system

Abstract

An object of a system according to an embodiment is to efficiently collect and organize information from an SNS and propose an optimal schedule to a user.SOLUTION: A system according to an embodiment includes an information collection unit, a screening unit, a summarization unit, a proposal unit, a word-of-mouth provision unit, a map linkage unit, and a personal data analysis unit. The information collection unit collects information from the SNS. The screening unit screens the information collected by the information collection unit. The summarizing unit summarizes and organizes the information screened by the screening unit. The proposal unit proposes an optimal schedule to the user based on the information summarized and organized by the summarization unit. The word-of-mouth providing unit provides word-of-mouth information. The map cooperation unit proposes a time schedule including transportation in cooperation with the map data. The personal data analysis unit analyzes the user's personal data and makes a proposal that suits the user's preference.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to efficiently collect and organize information from SNS and propose optimal schedules to users.

[0005] The system according to the embodiment aims to efficiently collect and organize information from SNS and propose optimal schedules to users. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a screening unit, a summarization unit, a suggestion unit, a word-of-mouth provision unit, a map linkage unit, and a personal data analysis unit. The information collection unit collects information from SNS. The screening unit screens the information collected by the information collection unit. The summarization unit summarizes and organizes the information screened by the screening unit. The suggestion unit proposes an optimal plan for the user based on the information summarized and organized by the summarization unit. The word-of-mouth provision unit provides word-of-mouth information. The map linkage unit proposes a time schedule including means of transportation in conjunction with map data. The personal data analysis unit analyzes the user's personal data and makes suggestions that match the user's preferences. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and organize information from SNS and propose optimal schedules to users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The event suggestion system according to the embodiment of the present invention is a system that suggests optimal events by utilizing a user's location information and personal data, thereby enabling the event suggestion system to efficiently suggest events that match the user's preferences and schedule.

[0029] An event proposal system according to an embodiment includes an information collection unit, a screening unit, a summarization unit, a proposal unit, a review provision unit, a map linkage unit, and a personal data analysis unit. The information collection unit collects information from social media platforms. For example, it collects information about events from social media platforms such as Twitter, Facebook, and Instagram. The screening unit screens the information collected by the information collection unit. For example, it evaluates the reliability of the collected information and selects highly reliable information. The summarization unit summarizes and organizes the information screened by the screening unit. For example, a generation AI uses a summarization algorithm to concisely summarize the information. The proposal unit proposes optimal plans to a user based on the information summarized and organized by the summarization unit. For example, it proposes events based on the user's preferences and schedule. The review provision unit provides review information. For example, it analyzes the review information collected by the generation AI and provides it to the user. The map linkage unit proposes a time schedule including transportation options in conjunction with map data. For example, it proposes optimal transportation options using map data such as Google Maps and OpenStreetMap. The personal data analysis unit analyzes the user's personal data and makes proposals that match the user's preferences. For example, events are suggested based on the user's past event participation history, interests, etc. This allows the event suggestion system according to the embodiment to efficiently suggest events that match the user's preferences and schedule.

[0030] The screening unit can prioritize suggesting events with many positive reviews about a particular event. For example, the screening unit refers to past success rate data for the event information collected by the generation AI from social media, and prioritizes suggesting events with a high success rate. For example, it prioritizes displaying information about events that have received high ratings in the past. The screening unit also analyzes participant satisfaction data for the event information collected by the generation AI from social media, and prioritizes suggesting events with high satisfaction. For example, it selects events based on participant reviews and evaluation scores. The screening unit also evaluates the event information collected by the generation AI from social media by combining past success rates and participant satisfaction to suggest events with high reliability. For example, it prioritizes displaying events with both a high success rate and high satisfaction. This allows for improved user satisfaction by prioritizing suggesting events with many positive reviews.

[0031] The screening unit can improve reliability by evaluating events based on their past success rates and participant satisfaction. For example, the screening unit refers to the organizer's past performance data for the event information collected by the generation AI from social media, and prioritizes suggesting events from highly reliable organizers. For example, it prioritizes displaying information about organizers who have held many successful events in the past. The screening unit also analyzes the organizer's reliability data for the event information collected by the generation AI from social media, and prioritizes suggesting events from highly reliable organizers. For example, it selects events based on the organizer's evaluation score and reviews. The screening unit also evaluates the event information collected by the generation AI from social media, combining the organizer's past performance and reliability, and suggests highly reliable events. For example, it prioritizes displaying events from organizers with both high performance and reliability. This makes it possible to suggest highly reliable events by evaluating based on past success rates and participant satisfaction.

[0032] The information collection unit can also collect information from online forums and blogs other than SNS, and the screening unit can integrate and screen that information. For example, the information collection unit allows the generation AI to collect event information from online forums and blogs other than SNS, integrate that information, and screen it. For example, it analyzes forum posts and blog articles to select highly reliable information. The information collection unit also integrates the information collected by the generation AI from online forums and blogs other than SNS with the information collected from SNS and performs a comprehensive evaluation. For example, it selects events based on data from multiple information sources. The information collection unit also compares the information collected by the generation AI from online forums and blogs other than SNS with the information collected from SNS, and preferentially suggests matching information. For example, it preferentially displays events that match across multiple information sources. This allows information to be collected from information sources other than SNS, integrated, and screened, thereby enabling more reliable event suggestions.

[0033] The information collection unit collects information from social media platforms in different languages ​​and cultures, and the screening unit can screen from a global perspective. For example, the generation AI collects event information from social media platforms in different languages ​​and cultures and screens it from a global perspective. For example, the information collection unit collects information from social media platforms in English, Chinese, Spanish, etc., integrates it, and evaluates it. The information collection unit also integrates the information collected by the generation AI from social media platforms in different languages ​​and cultures using language translation technology and performs a comprehensive evaluation. For example, it selects events based on the translated information. The information collection unit also screens the information collected by the generation AI from social media platforms in different languages ​​and cultures, taking cultural background into consideration. For example, it prioritizes suggesting culturally significant events and trends. This allows for the collection of information from different languages ​​and cultures and screening it from a global perspective, making it possible to suggest a wider variety of events.

[0034] The summary section can also propose a detailed event schedule and special offer information. For example, when the generation AI summarizes, the summary section proposes a detailed event schedule. For example, it displays the event start time, end time, and the timetable for each session in detail. The summary section also proposes special offer information for the event when the generation AI summarizes. For example, it displays special offers and discount information offered to participants. The summary section also proposes a detailed event schedule and special offer information in combination when the generation AI summarizes. For example, it displays the schedule and special offer information in a single view so that the user can easily check them. This makes it easier for the user to grasp the overall picture of the event by proposing a detailed schedule and special offer information.

[0035] The summarization unit can reflect the feedback and ratings of past participants. For example, the summarization unit reflects the feedback of past participants in the information summarized by the generation AI. For example, it incorporates participant reviews and comments into the summary. The summarization unit also reflects the rating scores of past participants in the information summarized by the generation AI. For example, it prioritizes suggesting events with high rating scores. The summarization unit also reflects a combination of the feedback and rating scores of past participants in the information summarized by the generation AI. For example, it selects events based on the feedback and rating scores and reflects them in the summary. In this way, by reflecting the feedback and ratings of past participants, it is possible to provide users with highly reliable information.

[0036] The summarization unit can provide the summarized information as visual notes or infographics to make it easier to understand visually. For example, the summarization unit may provide the information summarized by the generation AI as visual notes to make it easier to understand visually. For example, it may show important points using diagrams or icons. The summarization unit may also provide the information summarized by the generation AI as infographics to make it easier to understand visually. For example, it may display data or statistical information in graphs or charts. The summarization unit may also provide the information summarized by the generation AI as visual notes or infographics to make it easier to understand visually. For example, it may complement the summary text with diagrams, icons, and graphs. This can help the user's understanding by providing information in a format that is easy to understand visually.

[0037] The summarizing unit can optimize the summarized information for different devices (smartphones, tablets, PCs) and display it accordingly. For example, the summarizing unit may optimize the information summarized by the generation AI for smartphones and display it accordingly. For example, it may adjust the layout to fit the screen size of a smartphone. The summarizing unit may also optimize the information summarized by the generation AI for tablets and display it accordingly. For example, it may adjust the layout to fit the screen size of a tablet. The summarizing unit may also optimize the information summarized by the generation AI for PCs and display it accordingly. For example, it may adjust the layout to fit the screen size of a PC. This allows the information to be displayed in an optimized manner for different devices, thereby improving user convenience.

[0038] The review providing unit can analyze review information and extract event features based on specific keywords and phrases. In the review providing unit, for example, the generation AI analyzes review information and extracts event features based on specific keywords and phrases. For example, event features are identified based on keywords such as "fun," "family-friendly," and "music." In addition, the review providing unit analyzes review information and extracts frequently occurring phrases to identify event features. For example, event features are identified based on phrases such as "great performance" and "delicious food." In addition, the review providing unit analyzes review information and extracts positive keywords and phrases to identify event features. For example, event features are identified based on positive phrases such as "great," "fun," and "I want to go again." In this way, useful information can be provided to users by extracting event features based on specific keywords and phrases.

[0039] The review providing unit classifies the review information based on the age group and interests of the participants, and can provide the user with the most suitable information. For example, the generation AI in the review providing unit analyzes the review information and classifies it based on the age group of the participants. For example, it identifies events for young people and events for seniors and provides the user with the most suitable information. The generation AI in the review providing unit also analyzes the review information and classifies it based on the interests of the participants. For example, it provides event information according to interests such as music lovers, sports lovers, and art lovers. The generation AI in the review providing unit also analyzes the review information and classifies it based on both age group and interests. For example, it provides word-of-mouth information about music events to a young user who loves music. In this way, by classifying the review information based on the age group and interests of the participants, the most suitable information can be provided to the user.

[0040] The review providing unit can automatically translate review information into different languages ​​and provide feedback from an international perspective. For example, the generation AI automatically translates review information into different languages ​​and provides feedback from an international perspective. For example, the review providing unit translates and displays the information into languages ​​such as English, Chinese, and Spanish. The review providing unit also analyzes the review information translated into different languages ​​by the generation AI and provides feedback from an international perspective. For example, the review providing unit evaluates an event based on feedback from users from different cultural backgrounds. The review providing unit also integrates the review information translated into different languages ​​by the generation AI and performs a comprehensive evaluation. For example, the review providing unit prioritizes displaying positive reviews that match in multiple languages. This allows feedback from an international perspective to be provided by automatically translating into different languages.

[0041] The word-of-mouth providing unit can provide the word-of-mouth information as audio data, allowing the user to obtain the information aurally. For example, the word-of-mouth providing unit has the generation AI provide the word-of-mouth information as audio data, allowing the user to obtain the information aurally. For example, it converts text data into audio data using speech synthesis technology. The word-of-mouth providing unit also optimizes the word-of-mouth information provided by the generation AI as audio data for playback on the user's device. For example, it plays the audio data on a smartphone or tablet. The word-of-mouth providing unit also customizes the word-of-mouth information provided by the generation AI as audio data to suit the user's preferences. For example, it adjusts the tone and speed of the audio before playback. In this way, the word-of-mouth information can be provided as audio data, allowing the user to obtain the information aurally.

[0042] The map linking unit can analyze map data and propose the optimal means of transportation taking into account traffic conditions and weather information around the event venue. For example, the generation AI in the map linking unit analyzes map data and proposes the optimal means of transportation taking into account traffic conditions around the event venue. For example, it selects a means of transportation based on traffic congestion and the operation status of public transportation. The generation AI in the map linking unit also analyzes map data and proposes the optimal means of transportation taking into account weather information around the event venue. For example, it selects a means of transportation based on a weather forecast. The generation AI in the map linking unit also analyzes map data and proposes the optimal means of transportation by combining traffic conditions and weather information. For example, it selects a means of transportation based on traffic congestion and a weather forecast. This makes it possible to make the user's travel more efficient by proposing the optimal means of transportation taking into account traffic conditions and weather information.

[0043] The map linking unit can also suggest tourist spots and restaurants around the event venue based on the map data. For example, the generation AI in the map linking unit analyzes the map data and suggests tourist spots around the event venue. For example, it displays tourist spots that can be visited before or after the event. The generation AI in the map linking unit also analyzes the map data and suggests restaurants around the event venue. For example, it displays restaurants and cafes where you can enjoy a meal before or after the event. The generation AI in the map linking unit also analyzes the map data and suggests tourist spots and restaurants in combination. For example, it displays tourist spots and nearby restaurants together. This makes it possible to enrich the user's event experience by suggesting tourist spots and restaurants around the event venue.

[0044] The map linking unit can display map data on a 3D map or AR (augmented reality) to enable the user to intuitively understand the route. For example, the map linking unit causes the generation AI to display map data on a 3D map to enable the user to intuitively understand the route. For example, the map linking unit visually displays the route on a 3D map. Furthermore, the map linking unit causes the generation AI to display map data in AR (augmented reality) to enable the user to intuitively understand the route. For example, the map linking unit displays the route through a smartphone camera. Furthermore, the map linking unit causes the generation AI to display map data on a 3D map or AR to enable the user to intuitively understand the route. For example, the map linking unit combines 3D maps and AR to display the route. In this way, by displaying the route on a 3D map or AR, the user can intuitively understand the route.

[0045] The map linking unit links map data with real-time information from public transportation and can suggest the optimal means of transportation. In the map linking unit, for example, the generation AI links map data with real-time information from public transportation and suggests the optimal means of transportation. For example, it selects a means of transportation based on the operation status of trains and buses. In addition, the map linking unit links map data with real-time information from public transportation and suggests means of transportation that avoid traffic congestion. For example, it displays a route that avoids traffic congestion. In addition, the map linking unit links map data with real-time information from public transportation and suggests the optimal means of transportation and route. For example, it displays a route by combining the operation status of trains and buses with traffic congestion information. In this way, by linking with real-time information from public transportation, the optimal means of transportation can be suggested.

[0046] The personal data analysis unit can also suggest events in new genres based on the user's interests, thereby broadening the user's interests. In the personal data analysis unit, for example, the generation AI analyzes the user's interests and suggests events in new genres. For example, it suggests art events to a user who likes music. In addition, the personal data analysis unit can analyze the user's interests and suggest related events in new genres. For example, it suggests outdoor events to a user who likes sports. In addition, the personal data analysis unit can analyze the user's interests and suggest events in new genres to broaden the user's interests. For example, it suggests theater events to a user who likes movies. In this way, it is possible to broaden the user's interests by suggesting events in new genres based on the user's interests.

[0047] The personal data analysis unit can link the user's personal data with different devices and platforms to make integrated event suggestions. For example, the generation AI in the personal data analysis unit links the user's personal data with different devices such as smartphones, tablets, and PCs to make integrated event suggestions. For example, it synchronizes data between devices to make consistent suggestions. The personal data analysis unit also links the user's personal data with different platforms such as social media and email to make integrated event suggestions. For example, it suggests events based on social media data. The personal data analysis unit also links the user's personal data with different devices and platforms to integrate the data and make event suggestions. For example, it suggests events based on data from multiple devices and platforms. This makes it possible to make integrated event suggestions by linking different devices and platforms.

[0048] The personal data analysis unit can integrate the user's personal data with data on friends and family to suggest events that can be enjoyed as a group. For example, the generation AI in the personal data analysis unit can integrate the user's personal data with data on friends and family to suggest events that can be enjoyed as a group. For example, it can suggest events that the whole family can enjoy. Furthermore, the generation AI in the personal data analysis unit can integrate the user's personal data with data on friends to suggest events based on common interests. For example, it can suggest events that all friends are interested in. Furthermore, the generation AI in the personal data analysis unit can integrate the user's personal data with data on family members to suggest events that fit the schedules of all family members. For example, it can suggest events with dates and times that all family members can attend. In this way, by integrating data on friends and family, it can suggest events that can be enjoyed as a group.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The information collection unit can suggest similar events based on the user's past event participation history. For example, a similar music event can be suggested to a user who has previously attended a music festival. The information collection unit can also suggest events in a new genre based on the user's interests. For example, an outdoor event can be suggested to a user who likes sports. Furthermore, the information collection unit can suggest the most suitable date and time for an event to suit the user's schedule. This enables more personalized event suggestions based on the user's past participation history and interests.

[0051] The screening department can evaluate the environmental impact of an event and prioritize eco-friendly events. For example, it can select events that use renewable energy or have recycling programs. The screening department can also evaluate the social impact of an event and prioritize events that contribute to the community. For example, it can select events that support local artists or charity events. Furthermore, the screening department can evaluate the safety of an event and prioritize events that have solid safety measures. This makes it possible to recommend events that take into account the environment, social impact, and safety.

[0052] The screening unit can suggest appropriate events taking into account the user's health condition. For example, it can suggest allergen-free events to a user with allergies. The screening unit can also suggest events with an appropriate amount of exercise based on the user's fitness level. For example, it can select yoga classes for beginners and marathon events for advanced runners. Furthermore, the screening unit can suggest relaxation events or stress relief events taking into account the user's mental health. This makes it possible to suggest events according to the user's health condition.

[0053] The information collection unit can suggest similar events based on the user's past event participation history. For example, a similar music event can be suggested to a user who has previously attended a music festival. The information collection unit can also suggest events in a new genre based on the user's interests. For example, an outdoor event can be suggested to a user who likes sports. Furthermore, the information collection unit can suggest the most suitable date and time for an event to suit the user's schedule. This enables more personalized event suggestions based on the user's past participation history and interests.

[0054] The information gathering unit collects information from social media platforms in different languages ​​and cultures, allowing the screening unit to screen from a global perspective. For example, it collects, integrates, and evaluates information from social media platforms in English, Chinese, Spanish, and other languages. The information gathering unit also integrates and comprehensively evaluates the information collected by the generation AI from social media platforms in different languages ​​and cultures using language translation technology. For example, it selects events based on the translated information. The information gathering unit also screens the information collected by the generation AI from social media platforms in different languages ​​and cultures, taking cultural background into consideration. For example, it prioritizes the suggestion of culturally significant events and trends. This allows the system to collect information from different languages ​​and cultures and screen it from a global perspective, making it possible to suggest a wider variety of events.

[0055] The summary section can also propose detailed event schedules and special offer information. For example, it displays the start and end times of the event, the timetable for each session, and other details. When the generation AI summarizes, the summary section also proposes special offer information for the event. For example, it displays special offers and discount information offered to participants. When the generation AI summarizes, the summary section also proposes a combination of the detailed event schedule and special offer information. For example, it can display the schedule and special offer information in a single view, making it easy for users to check. This makes it easier for users to grasp the overall picture of the event by including detailed schedules and special offer information in its proposals.

[0056] The summarization unit can reflect the feedback and ratings of past participants. For example, it can incorporate participants' reviews and comments into the summary. The summarization unit also reflects the rating scores of past participants in the information summarized by the generation AI. For example, it can prioritize events with high rating scores. The summarization unit also reflects the feedback and rating scores of past participants in the information summarized by the generation AI. For example, it can select events based on their feedback and rating scores and reflect them in the summary. In this way, by reflecting the feedback and ratings of past participants, it is possible to provide users with highly reliable information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The information gathering department collects information from social media platforms, such as Twitter, Facebook, and Instagram, about the event. Step 2: The screening unit screens the information collected by the information collection unit, for example, evaluating the reliability of the collected information and selecting highly reliable information. Step 3: The summarization unit summarizes and organizes the information screened by the screening unit. For example, the generation AI uses a summarization algorithm to concisely summarize the information. Step 4: The suggestion unit suggests the best schedule for the user based on the information summarized and organized by the summarization unit. For example, it suggests events based on the user's preferences and schedule. Step 5: The review provider provides the review information. For example, it analyzes the review information collected by the generation AI and provides it to the user. Step 6: The map linking unit links with map data to propose a time schedule including transportation options. For example, it uses map data from Google Maps or OpenStreetMap to propose the optimal transportation options. Step 7: The personal data analysis unit analyzes the user's personal data and makes suggestions that match the user's preferences. For example, it suggests events based on the user's past event participation history and interests.

[0059] (Example 2) The event suggestion system according to the embodiment of the present invention is a system that suggests optimal events by utilizing a user's location information and personal data, thereby enabling the event suggestion system to efficiently suggest events that match the user's preferences and schedule.

[0060] An event proposal system according to an embodiment includes an information collection unit, a screening unit, a summarization unit, a proposal unit, a review provision unit, a map linkage unit, and a personal data analysis unit. The information collection unit collects information from social media platforms. For example, it collects information about events from social media platforms such as Twitter, Facebook, and Instagram. The screening unit screens the information collected by the information collection unit. For example, it evaluates the reliability of the collected information and selects highly reliable information. The summarization unit summarizes and organizes the information screened by the screening unit. For example, a generation AI uses a summarization algorithm to concisely summarize the information. The proposal unit proposes optimal plans to a user based on the information summarized and organized by the summarization unit. For example, it proposes events based on the user's preferences and schedule. The review provision unit provides review information. For example, it analyzes the review information collected by the generation AI and provides it to the user. The map linkage unit proposes a time schedule including transportation options in conjunction with map data. For example, it proposes optimal transportation options using map data such as Google Maps and OpenStreetMap. The personal data analysis unit analyzes the user's personal data and makes proposals that match the user's preferences. For example, events are suggested based on the user's past event participation history, interests, etc. This allows the event suggestion system according to the embodiment to efficiently suggest events that match the user's preferences and schedule.

[0061] The screening unit can prioritize suggesting events with many positive reviews about a particular event. For example, the screening unit refers to past success rate data for the event information collected by the generation AI from social media, and prioritizes suggesting events with a high success rate. For example, it prioritizes displaying information about events that have received high ratings in the past. The screening unit also analyzes participant satisfaction data for the event information collected by the generation AI from social media, and prioritizes suggesting events with high satisfaction. For example, it selects events based on participant reviews and evaluation scores. The screening unit also evaluates the event information collected by the generation AI from social media by combining past success rates and participant satisfaction to suggest events with high reliability. For example, it prioritizes displaying events with both a high success rate and high satisfaction. This allows for improved user satisfaction by prioritizing suggesting events with many positive reviews.

[0062] The screening unit can improve reliability by evaluating events based on their past success rates and participant satisfaction. For example, the screening unit refers to the organizer's past performance data for the event information collected by the generation AI from social media, and prioritizes suggesting events from highly reliable organizers. For example, it prioritizes displaying information about organizers who have held many successful events in the past. The screening unit also analyzes the organizer's reliability data for the event information collected by the generation AI from social media, and prioritizes suggesting events from highly reliable organizers. For example, it selects events based on the organizer's evaluation score and reviews. The screening unit also evaluates the event information collected by the generation AI from social media, combining the organizer's past performance and reliability, and suggests highly reliable events. For example, it prioritizes displaying events from organizers with both high performance and reliability. This makes it possible to suggest highly reliable events by evaluating based on past success rates and participant satisfaction.

[0063] The screening unit can use the emotion estimation function to analyze a user's emotion toward an event from posts on the SNS and preferentially suggest events that are associated with a large number of positive emotions. For example, the screening unit can use the emotion estimation function to analyze a user's emotion toward an event from posts on the SNS and preferentially suggest events that are associated with a large number of positive emotions. For example, events that are associated with a large number of emotions such as joy and excitement can be preferentially displayed. The screening unit can also use the emotion estimation function to calculate a user's emotion score toward an event from posts on the SNS and preferentially suggest events that have a high positive emotion score. For example, events that have a high emotion score can be preferentially displayed. The screening unit can also use the emotion estimation function to analyze a user's emotion trend toward an event from posts on the SNS and preferentially suggest events that have a high positive emotion trend. For example, events that have a continuing positive emotion trend can be preferentially displayed. This can improve user satisfaction by preferentially suggesting events that have a high number of positive emotions.

[0064] The information collection unit can also collect information from online forums and blogs other than SNS, and the screening unit can integrate and screen that information. For example, the information collection unit allows the generation AI to collect event information from online forums and blogs other than SNS, integrate that information, and screen it. For example, it analyzes forum posts and blog articles to select highly reliable information. The information collection unit also integrates the information collected by the generation AI from online forums and blogs other than SNS with the information collected from SNS and performs a comprehensive evaluation. For example, it selects events based on data from multiple information sources. The information collection unit also compares the information collected by the generation AI from online forums and blogs other than SNS with the information collected from SNS, and preferentially suggests matching information. For example, it preferentially displays events that match across multiple information sources. This allows information to be collected from information sources other than SNS, integrated, and screened, thereby enabling more reliable event suggestions.

[0065] The information collection unit collects information from social media platforms in different languages ​​and cultures, and the screening unit can screen from a global perspective. For example, the generation AI collects event information from social media platforms in different languages ​​and cultures and screens it from a global perspective. For example, the information collection unit collects information from social media platforms in English, Chinese, Spanish, etc., integrates it, and evaluates it. The information collection unit also integrates the information collected by the generation AI from social media platforms in different languages ​​and cultures using language translation technology and performs a comprehensive evaluation. For example, it selects events based on the translated information. The information collection unit also screens the information collected by the generation AI from social media platforms in different languages ​​and cultures, taking cultural background into consideration. For example, it prioritizes suggesting culturally significant events and trends. This allows for the collection of information from different languages ​​and cultures and screening it from a global perspective, making it possible to suggest a wider variety of events.

[0066] The information collection unit can use the emotion estimation function to analyze the emotions of a user when posting to an SNS in real time and provide feedback to elicit positive emotions. For example, the information collection unit uses the emotion estimation function to analyze the emotions of a user when posting to an SNS in real time and provide feedback to elicit positive emotions. For example, the information collection unit displays positive comments and encouraging messages. The information collection unit also uses the emotion estimation function to calculate an emotion score when a user posts to an SNS and provides positive feedback if the positive emotion score is low. For example, the information collection unit makes positive suggestions for posts with low emotion scores. The information collection unit also uses the emotion estimation function to analyze the emotion trend of a user when posting to an SNS and provides feedback to maintain the positive emotion trend. For example, the information collection unit displays encouraging messages to maintain the positive emotion trend. In this way, by providing feedback to elicit positive emotions from the user, the user experience on the SNS can be improved.

[0067] The summary section can also propose a detailed event schedule and special offer information. For example, when the generation AI summarizes, the summary section proposes a detailed event schedule. For example, it displays the event start time, end time, and the timetable for each session in detail. The summary section also proposes special offer information for the event when the generation AI summarizes. For example, it displays special offers and discount information offered to participants. The summary section also proposes a detailed event schedule and special offer information in combination when the generation AI summarizes. For example, it displays the schedule and special offer information in a single view so that the user can easily check them. This makes it easier for the user to grasp the overall picture of the event by proposing a detailed schedule and special offer information.

[0068] The summarization unit can reflect the feedback and ratings of past participants. For example, the summarization unit reflects the feedback of past participants in the information summarized by the generation AI. For example, it incorporates participant reviews and comments into the summary. The summarization unit also reflects the rating scores of past participants in the information summarized by the generation AI. For example, it prioritizes suggesting events with high rating scores. The summarization unit also reflects a combination of the feedback and rating scores of past participants in the information summarized by the generation AI. For example, it selects events based on the feedback and rating scores and reflects them in the summary. In this way, by reflecting the feedback and ratings of past participants, it is possible to provide users with highly reliable information.

[0069] The summarization unit can use the emotion estimation function to suggest an optimal event based on the user's current emotional state. For example, the summarization unit uses the emotion estimation function to analyze the user's current emotional state and suggest an optimal event based on that emotional state. For example, a relaxation event is suggested for a user who wants to relax. The summarization unit also uses the emotion estimation function to calculate the user's emotion score and suggest an optimal event based on that score. For example, an active event is suggested for a user with a high emotion score. The summarization unit also uses the emotion estimation function to analyze the user's emotional trend and suggest an optimal event based on that trend. For example, a fun event is suggested for a user who has a continuing positive emotional trend. In this way, by suggesting an optimal event based on the user's current emotional state, user satisfaction can be improved.

[0070] The summarization unit can provide the summarized information as visual notes or infographics to make it easier to understand visually. For example, the summarization unit may provide the information summarized by the generation AI as visual notes to make it easier to understand visually. For example, it may show important points using diagrams or icons. The summarization unit may also provide the information summarized by the generation AI as infographics to make it easier to understand visually. For example, it may display data or statistical information in graphs or charts. The summarization unit may also provide the information summarized by the generation AI as visual notes or infographics to make it easier to understand visually. For example, it may complement the summary text with diagrams, icons, and graphs. This can help the user's understanding by providing information in a format that is easy to understand visually.

[0071] The summarizing unit can optimize the summarized information for different devices (smartphones, tablets, PCs) and display it accordingly. For example, the summarizing unit may optimize the information summarized by the generation AI for smartphones and display it accordingly. For example, it may adjust the layout to fit the screen size of a smartphone. The summarizing unit may also optimize the information summarized by the generation AI for tablets and display it accordingly. For example, it may adjust the layout to fit the screen size of a tablet. The summarizing unit may also optimize the information summarized by the generation AI for PCs and display it accordingly. For example, it may adjust the layout to fit the screen size of a PC. This allows the information to be displayed in an optimized manner for different devices, thereby improving user convenience.

[0072] The summarization unit can use the emotion estimation function to predict the emotion the user will have toward the proposed event and provide additional information to elicit positive emotions. For example, the summarization unit uses the emotion estimation function to predict the emotion the user will have toward the proposed event and provide additional information to elicit positive emotions. For example, the summarization unit displays information that emphasizes the appeal of the event. The summarization unit also uses the emotion estimation function to calculate an emotion score the user will have toward the proposed event and provides additional information if the positive emotion score is low. For example, the summarization unit displays information about special offers and discounts for the event. The summarization unit also uses the emotion estimation function to analyze the emotion trend the user will have toward the proposed event and provides additional information to maintain the positive emotion trend. For example, the summarization unit displays positive reviews from past participants. This allows the user to feel positive emotions toward the proposed event by providing additional information, thereby improving user satisfaction.

[0073] The review providing unit can analyze review information and extract event features based on specific keywords and phrases. In the review providing unit, for example, the generation AI analyzes review information and extracts event features based on specific keywords and phrases. For example, event features are identified based on keywords such as "fun," "family-friendly," and "music." In addition, the review providing unit analyzes review information and extracts frequently occurring phrases to identify event features. For example, event features are identified based on phrases such as "great performance" and "delicious food." In addition, the review providing unit analyzes review information and extracts positive keywords and phrases to identify event features. For example, event features are identified based on positive phrases such as "great," "fun," and "I want to go again." In this way, useful information can be provided to users by extracting event features based on specific keywords and phrases.

[0074] The review providing unit classifies the review information based on the age group and interests of the participants, and can provide the user with the most suitable information. For example, the generation AI in the review providing unit analyzes the review information and classifies it based on the age group of the participants. For example, it identifies events for young people and events for seniors and provides the user with the most suitable information. The generation AI in the review providing unit also analyzes the review information and classifies it based on the interests of the participants. For example, it provides event information according to interests such as music lovers, sports lovers, and art lovers. The generation AI in the review providing unit also analyzes the review information and classifies it based on both age group and interests. For example, it provides word-of-mouth information about music events to a young user who loves music. In this way, by classifying the review information based on the age group and interests of the participants, the most suitable information can be provided to the user.

[0075] The word-of-mouth providing unit can use the emotion estimation function to analyze the emotional tone of the word-of-mouth information and preferentially display positive word-of-mouth. The word-of-mouth providing unit, for example, uses the emotion estimation function to analyze the emotional tone of the word-of-mouth information and preferentially display positive word-of-mouth. For example, it preferentially displays word-of-mouth that express joy or satisfaction. The word-of-mouth providing unit also uses the emotion estimation function to calculate an emotion score of the word-of-mouth information and preferentially display word-of-mouth with a high positive emotion score. For example, it preferentially displays word-of-mouth with a high emotion score. The word-of-mouth providing unit also uses the emotion estimation function to analyze an emotion trend of the word-of-mouth information and preferentially display word-of-mouth that continues to have a positive emotion trend. For example, it preferentially displays word-of-mouth that continues to have a positive emotion trend. In this way, by preferentially displaying positive word-of-mouth, it is possible to provide useful information to the user.

[0076] The review providing unit can automatically translate review information into different languages ​​and provide feedback from an international perspective. For example, the generation AI automatically translates review information into different languages ​​and provides feedback from an international perspective. For example, the review providing unit translates and displays the information into languages ​​such as English, Chinese, and Spanish. The review providing unit also analyzes the review information translated into different languages ​​by the generation AI and provides feedback from an international perspective. For example, the review providing unit evaluates an event based on feedback from users from different cultural backgrounds. The review providing unit also integrates the review information translated into different languages ​​by the generation AI and performs a comprehensive evaluation. For example, the review providing unit prioritizes displaying positive reviews that match in multiple languages. This allows feedback from an international perspective to be provided by automatically translating into different languages.

[0077] The word-of-mouth providing unit can provide the word-of-mouth information as audio data, allowing the user to obtain the information aurally. For example, the word-of-mouth providing unit has the generation AI provide the word-of-mouth information as audio data, allowing the user to obtain the information aurally. For example, it converts text data into audio data using speech synthesis technology. The word-of-mouth providing unit also optimizes the word-of-mouth information provided by the generation AI as audio data for playback on the user's device. For example, it plays the audio data on a smartphone or tablet. The word-of-mouth providing unit also customizes the word-of-mouth information provided by the generation AI as audio data to suit the user's preferences. For example, it adjusts the tone and speed of the audio before playback. In this way, the word-of-mouth information can be provided as audio data, allowing the user to obtain the information aurally.

[0078] The review providing unit can use the emotion estimation function to collect users' emotional reactions to word-of-mouth information and optimize the display order of the reviews based on the collected emotional reactions. The review providing unit, for example, uses the emotion estimation function to collect users' emotional reactions to word-of-mouth information and optimize the display order of the reviews based on the collected emotional reactions. For example, reviews with a high number of positive emotional reactions are preferentially displayed. The review providing unit also uses the emotion estimation function to calculate a user's emotion score and optimize the display order of the reviews based on the score. For example, reviews with a high emotion score are preferentially displayed. The review providing unit also uses the emotion estimation function to analyze a user's emotional trend and optimize the display order of the reviews based on the trend. For example, reviews with a continuing positive emotional trend are preferentially displayed. In this way, by optimizing the display order of the reviews based on the user's emotional reaction, it is possible to provide useful information to the user.

[0079] The map linking unit can analyze map data and propose the optimal means of transportation taking into account traffic conditions and weather information around the event venue. For example, the generation AI in the map linking unit analyzes map data and proposes the optimal means of transportation taking into account traffic conditions around the event venue. For example, it selects a means of transportation based on traffic congestion and the operation status of public transportation. The generation AI in the map linking unit also analyzes map data and proposes the optimal means of transportation taking into account weather information around the event venue. For example, it selects a means of transportation based on a weather forecast. The generation AI in the map linking unit also analyzes map data and proposes the optimal means of transportation by combining traffic conditions and weather information. For example, it selects a means of transportation based on traffic congestion and a weather forecast. This makes it possible to make the user's travel more efficient by proposing the optimal means of transportation taking into account traffic conditions and weather information.

[0080] The map linking unit can also suggest tourist spots and restaurants around the event venue based on the map data. For example, the generation AI in the map linking unit analyzes the map data and suggests tourist spots around the event venue. For example, it displays tourist spots that can be visited before or after the event. The generation AI in the map linking unit also analyzes the map data and suggests restaurants around the event venue. For example, it displays restaurants and cafes where you can enjoy a meal before or after the event. The generation AI in the map linking unit also analyzes the map data and suggests tourist spots and restaurants in combination. For example, it displays tourist spots and nearby restaurants together. This makes it possible to enrich the user's event experience by suggesting tourist spots and restaurants around the event venue.

[0081] The map linking unit can use the emotion estimation function to analyze the emotional state of the user while traveling and suggest transportation means to reduce stress. The map linking unit, for example, uses the emotion estimation function to analyze the emotional state of the user while traveling and suggest transportation means to reduce stress. For example, it suggests transportation means that allow relaxation. The map linking unit also uses the emotion estimation function to calculate the user's emotion score and suggest transportation means to reduce stress. For example, it suggests transportation means that allow relaxation when the emotion score is low. The map linking unit also uses the emotion estimation function to analyze the user's emotion trend and suggest transportation means to reduce stress. For example, it suggests transportation means to maintain a positive emotion trend. In this way, by analyzing the user's emotional state while traveling and suggesting transportation means to reduce stress, it is possible to improve the user's travel experience.

[0082] The map linking unit can display map data on a 3D map or AR (augmented reality) to enable the user to intuitively understand the route. For example, the map linking unit causes the generation AI to display map data on a 3D map to enable the user to intuitively understand the route. For example, the map linking unit visually displays the route on a 3D map. Furthermore, the map linking unit causes the generation AI to display map data in AR (augmented reality) to enable the user to intuitively understand the route. For example, the map linking unit displays the route through a smartphone camera. Furthermore, the map linking unit causes the generation AI to display map data on a 3D map or AR to enable the user to intuitively understand the route. For example, the map linking unit combines 3D maps and AR to display the route. In this way, by displaying the route on a 3D map or AR, the user can intuitively understand the route.

[0083] The map linking unit links map data with real-time information from public transportation and can suggest the optimal means of transportation. In the map linking unit, for example, the generation AI links map data with real-time information from public transportation and suggests the optimal means of transportation. For example, it selects a means of transportation based on the operation status of trains and buses. In addition, the map linking unit links map data with real-time information from public transportation and suggests means of transportation that avoid traffic congestion. For example, it displays a route that avoids traffic congestion. In addition, the map linking unit links map data with real-time information from public transportation and suggests the optimal means of transportation and route. For example, it displays a route by combining the operation status of trains and buses with traffic congestion information. In this way, by linking with real-time information from public transportation, the optimal means of transportation can be suggested.

[0084] The map linking unit can use the emotion estimation function to monitor the emotions felt by the user while traveling in real time and suggest music or podcasts that will elicit positive emotions. For example, the map linking unit can use the emotion estimation function to monitor the emotions felt by the user while traveling in real time and suggest music that will elicit positive emotions. For example, the map linking unit can play relaxing music. The map linking unit can also use the emotion estimation function to calculate an emotion score felt by the user while traveling and suggest podcasts that will elicit positive emotions. For example, if the emotion score is low, the map linking unit can play an enjoyable podcast. The map linking unit can also use the emotion estimation function to analyze the emotion trend felt by the user while traveling and suggest music or podcasts that will elicit positive emotions. For example, the map linking unit can play music or podcasts that will maintain a positive emotion trend. In this way, the user's traveling experience can be improved by monitoring the emotions felt by the user while traveling in real time and suggesting music or podcasts that will elicit positive emotions.

[0085] The personal data analysis unit can also suggest events in new genres based on the user's interests, thereby broadening the user's interests. In the personal data analysis unit, for example, the generation AI analyzes the user's interests and suggests events in new genres. For example, it suggests art events to a user who likes music. In addition, the personal data analysis unit can analyze the user's interests and suggest related events in new genres. For example, it suggests outdoor events to a user who likes sports. In addition, the personal data analysis unit can analyze the user's interests and suggest events in new genres to broaden the user's interests. For example, it suggests theater events to a user who likes movies. In this way, it is possible to broaden the user's interests by suggesting events in new genres based on the user's interests.

[0086] The personal data analysis unit can use the emotion estimation function to suggest an optimal event based on the user's current emotional state. For example, the personal data analysis unit uses the emotion estimation function to analyze the user's current emotional state and suggest an optimal event based on that emotional state. For example, a relaxation event is suggested for a user who wants to relax. The personal data analysis unit also uses the emotion estimation function to calculate the user's emotion score and suggest an optimal event based on that score. For example, an active event is suggested for a user with a high emotion score. The personal data analysis unit also uses the emotion estimation function to analyze the user's emotional trend and suggest an optimal event based on that trend. For example, a fun event is suggested for a user who has a continuing positive emotional trend. In this way, by suggesting an optimal event based on the user's current emotional state, user satisfaction can be improved.

[0087] The personal data analysis unit can link the user's personal data with different devices and platforms to make integrated event suggestions. For example, the generation AI in the personal data analysis unit links the user's personal data with different devices such as smartphones, tablets, and PCs to make integrated event suggestions. For example, it synchronizes data between devices to make consistent suggestions. The personal data analysis unit also links the user's personal data with different platforms such as social media and email to make integrated event suggestions. For example, it suggests events based on social media data. The personal data analysis unit also links the user's personal data with different devices and platforms to integrate the data and make event suggestions. For example, it suggests events based on data from multiple devices and platforms. This makes it possible to make integrated event suggestions by linking different devices and platforms.

[0088] The personal data analysis unit can integrate the user's personal data with data on friends and family to suggest events that can be enjoyed as a group. For example, the generation AI in the personal data analysis unit can integrate the user's personal data with data on friends and family to suggest events that can be enjoyed as a group. For example, it can suggest events that the whole family can enjoy. Furthermore, the generation AI in the personal data analysis unit can integrate the user's personal data with data on friends to suggest events based on common interests. For example, it can suggest events that all friends are interested in. Furthermore, the generation AI in the personal data analysis unit can integrate the user's personal data with data on family members to suggest events that fit the schedules of all family members. For example, it can suggest events with dates and times that all family members can attend. In this way, by integrating data on friends and family, it can suggest events that can be enjoyed as a group.

[0089] The personal data analysis unit can use the emotion estimation function to suggest relaxing events or active events based on the user's emotional state. For example, the personal data analysis unit uses the emotion estimation function to analyze the user's emotional state and suggest relaxing events. For example, it suggests relaxation events or yoga classes. The personal data analysis unit also uses the emotion estimation function to calculate the user's emotion score and suggest active events. For example, it suggests sporting events or outdoor activities. The personal data analysis unit also uses the emotion estimation function to analyze the user's emotional trend and suggest relaxing events or active events based on the trend. For example, it selects events according to the emotional trend. In this way, it is possible to improve user satisfaction by suggesting relaxing events or active events based on the user's emotional state.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The information collection unit can suggest similar events based on the user's past event participation history. For example, a similar music event can be suggested to a user who has previously attended a music festival. The information collection unit can also suggest events in a new genre based on the user's interests. For example, an outdoor event can be suggested to a user who likes sports. Furthermore, the information collection unit can suggest the most suitable date and time for an event to suit the user's schedule. This enables more personalized event suggestions based on the user's past participation history and interests.

[0092] The screening department can evaluate the environmental impact of an event and prioritize eco-friendly events. For example, it can select events that use renewable energy or have recycling programs. The screening department can also evaluate the social impact of an event and prioritize events that contribute to the community. For example, it can select events that support local artists or charity events. Furthermore, the screening department can evaluate the safety of an event and prioritize events that have solid safety measures. This makes it possible to recommend events that take into account the environment, social impact, and safety.

[0093] The screening unit can suggest appropriate events taking into account the user's health condition. For example, it can suggest allergen-free events to a user with allergies. The screening unit can also suggest events with an appropriate amount of exercise based on the user's fitness level. For example, it can select yoga classes for beginners and marathon events for advanced runners. Furthermore, the screening unit can suggest relaxation events or stress relief events taking into account the user's mental health. This makes it possible to suggest events according to the user's health condition.

[0094] The screening unit can use the emotion estimation function to optimize event suggestions based on the user's emotional state. For example, a relaxation event can be suggested to a user who is feeling stressed. The screening unit can also use the emotion estimation function to calculate a user's emotion score and select events based on that score. For example, an active event can be suggested to a user with a high emotion score. Furthermore, the screening unit can use the emotion estimation function to analyze a user's emotional trend and suggest events based on that trend. This makes it possible to suggest events based on the user's emotional state.

[0095] The information collection unit can suggest similar events based on the user's past event participation history. For example, a similar music event can be suggested to a user who has previously attended a music festival. The information collection unit can also suggest events in a new genre based on the user's interests. For example, an outdoor event can be suggested to a user who likes sports. Furthermore, the information collection unit can suggest the most suitable date and time for an event to suit the user's schedule. This enables more personalized event suggestions based on the user's past participation history and interests.

[0096] The information gathering unit collects information from social media platforms in different languages ​​and cultures, allowing the screening unit to screen from a global perspective. For example, it collects, integrates, and evaluates information from social media platforms in English, Chinese, Spanish, and other languages. The information gathering unit also integrates and comprehensively evaluates the information collected by the generation AI from social media platforms in different languages ​​and cultures using language translation technology. For example, it selects events based on the translated information. The information gathering unit also screens the information collected by the generation AI from social media platforms in different languages ​​and cultures, taking cultural background into consideration. For example, it prioritizes the suggestion of culturally significant events and trends. This allows the system to collect information from different languages ​​and cultures and screen it from a global perspective, making it possible to suggest a wider variety of events.

[0097] The information collection unit uses the emotion estimation function to analyze the emotions of users when they post to the SNS in real time, and can provide feedback to elicit positive emotions. For example, it displays positive comments and encouraging messages. The information collection unit also uses the emotion estimation function to calculate an emotion score when a user posts to the SNS, and provides positive feedback if the positive emotion score is low. For example, it makes positive suggestions for posts with low emotion scores. The information collection unit also uses the emotion estimation function to analyze the emotion trend of a user when they post to the SNS, and provides feedback to maintain the positive emotion trend. For example, it displays encouraging messages to maintain the positive emotion trend. In this way, by providing feedback to elicit positive emotions from users, it is possible to improve the user experience on the SNS.

[0098] The summary section can also propose detailed event schedules and special offer information. For example, it displays the start and end times of the event, the timetable for each session, and other details. When the generation AI summarizes, the summary section also proposes special offer information for the event. For example, it displays special offers and discount information offered to participants. When the generation AI summarizes, the summary section also proposes a combination of the detailed event schedule and special offer information. For example, it can display the schedule and special offer information in a single view, making it easy for users to check. This makes it easier for users to grasp the overall picture of the event by including detailed schedules and special offer information in its proposals.

[0099] The summarization unit can reflect the feedback and ratings of past participants. For example, it can incorporate participants' reviews and comments into the summary. The summarization unit also reflects the rating scores of past participants in the information summarized by the generation AI. For example, it can prioritize events with high rating scores. The summarization unit also reflects the feedback and rating scores of past participants in the information summarized by the generation AI. For example, it can select events based on their feedback and rating scores and reflect them in the summary. In this way, by reflecting the feedback and ratings of past participants, it is possible to provide users with highly reliable information.

[0100] The summarization unit can use the emotion estimation function to suggest optimal events based on the user's current emotional state. For example, a relaxation event is suggested for a user who wants to relax. The summarization unit also uses the emotion estimation function to calculate the user's emotion score and suggest optimal events based on that score. For example, an active event is suggested for a user with a high emotion score. The summarization unit also uses the emotion estimation function to analyze the user's emotional trend and suggest optimal events based on that trend. For example, a fun event is suggested for a user who has a continuing positive emotional trend. In this way, user satisfaction can be improved by suggesting optimal events based on the user's current emotional state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The information gathering department collects information from social media platforms, such as Twitter, Facebook, and Instagram, about the event. Step 2: The screening unit screens the information collected by the information collection unit, for example, evaluating the reliability of the collected information and selecting highly reliable information. Step 3: The summarization unit summarizes and organizes the information screened by the screening unit. For example, the generation AI uses a summarization algorithm to concisely summarize the information. Step 4: The suggestion unit suggests the best schedule for the user based on the information summarized and organized by the summarization unit. For example, it suggests events based on the user's preferences and schedule. Step 5: The review provider provides the review information. For example, it analyzes the review information collected by the generation AI and provides it to the user. Step 6: The map linking unit links with map data to propose a time schedule including transportation options. For example, it uses map data from Google Maps or OpenStreetMap to propose the optimal transportation options. Step 7: The personal data analysis unit analyzes the user's personal data and makes suggestions that match the user's preferences. For example, it suggests events based on the user's past event participation history and interests.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 information from SNS, a screening unit that screens the information collected by the information collecting unit; a summarizing unit that summarizes and organizes the information screened by the screening unit; a suggestion unit that suggests an optimal schedule to a user based on the information summarized and organized by the summarization unit; A review section that provides review information; A map linking unit that links with map data to propose a time schedule including transportation means; A personal data analysis unit that analyzes the user's personal data and makes suggestions that suit the user's preferences. A system characterized by:

2. The screening unit If a particular event has many positive reviews, we'll prioritize it as a recommendation.

2. The system of claim 1.

3. The screening unit Increase credibility by evaluating your event based on past success and attendee satisfaction.

2. The system of claim 1.

4. The screening unit Analyzing the user's feelings about the event from the posts on the SNS and preferentially suggesting the event with a high number of positive feelings 2. The system of claim 1.

5. The information collecting unit We collect the information from online forums and blogs other than the SNS. The screening unit The above information is integrated and screened.

2. The system of claim 1.

6. The information collecting unit Collecting the information from SNS in different languages ​​and cultures, The screening unit Screening from a global perspective 2. The system of claim 1.

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