide virtual tours that introduce the interiors of tourist attractions in detail. The editorial department can also have the generation AI generate virtual tours tailored to the interests of tourists. For example, it can provide virtual tours of historical buildings to tourists who are interested in history. This allows the editorial department to generate 3D models of local tourist attractions and provide virtual tours.

[0034] The aggregation unit can automatically analyze the specific problems behind the negative opinions and generate detailed improvement proposals. For example, the generation AI collects negative opinions from comments on social media and blogs, and automatically analyzes the specific problems behind them. For example, from the opinion that "transportation is inconvenient," the aggregation unit can identify problems with specific transportation methods and routes. The aggregation unit can also allow the generation AI to analyze the frequency and trends of negative opinions and generate improvement proposals based on that. For example, it can propose specific measures for frequently occurring problems. The aggregation unit can also allow the generation AI to analyze the content of negative opinions in detail and generate actionable improvement proposals based on that. For example, it can propose specific measures to improve transportation. In this way, the specific problems behind negative opinions can be analyzed and improvement proposals can be generated.

[0035] The aggregation unit can analyze the frequency and timing of negative opinions and identify issues related to seasons or events. For example, the generation AI collects negative opinions from comments on social media or blogs, and the aggregation unit analyzes the frequency and timing of their occurrence. For example, it determines whether a large number of negative opinions occur during a specific season or event period. The aggregation unit can also analyze the timing of negative opinions generated by the generation AI and identify issues related to seasons or events based on that analysis. For example, if a large number of negative opinions occur during the summer tourist season, it can identify the cause. The aggregation unit can also analyze the frequency of negative opinions generated by the generation AI and identify issues based on that analysis. For example, if a large number of negative opinions occur during a specific event period, it can identify issues related to that event. This makes it possible to analyze the frequency and timing of negative opinions and identify issues related to seasons or events.

[0036] The aggregation unit can visualize negative opinions on a map and identify where problems are occurring. For example, the generation AI collects negative opinions from comments on social media or blogs and visualizes them on a map. For example, negative opinions about specific areas or tourist spots are displayed on the map. The aggregation unit can also have the generation AI identify the locations where negative opinions are occurring and display them on the map. For example, it can identify the locations where negative opinions are occurring based on GPS data or address information. The aggregation unit can also have the generation AI analyze the locations where negative opinions are occurring and propose improvement measures based on that. For example, if a large number of negative opinions are occurring in a specific area, it can propose specific improvement measures for that area. This makes it possible to visualize negative opinions on a map and identify where problems are occurring.

[0037] The aggregation unit can compare negative opinions with other regions and evaluate the relative severity of the issues. For example, the generation AI collects negative opinions from comments on social media and blogs and compares them with other regions. For example, it compares negative opinions from different regions within the same tourist destination and evaluates the relative severity of the issues. The aggregation unit can also have the generation AI analyze the content of the negative opinions and compare them with other regions based on that analysis. For example, it can check whether a specific problem is also occurring in other regions. The aggregation unit can also have the generation AI analyze the frequency and trends of negative opinions and evaluate the relative severity of the issues based on that analysis. For example, it can check whether a frequently occurring problem is also occurring in other regions. This allows the generation AI to compare negative opinions with other regions and evaluate the relative severity of the issues.

[0038] The collaboration unit can obtain traffic conditions and weather information in real time and propose optimal sightseeing routes based on that information. For example, the generation AI in the collaboration unit obtains traffic conditions in real time and proposes optimal sightseeing routes based on that information. For example, it can propose detour routes taking traffic congestion information into consideration. The collaboration unit can also obtain weather information in real time and propose sightseeing routes based on that information. For example, it can propose indoor tourist spots when it rains. The collaboration unit can also propose optimal sightseeing routes by combining traffic conditions and weather information with the generation AI. For example, it can propose optimal routes taking traffic congestion and weather into consideration. This allows traffic conditions and weather information to be obtained in real time and optimal sightseeing routes to be proposed.

[0039] The linking unit can analyze the user's past movement history and propose customized routes tailored to individual preferences. For example, the linking unit uses a generation AI to analyze the user's past movement history and propose customized sightseeing routes based on that. For example, it can propose new tourist spots based on spots visited in the past. The linking unit can also use the generation AI to analyze the user's preferences and propose customized routes based on that. For example, it can propose natural parks and hiking trails for a user who likes natural scenery. The linking unit can also use the generation AI to propose optimal routes by combining the user's past movement history and preferences. For example, it can propose new tourist spots taking into account past visit history and preferences. This makes it possible to analyze the user's past movement history and propose customized routes tailored to individual preferences.

[0040] The collaboration unit can collaborate with public transportation operation information to propose alternative routes in response to delays or service suspensions. For example, the collaboration unit's generation AI obtains public transportation operation information in real time and proposes alternative routes in response to delays or service suspensions. For example, it proposes bus or taxi routes based on train delay information. The collaboration unit can also have the generation AI analyze operation information and propose the optimal alternative route based on that information. For example, it can propose other means of transportation in the event of a service suspension. The collaboration unit can also have the generation AI combine operation information and traffic conditions to propose an alternative route. For example, it proposes the optimal route taking into account train delays and congestion information. This makes it possible to collaborate with public transportation operation information and propose alternative routes in response to delays or service suspensions.

[0041] The collaboration unit can obtain the congestion status of tourist spots in real time and suggest routes that avoid crowds. For example, the generation AI of the collaboration unit obtains the congestion status of tourist spots in real time and suggests tourist routes that avoid crowds based on that information. For example, it can suggest other spots that avoid crowded spots. The collaboration unit can also have the generation AI analyze the congestion status and suggest the optimal route based on that. For example, it can suggest a detour route to avoid crowds. The collaboration unit can also have the generation AI combine the congestion status and traffic conditions to suggest the optimal route. For example, it can suggest the optimal route taking into account crowded spots and traffic congestion information. This allows the generation AI to obtain the congestion status of tourist spots in real time and suggest routes that avoid crowds.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The local area introduction system can also include a health management unit that monitors the user's health condition and suggests a health-conscious sightseeing route. For example, the health management unit can monitor the user's number of steps and heart rate and suggest a route that avoids excessive exercise. The health management unit can also suggest restaurants where the user can eat healthy meals based on the user's diet history. Furthermore, the health management unit can analyze the user's sleep state and suggest tourist spots where the user can relax. This makes it possible to provide a sightseeing route that takes the user's health condition into consideration.

[0044] The local area introduction system can also include a review analysis unit that suggests tourist spots tailored to individual preferences based on the user's past reviews and ratings. For example, the review analysis unit may prioritize suggesting spots that the user has previously given high ratings. The review analysis unit can also analyze the content of the user's reviews and suggest new spots based on specific keywords. Furthermore, the review analysis unit can suggest popular spots based on reviews from other users. This makes it possible to provide tourist spots tailored to the user's preferences.

[0045] The local introduction system can also include a climate analysis unit that suggests the best time to visit based on local climate data. For example, the climate analysis unit can analyze past climate data and suggest the most comfortable time. The climate analysis unit can also suggest appropriate clothing and items to bring based on the weather conditions in a particular season. Furthermore, the climate analysis unit can also predict future visit times, taking into account the effects of climate change. This allows the system to provide the user with the best time to visit.

[0046] The local introduction system may further include a culture introduction section that collects information about local music and art and provides cultural experiences. For example, the culture introduction section collects information about local music festivals and art exhibitions and suggests them to users. The culture introduction section may also provide information about local traditional music and art. Furthermore, the culture introduction section may generate interview articles with local artists and musicians and provide them to users. This allows the cultural appeal of the local area to be effectively introduced.

[0047] The local area introduction system may further include a sports introduction unit that collects information about local sporting events and activities and provides an active experience. For example, the sports introduction unit collects information about local marathons and cycling events and suggests them to the user. The sports introduction unit may also provide information about local sporting facilities and activities. Furthermore, the sports introduction unit may generate articles about local sporting teams and athletes and provide them to the user. This allows local sporting events and activities to be introduced effectively.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The information gathering department collects articles posted in local newsletters, social media, or blogs. For example, articles are collected from newsletters published by local governments, social media such as Facebook and Twitter, and personal and corporate blogs. Step 2: The editorial department automatically edits the articles collected by the information collection department. For example, they use generative AI to summarize the articles and generate attractive introductions. They also organize the content of the articles, edit them into an easy-to-read format, and extract key points to generate concise introductions. Step 3: The tallying department tally and summarizes negative opinions based on the articles edited by the editorial department. For example, they extract negative opinions from comments on social media and blogs, tally them, and present them as issues. They also analyze the frequency and trends of negative opinions and create a detailed report. Step 4: The linking unit links with a car navigation system or train transfer app. For example, it links with a car navigation system to obtain the destination and estimated arrival time. It also links with a train transfer app to suggest the optimal transfer route. It also suggests the optimal sightseeing route based on the user's current location and destination.

[0050] (Example 2) The local introduction system according to an embodiment of the present invention collects articles posted in local newsletters, social media, and blogs, and uses a generation AI to automatically edit and introduce the local area. This allows the local introduction system to effectively introduce the area's attractions and promote customer attraction.

[0051] A local introduction system according to an embodiment includes an information collection unit, an editing unit, a tallying unit, and a linking unit. The information collection unit collects articles posted in local newsletters, social media, or blogs. For example, the information collection unit collects newsletters issued by local governments. The information collection unit can also collect articles from social media sites such as Facebook and Twitter. The information collection unit can also collect articles from personal blogs and corporate blogs. The editing unit automatically edits the articles collected by the information collection unit. For example, the editing unit can use a generation AI to summarize the articles and generate an attractive introduction. The editing unit can also use the generation AI to organize the content of the articles and edit them into an easy-to-read format. The editing unit can also use the generation AI to extract important points from the articles and generate a concise introduction. The tallying unit tally and summarizes negative opinions based on the articles edited by the editing unit. For example, the tallying unit can extract negative opinions from comments on social media or blogs and tally them. The tallying unit can also summarize the negative opinions and present them as challenges. The aggregation unit can also analyze the frequency and trends of negative opinions and create detailed reports. The linking unit links with a car navigation system or a train transfer app. For example, the linking unit links with a car navigation system to acquire destinations and estimated arrival times. The linking unit can also link with a train transfer app to suggest optimal transfer routes. The linking unit can also suggest optimal sightseeing routes based on the user's current location and destination. This allows the regional introduction system according to the embodiment to effectively introduce the attractions of a region and promote visitor numbers. For example, by compiling and introducing information about regional tourist spots and events in an attractive manner, it is possible to attract tourists and encourage them to visit. Furthermore, by aggregating and summarizing negative opinions, regional issues can be clarified and improvement measures can be implemented. Furthermore, by linking with a car navigation system or a train transfer app, it is possible to suggest optimal sightseeing routes and visiting spots to users, improving convenience.

[0052] The editorial department can automatically research the history and cultural background of a region and generate an in-depth introduction based on that. For example, the editorial department can have the generation AI automatically research the historical events and cultural background of a region and generate an introduction based on that. For example, detailed information about traditional festivals and historical buildings in the region can be included. The editorial department can also have the generation AI collect information about local cultural assets and customs and generate an introduction based on that. For example, the generation AI can generate an introduction about traditional crafts and local customs in the region. The editorial department can also have the generation AI generate a story-like introduction based on the history and cultural background of the region. For example, the generation AI can generate an introduction that weaves in historical episodes and cultural background of the region. This makes it possible to generate an in-depth introduction based on the history and cultural background of the region.

[0053] The editorial department can generate the most appropriate introduction text for each season, taking into account local seasonal events and local specialties. For example, the editorial department has the generation AI collect information on local seasonal events and generate an introduction text based on that information. For example, the editorial department can introduce seasonal events such as spring cherry blossom festivals and summer fireworks displays. The editorial department can also have the generation AI collect information on local specialties and generate an introduction text based on that information. For example, the editorial department can generate an introduction text about agricultural products and crafts unique to the region. The editorial department can also have the generation AI generate an introduction text that combines seasonal events and local specialties. For example, the editorial department can generate an introduction text that combines the spring cherry blossom festival and local specialties. This makes it possible to generate an introduction text that takes into account seasonal events and local specialties.

[0054] The editorial department can use the emotion estimation function to generate an introductory text that matches the user's emotions and draw out positive emotions. For example, the editorial department uses the emotion estimation function to analyze the user's emotional state and generate an introductory text that draws out positive emotions based on that. For example, if the user wants to relax, the editorial department can recommend tranquil natural landscapes. The editorial department can also use the emotion estimation function to suggest tourist spots that match the user's emotions. For example, if the user wants to get excited, the editorial department can recommend spots with plenty of activities. The editorial department can also use the emotion estimation function to provide event information that matches the user's emotions. For example, if the user wants to have fun, the editorial department can recommend local festivals and events. In this way, an introductory text that matches the user's emotions can be generated and draw out positive emotions.

[0055] The editorial department can automatically generate an audio guide for a region and make it available for tourists to listen to on their smartphones. For example, the editorial department has the generation AI collect information about local tourist spots and historical background, and automatically generate an audio guide based on that information. For example, the editorial department can provide an audio guide that includes detailed explanations of tourist spots and historical episodes. The editorial department can also have the generation AI collect information about local events, and generate an audio guide based on that information. For example, the editorial department can provide an audio guide that includes information about local festivals and events. The editorial department can also have the generation AI generate an audio guide tailored to the interests of tourists. For example, the editorial department can provide an audio guide for natural parks to tourists who are interested in natural landscapes. This allows the automatic generation of audio guides that tourists can listen to on their smartphones.

[0056] The editorial department can generate 3D models of local tourist attractions and provide virtual tours. For example, the editorial department has the generation AI collect photos and map data of local tourist attractions and generate 3D models based on that. For example, it can create 3D models of historical buildings and natural landscapes and provide virtual tours. The editorial department can also have the generation AI collect detailed information about local tourist attractions and generate virtual tours based on that information. For example, it can provide virtual tours that introduce the interiors of tourist attractions in detail. The editorial department can also have the generation AI generate virtual tours tailored to the interests of tourists. For example, it can provide virtual tours of historical buildings to tourists who are interested in history. This allows the editorial department to generate 3D models of local tourist attractions and provide virtual tours.

[0057] The editorial department can use the emotion estimation function to suggest tourist spots that the user may be interested in in real time. For example, the editorial department uses the emotion estimation function to analyze the user's interests and suggests tourist spots in real time based on the analysis. For example, if the user is interested in natural landscapes, natural parks and hiking trails can be suggested. The editorial department can also use the emotion estimation function to analyze the user's emotional state and suggest tourist spots based on the analysis. For example, if the user wants to relax, tranquil natural landscapes can be suggested. The editorial department can also use the emotion estimation function to suggest tourist spots based on the user's past visit history. For example, new tourist spots can be suggested based on previously visited spots. This makes it possible to suggest tourist spots that the user may be interested in in real time.

[0058] The aggregation unit can automatically analyze the specific problems behind the negative opinions and generate detailed improvement proposals. For example, the generation AI collects negative opinions from comments on social media and blogs, and automatically analyzes the specific problems behind them. For example, from the opinion that "transportation is inconvenient," the aggregation unit can identify problems with specific transportation methods and routes. The aggregation unit can also allow the generation AI to analyze the frequency and trends of negative opinions and generate improvement proposals based on that. For example, it can propose specific measures for frequently occurring problems. The aggregation unit can also allow the generation AI to analyze the content of negative opinions in detail and generate actionable improvement proposals based on that. For example, it can propose specific measures to improve transportation. In this way, the specific problems behind negative opinions can be analyzed and improvement proposals can be generated.

[0059] The aggregation unit can analyze the frequency and timing of negative opinions and identify issues related to seasons or events. For example, the generation AI collects negative opinions from comments on social media or blogs, and the aggregation unit analyzes the frequency and timing of their occurrence. For example, it determines whether a large number of negative opinions occur during a specific season or event period. The aggregation unit can also analyze the timing of negative opinions generated by the generation AI and identify issues related to seasons or events based on that analysis. For example, if a large number of negative opinions occur during the summer tourist season, it can identify the cause. The aggregation unit can also analyze the frequency of negative opinions generated by the generation AI and identify issues based on that analysis. For example, if a large number of negative opinions occur during a specific event period, it can identify issues related to that event. This makes it possible to analyze the frequency and timing of negative opinions and identify issues related to seasons or events.

[0060] The aggregation unit can visualize negative opinions on a map and identify where problems are occurring. For example, the generation AI collects negative opinions from comments on social media or blogs and visualizes them on a map. For example, negative opinions about specific areas or tourist spots are displayed on the map. The aggregation unit can also have the generation AI identify the locations where negative opinions are occurring and display them on the map. For example, it can identify the locations where negative opinions are occurring based on GPS data or address information. The aggregation unit can also have the generation AI analyze the locations where negative opinions are occurring and propose improvement measures based on that. For example, if a large number of negative opinions are occurring in a specific area, it can propose specific improvement measures for that area. This makes it possible to visualize negative opinions on a map and identify where problems are occurring.

[0061] The aggregation unit can compare negative opinions with other regions and evaluate the relative severity of the issues. For example, the generation AI collects negative opinions from comments on social media and blogs and compares them with other regions. For example, it compares negative opinions from different regions within the same tourist destination and evaluates the relative severity of the issues. The aggregation unit can also have the generation AI analyze the content of the negative opinions and compare them with other regions based on that analysis. For example, it can check whether a specific problem is also occurring in other regions. The aggregation unit can also have the generation AI analyze the frequency and trends of negative opinions and evaluate the relative severity of the issues based on that analysis. For example, it can check whether a frequently occurring problem is also occurring in other regions. This allows the generation AI to compare negative opinions with other regions and evaluate the relative severity of the issues.

[0062] The aggregation unit can use the emotion estimation function to monitor users' emotional reactions to negative opinions in real time and encourage them to take prompt action. The aggregation unit, for example, uses the emotion estimation function to monitor users' emotional reactions to negative opinions from comments on social media or blogs in real time. For example, it encourages users to take prompt action when emotions of anger or dissatisfaction are strong. The aggregation unit can also use the emotion estimation function to analyze users' emotional reactions and suggest countermeasures based on the analysis. For example, it can suggest appropriate countermeasures according to the user's emotional state. The aggregation unit can also use the emotion estimation function to evaluate the emotional intensity of negative opinions and encourage users to take prompt action based on the evaluation. For example, it can prioritize responses to opinions with high emotional intensity. This makes it possible to monitor users' emotional reactions to negative opinions in real time and encourage users to take prompt action.

[0063] The collaboration unit can obtain traffic conditions and weather information in real time and propose optimal sightseeing routes based on that information. For example, the generation AI in the collaboration unit obtains traffic conditions in real time and proposes optimal sightseeing routes based on that information. For example, it can propose detour routes taking traffic congestion information into consideration. The collaboration unit can also obtain weather information in real time and propose sightseeing routes based on that information. For example, it can propose indoor tourist spots when it rains. The collaboration unit can also propose optimal sightseeing routes by combining traffic conditions and weather information with the generation AI. For example, it can propose optimal routes taking traffic congestion and weather into consideration. This allows traffic conditions and weather information to be obtained in real time and optimal sightseeing routes to be proposed.

[0064] The linking unit can analyze the user's past movement history and propose customized routes tailored to individual preferences. For example, the linking unit uses a generation AI to analyze the user's past movement history and propose customized sightseeing routes based on that. For example, it can propose new tourist spots based on spots visited in the past. The linking unit can also use the generation AI to analyze the user's preferences and propose customized routes based on that. For example, it can propose natural parks and hiking trails for a user who likes natural scenery. The linking unit can also use the generation AI to propose optimal routes by combining the user's past movement history and preferences. For example, it can propose new tourist spots taking into account past visit history and preferences. This makes it possible to analyze the user's past movement history and propose customized routes tailored to individual preferences.

[0065] The collaboration unit can collaborate with public transportation operation information to propose alternative routes in response to delays or service suspensions. For example, the collaboration unit's generation AI obtains public transportation operation information in real time and proposes alternative routes in response to delays or service suspensions. For example, it proposes bus or taxi routes based on train delay information. The collaboration unit can also have the generation AI analyze operation information and propose the optimal alternative route based on that information. For example, it can propose other means of transportation in the event of a service suspension. The collaboration unit can also have the generation AI combine operation information and traffic conditions to propose an alternative route. For example, it proposes the optimal route taking into account train delays and congestion information. This makes it possible to collaborate with public transportation operation information and propose alternative routes in response to delays or service suspensions.

[0066] The collaboration unit can obtain the congestion status of tourist spots in real time and suggest routes that avoid crowds. For example, the generation AI of the collaboration unit obtains the congestion status of tourist spots in real time and suggests tourist routes that avoid crowds based on that information. For example, it can suggest other spots that avoid crowded spots. The collaboration unit can also have the generation AI analyze the congestion status and suggest the optimal route based on that. For example, it can suggest a detour route to avoid crowds. The collaboration unit can also have the generation AI combine the congestion status and traffic conditions to suggest the optimal route. For example, it can suggest the optimal route taking into account crowded spots and traffic congestion information. This allows the generation AI to obtain the congestion status of tourist spots in real time and suggest routes that avoid crowds.

[0067] The linking unit can use the emotion estimation function to collect the user's emotional reactions to the spots they have visited and reflect them in the next suggestion. For example, the linking unit can use the emotion estimation function to collect the user's emotional reactions to the tourist spots they have visited and make the next suggestion based on that. For example, the linking unit can suggest spots where the user has expressed positive emotions again next time. The linking unit can also use the emotion estimation function to analyze the user's emotional reactions and make the next suggestion based on that. For example, the linking unit can suggest spots that the user enjoyed again next time. The linking unit can also use the emotion estimation function to suggest new tourist spots based on the user's emotional reactions. For example, the linking unit can suggest spots where the user felt relaxed again next time. In this way, the linking unit can collect the user's emotional reactions to the spots they have visited and reflect them in the next suggestion.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The local area introduction system can also include a health management unit that monitors the user's health condition and suggests a health-conscious sightseeing route. For example, the health management unit can monitor the user's number of steps and heart rate and suggest a route that avoids excessive exercise. The health management unit can also suggest restaurants where the user can eat healthy meals based on the user's diet history. Furthermore, the health management unit can analyze the user's sleep state and suggest tourist spots where the user can relax. This makes it possible to provide a sightseeing route that takes the user's health condition into consideration.

[0070] The local area introduction system can also include a review analysis unit that suggests tourist spots tailored to individual preferences based on the user's past reviews and ratings. For example, the review analysis unit may prioritize suggesting spots that the user has previously given high ratings. The review analysis unit can also analyze the content of the user's reviews and suggest new spots based on specific keywords. Furthermore, the review analysis unit can suggest popular spots based on reviews from other users. This makes it possible to provide tourist spots tailored to the user's preferences.

[0071] The local introduction system can also include a climate analysis unit that suggests the best time to visit based on local climate data. For example, the climate analysis unit can analyze past climate data and suggest the most comfortable time. The climate analysis unit can also suggest appropriate clothing and items to bring based on the weather conditions in a particular season. Furthermore, the climate analysis unit can also predict future visit times, taking into account the effects of climate change. This allows the system to provide the user with the best time to visit.

[0072] The local introduction system may further include a culture introduction section that collects information about local music and art and provides cultural experiences. For example, the culture introduction section collects information about local music festivals and art exhibitions and suggests them to users. The culture introduction section may also provide information about local traditional music and art. Furthermore, the culture introduction section may generate interview articles with local artists and musicians and provide them to users. This allows the cultural appeal of the local area to be effectively introduced.

[0073] The local area introduction system may further include a sports introduction unit that collects information about local sporting events and activities and provides an active experience. For example, the sports introduction unit collects information about local marathons and cycling events and suggests them to the user. The sports introduction unit may also provide information about local sporting facilities and activities. Furthermore, the sports introduction unit may generate articles about local sporting teams and athletes and provide them to the user. This allows local sporting events and activities to be introduced effectively.

[0074] Using the emotion estimation function, the editorial department can suggest local traditional cuisine and restaurants based on the user's emotions. For example, if the user wants to relax, a restaurant with a quiet atmosphere can be suggested. If the user wants to get excited, a lively restaurant or bar can be suggested. Furthermore, if the user is interested in a particular cuisine, a restaurant that serves that cuisine can be suggested. This allows the editorial department to provide a dining experience that matches the user's emotions.

[0075] Using the emotion estimation function, the editorial department can suggest local activities based on the user's emotions. For example, if the user wants to relax, yoga or meditation classes can be suggested. If the user wants to get excited, adventure sports or theme parks can be suggested. Furthermore, if the user wants to learn, local workshops and seminars can be suggested. This allows the editorial department to provide activities that match the user's emotions.

[0076] Using the emotion estimation function, the editorial department can suggest local accommodations based on the user's emotions. For example, if the user wants to relax, a quiet resort hotel can be suggested. If the user wants to get excited, hotels and resorts with plenty of activities can be suggested. Furthermore, if the user is interested in a specific theme, accommodations that match that theme can be suggested. This allows the editorial department to provide a lodging experience that matches the user's emotions.

[0077] Using the emotion estimation function, the editorial department can suggest local shopping spots based on the user's emotions. For example, if the user wants to relax, quiet shopping malls and boutiques can be suggested. If the user wants to get excited, large shopping centers and outlets can be suggested. Furthermore, if the user is interested in a particular product, stores that carry that product can be suggested. This allows the editorial department to provide a shopping experience that is tailored to the user's emotions.

[0078] Using the emotion estimation function, the editorial department can suggest local entertainment spots based on the user's emotions. For example, if the user wants to relax, a quiet movie theater or theater can be suggested. If the user wants to get excited, a live music venue or nightclub can be suggested. Furthermore, if the user is interested in a particular type of entertainment, spots that offer that type of entertainment can be suggested. This allows the editorial department to provide an entertainment experience that matches the user's emotions.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The information gathering department collects articles posted in local newsletters, social media, or blogs. For example, articles are collected from newsletters published by local governments, social media such as Facebook and Twitter, and personal and corporate blogs. Step 2: The editorial department automatically edits the articles collected by the information collection department. For example, they use generative AI to summarize the articles and generate attractive introductions. They also organize the content of the articles, edit them into an easy-to-read format, and extract key points to generate concise introductions. Step 3: The tallying department tally and summarizes negative opinions based on the articles edited by the editorial department. For example, they extract negative opinions from comments on social media and blogs, tally them, and present them as issues. They also analyze the frequency and trends of negative opinions and create a detailed report. Step 4: The linking unit links with a car navigation system or train transfer app. For example, it links with a car navigation system to obtain the destination and estimated arrival time. It also links with a train transfer app to suggest the optimal transfer route. It also suggests the optimal sightseeing route based on the user's current location and destination.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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. [Explanation of symbols]

[0148] 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. An information gathering department that collects articles posted in local newsletters, social media, or blogs; an editing unit that automatically edits the articles collected by the information collecting unit; a compilation unit that compiles and summarizes negative opinions based on the articles edited by the editorial unit; and a linking unit that links with a car navigation or train transfer app. A system characterized by:

2. The editorial department Automatically research the history and cultural background of a region and generate in-depth introductions based on that information 2. The system of claim 1.

3. The editorial department Automatically generate local audio guides that tourists can listen to on their smartphones 2. The system of claim 1.

4. The aggregation unit Automatically analyze the specific issues behind negative feedback and generate detailed improvement suggestions 2. The system of claim 1.

5. The linking unit is Obtaining real-time traffic and weather information and suggesting optimal sightseeing routes based on that information 2. The system of claim 1.

6. The editorial department Generate introductory text that matches the user's emotions and elicits positive feelings 2. The system of claim 1.

7. The aggregation unit Evaluate the emotional intensity of negative feedback and prioritize the most impactful issues 2. The system of claim 1.

8. The linking unit is Collecting users' emotional reactions to the places they visited and reflecting them in future recommendations 2. The system of claim 1.

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