System

Abstract

An object of a system according to an embodiment is to propose an event or a circle based on a user's interest or hobby and to easily make a reservation for participation.SOLUTION: A system according to an embodiment includes a search unit, a suggestion unit, and a reservation unit. The search unit searches for an event or a circle based on the user's interest or hobby. The suggestion unit analyzes a conversation of the user based on the information collected by the search unit, and suggests an event or a circle. The reservation unit reserves participation in the event or the circle proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, information about events and clubs that users participate in on holidays is scattered, making it difficult to make appropriate selections and reservations.

[0005] The system according to the embodiment aims to suggest events and circles based on the user's interests and hobbies, and to make it easy to make reservations for participation. [Means for solving the problem]

[0006] The system according to the embodiment includes a search unit, a suggestion unit, and a reservation unit. The search unit searches for events and circles based on the user's interests and hobbies. The suggestion unit analyzes the user's conversations based on the information collected by the search unit and suggests events and circles. The reservation unit makes reservations to participate in the events and circles suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment suggests events and circles based on the user's interests and hobbies, and allows the user to easily make reservations to participate. [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) An information platform system according to an embodiment of the present invention searches for events and groups based on a user's interests and hobbies, and a generation AI analyzes the user's conversation to suggest appropriate events and groups and allow the user to make a reservation. The information platform system allows users to easily experience new things and build relationships by allowing them to search for events and groups based on their interests and hobbies, and then the generation AI analyzes the user's conversation to suggest appropriate events and groups and allow the user to make a reservation. For example, the information platform system displays related events and groups when a user simply enters a keyword. For example, entering keywords such as "yoga" or "cooking class" displays a list of related events and groups. The information platform system then uses the generation AI to analyze the user's conversation and suggest appropriate events and groups. For example, if a user says, "I want to do something fun this weekend," the generation AI analyzes the conversation and suggests events and groups that match the user's interests. The information platform system then displays details of the suggested events and groups, allowing the user to make a reservation with one click. This allows users to easily enjoy new experiences. The information platform system thus centralizes and provides information to help users spend their holidays more meaningfully, making it easier for them to find friends and places that share their hobbies and interests. Furthermore, by utilizing generative AI, appropriate events and circles can be suggested based on user conversations, allowing for a smooth process of booking participation. This allows the information platform system to provide users with centralized information to help them spend their holidays meaningfully, making it easier for them to find friends and places that share their hobbies and interests. Furthermore, by utilizing generative AI, appropriate events and circles can be suggested based on user conversations, allowing for a smooth process of booking participation.

[0029] An information platform system according to an embodiment includes a search unit, a suggestion unit, and a reservation unit. The search unit searches for events and clubs based on a user's interests and hobbies. For example, the search unit searches for related events and clubs based on keywords entered by the user. The search unit can also set filtering conditions to narrow search results. For example, when a user enters keywords such as "yoga" or "cooking class," the search unit displays a list of related events and clubs. The search unit can also analyze the user's past search history and select an optimal search algorithm. The suggestion unit uses a generation AI to analyze the user's conversation based on information collected by the search unit and suggest appropriate events and clubs. For example, when a user says, "I want to do something fun this weekend," the suggestion unit analyzes the conversation and suggests events and clubs that match the user's interests. The suggestion unit can also collect feedback based on information obtained from the user's conversation to be used in future suggestions. For example, the suggestion unit analyzes the content of the user's conversation and collects data to be reflected in future suggestions. The reservation unit makes reservations for participation in events and circles suggested by the suggestion unit. The reservation unit, for example, displays details of the suggested events and circles, allowing the user to make a reservation for participation with one click. The reservation unit can also analyze the user's past reservation history and select the optimal reservation method. For example, the reservation unit suggests the optimal reservation method based on the reservation methods used by the user in the past. This allows the information platform system according to the embodiment to search for, suggest, and make reservations for events and circles based on the user's interests and hobbies.

[0030] The search unit can search for related events and circles based on keywords entered by the user. For example, the search unit searches for related events and circles based on keywords entered by the user. For example, when a user enters keywords such as "yoga" or "cooking class," the search unit displays a list of related events and circles. The search unit can also set filtering conditions to narrow down search results. For example, the search unit narrows down search results by setting conditions such as the date, time, location, and participation fee of the event. Furthermore, the search unit can analyze the user's past search history and select the optimal search algorithm. For example, the search unit prioritizes displaying related events and circles based on keywords searched for by the user in the past. This makes it possible to search for related events and circles based on the keywords entered by the user.

[0031] The suggestion unit can analyze a user's conversation and suggest events and circles that match the user's interests. The suggestion unit uses a generation AI to analyze a user's conversation and suggest events and circles that match the user's interests. For example, when a user has a conversation such as, "I want to do something fun this weekend," the suggestion unit analyzes the conversation and suggests events and circles that match the user's interests. The suggestion unit can also collect feedback based on information obtained from the user's conversation to be used in future suggestions. For example, the suggestion unit analyzes the content of the user's conversation and collects data to be reflected in future suggestions. Furthermore, the suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion unit improves the accuracy of suggestions based on feedback from events and circles the user has participated in in the past. This makes it possible to analyze a user's conversation and suggest events and circles that match the user's interests.

[0032] The reservation unit displays details of proposed events and circles, allowing users to make reservations for participation. The reservation unit displays details of proposed events and circles, allowing users to make reservations for participation with one click. For example, the reservation unit displays details such as the date, time, location, and participation fee of proposed events and circles. The reservation unit can also analyze the user's past reservation history and select the optimal reservation method. For example, the reservation unit can suggest the optimal reservation method based on the reservation methods used by the user in the past. Furthermore, the reservation unit can reflect user feedback to improve the reservation method. For example, the reservation unit can improve the reservation method based on feedback provided by the user. This allows users to display details of proposed events and circles, allowing them to make reservations for participation with one click.

[0033] The suggestion unit can collect feedback to be used in the next suggestion based on information obtained from the user's conversation. The suggestion unit uses a generative AI to collect feedback to be used in the next suggestion based on information obtained from the user's conversation. For example, the suggestion unit analyzes the content of the user's conversation and collects data to be reflected in the next suggestion. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion based on feedback from events and circles that the user has previously participated in. Furthermore, the suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit makes suggestions using calmer expressions. This makes it possible to collect feedback to be used in the next suggestion based on information obtained from the user's conversation.

[0034] The search unit can analyze the user's past search history and select the optimal search algorithm. For example, the search unit can prioritize displaying related events and circles based on keywords the user has previously searched for. For example, the search unit can identify categories of interest from the user's past search history and display events and circles related to those categories. For example, the search unit can analyze the user's past search history and display the most relevant events and circles. This allows the user's past search history to be analyzed and the optimal search algorithm to be selected. The search algorithm can be realized using technologies such as TF-IDF and PageRank. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or without AI. For example, the search unit can input the user's past search history data into a generation AI and have the generation AI select the optimal search algorithm.

[0035] The search unit can perform filtering based on the user's current living situation and areas of interest during a search. For example, when the user inputs their current living situation, the search unit displays events and circles that match that situation. The search unit can also filter and display related events and circles based on the user's areas of interest, for example. The search unit can also display optimal events and circles taking into account the user's current living situation and areas of interest, for example. This allows filtering based on the user's current living situation and areas of interest. Living situations include information such as occupation, family environment, and health status. Areas of interest include information such as hobbies and research topics. Some or all of the above-described processing in the search unit can be performed using, or without, AI. For example, the search unit can input data on the user's living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0036] The search unit can select a search method according to the user's input method during a search. For example, when a user searches by voice, the search unit uses voice recognition technology to display optimal search results. For example, when a user searches by text, the search unit can also display optimal search results based on the input keywords. For example, when a user searches by image, the search unit can also display optimal search results using image recognition technology. This makes it possible to select an optimal search method according to the user's input method. Input methods include, for example, voice input, text input, and image input. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's input data into a generation AI and have the generation AI select the optimal search method.

[0037] During a search, the search unit can prioritize displaying highly relevant events and circles by taking into account the user's geographical location information. For example, the search unit can prioritize displaying events and circles held nearby based on the user's current location. For example, the search unit can also display events and circles that are easily accessible based on the user's geographical location information. For example, the search unit can also display optimal events and circles by taking into account the user's geographical location information. This allows highly relevant events and circles to be prioritized by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data, IP address, etc. Some or all of the above-described processing in the search unit may be performed using, or without, AI. For example, the search unit can input the user's geographical location information data into a generation AI and cause the generation AI to display highly relevant events and circles.

[0038] The search unit can analyze the user's social media activity during a search and display related events and circles. For example, the search unit can analyze the content of the user's social media posts and display related events and circles. For example, the search unit can also display related events and circles based on the activity of the user's friends on social media. For example, the search unit can display related events and circles based on the user's social media check-in information. In this way, the user's social media activity can be analyzed and related events and circles can be displayed. Social media activity includes information such as the content of posts and the number of likes. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's social media activity data into a generation AI and cause the generation AI to display related events and circles.

[0039] The search unit can customize the search method by reflecting the user's past feedback during a search. The search unit customizes search results based on, for example, feedback provided by the user in the past. The search unit can also analyze, for example, the user's past feedback and suggest an optimal search method. The search unit can also adjust the display order of search results by reflecting, for example, the user's past feedback. This allows the search method to be customized by reflecting the user's past feedback. The feedback includes, for example, information such as questionnaires and behavior logs. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's feedback data into a generation AI and cause the generation AI to customize the search method.

[0040] The suggestion unit can adjust the content of the suggestion based on the importance of the event or circle when making the suggestion. The suggestion unit uses the generation AI to adjust the content of the suggestion based on the importance of the event or circle when making the suggestion. For example, the suggestion unit provides detailed information for events or circles with high importance. The suggestion unit can also provide concise information for events or circles with low importance. The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the event or circle. This allows the content of the suggestion to be adjusted based on the importance of the event or circle. The importance is evaluated based on criteria such as the number of participants and frequency of events. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of events or circles into the generation AI and cause the generation AI to adjust the content of the suggestion.

[0041] The suggestion unit can apply multiple suggestion algorithms depending on the category of the event or club when making a suggestion. The suggestion unit uses the generation AI to apply multiple suggestion algorithms depending on the category of the event or club when making a suggestion. For example, the suggestion unit can apply an active suggestion algorithm to events or clubs in the sports category. The suggestion unit can also apply a moderate suggestion algorithm to events or clubs in the culture category. The suggestion unit can also apply an interesting suggestion algorithm to events or clubs in the hobby category. This allows different suggestion algorithms to be applied depending on the category of the event or club. Categories include definitions such as sports, music, and academics. The suggestion algorithm is realized using technologies such as collaborative filtering and content-based filtering. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without AI. For example, the suggestion unit can input event or club category data to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0042] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit uses the generation AI to improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion based on feedback from events or circles the user has previously participated in. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. This allows the accuracy of the suggestion to be improved by referring to the user's past suggestion results. The suggestion results include information such as the success rate of the suggestion and user feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0043] The suggestion unit can determine the order of proposals based on the timing of events and circles when making suggestions. The suggestion unit, using the generation AI, can determine the order of proposals based on the timing of events and circles when making suggestions. For example, the suggestion unit prioritizes upcoming events and circles. The suggestion unit can also postpone events and circles that are far away, for example. The suggestion unit can also determine the priority of proposals based on the timing of events and circles. This makes it possible to determine the order of proposals based on the timing of events and circles. The timing includes information such as date, time period, and season. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input event and circle timing data into the generation AI and have the generation AI determine the order of proposals.

[0044] The suggestion unit can adjust the order of suggestions based on the relevance of events and circles when making suggestions. The suggestion unit uses the generation AI to adjust the order of suggestions based on the relevance of events and circles when making suggestions. For example, the suggestion unit prioritizes suggesting events and circles that are most relevant to the user's interests. The suggestion unit can also postpone less relevant events and circles, for example. The suggestion unit can also adjust the order of suggestions based on the relevance of events and circles, for example. This makes it possible to adjust the order of suggestions based on the relevance of events and circles. Relevance is evaluated based on criteria such as a common theme or participant attributes. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of events and circles into the generation AI and cause the generation AI to adjust the order of suggestions.

[0045] The suggestion unit can adjust the use of proposed terms according to the user's expertise level when making a suggestion. The suggestion unit uses the generation AI to adjust the use of proposed terms according to the user's expertise level when making a suggestion. For example, if the user is a beginner, the suggestion unit can avoid technical terms when making a suggestion. For example, if the user is an intermediate user, the suggestion unit can also use appropriate technical terms when making a suggestion. For example, if the user is an advanced user, the suggestion unit can also use a lot of technical terms when making a suggestion. This allows the use of proposed terms to be adjusted according to the user's expertise level. The expertise level includes information such as survey results and past activity history. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without AI. For example, the suggestion unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the use of proposed terms.

[0046] The reservation unit can analyze the user's past reservation history and select the optimal reservation method when making a reservation. The reservation unit uses the generation AI to analyze the user's past reservation history and select the optimal reservation method when making a reservation. For example, the reservation unit can suggest the optimal reservation method based on reservation methods used by the user in the past. The reservation unit can also analyze the user's past reservation history and suggest the most efficient reservation method. The reservation unit can also select the optimal reservation method by referring to the user's past reservation history. This allows the optimal reservation method to be selected by analyzing the user's past reservation history. The reservation history includes information such as past reservation success rates and cancellation rates. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's past reservation history data into the generation AI and have the generation AI select the optimal reservation method.

[0047] The reservation unit can customize the reservation method based on the user's current living situation at the time of reservation. The reservation unit uses the generation AI to customize the reservation method based on the user's current living situation at the time of reservation. For example, the reservation unit can provide a simple reservation method when the user is busy. For example, the reservation unit can provide a detailed reservation method when the user is relaxed. The reservation unit can also provide the optimal reservation method based on the user's current living situation. This allows the reservation method to be customized based on the user's current living situation. The living situation includes information such as occupation, family environment, and health condition. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input the user's living situation data into the generation AI and have the generation AI customize the reservation method.

[0048] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit uses the generation AI to improve the reservation method by reflecting user feedback at the time of reservation. For example, the reservation unit improves the reservation method based on feedback provided by the user. The reservation unit can, for example, analyze user feedback and propose an optimal reservation method. The reservation unit can, for example, improve the reservation method by reflecting user feedback. This makes it possible to improve the reservation method by reflecting user feedback. Feedback includes, for example, information such as questionnaires and behavior logs. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, or may be performed without using AI. For example, the reservation unit can input user feedback data into the generation AI and have the generation AI improve the reservation method.

[0049] The reservation unit can select the optimal reservation method by taking into account the user's geographical location information when making a reservation. The reservation unit uses the generation AI to select the optimal reservation method by taking into account the user's geographical location information when making a reservation. For example, the reservation unit prioritizes reservations for events or clubs held nearby based on the user's current location. The reservation unit can also reserve events or clubs that are easily accessible based on the user's geographical location information. The reservation unit can also select the optimal reservation method by taking into account the user's geographical location information. This allows the optimal reservation method to be selected by taking into account the user's geographical location information. Geographical location information includes information such as GPS data and IP address. Some or all of the above-described processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal reservation method.

[0050] The reservation unit can analyze the user's social media activity and suggest a means of reservation at the time of reservation. The reservation unit uses a generation AI to analyze the user's social media activity and suggest a means of reservation at the time of reservation. For example, the reservation unit can analyze the user's social media posts and suggest reservations for related events or clubs. The reservation unit can also suggest reservations for related events or clubs based on the user's social media friends' activities, for example. The reservation unit can also suggest reservations for related events or clubs based on the user's social media check-in information, for example. In this way, the user's social media activity can be analyzed to suggest a means of reservation. Social media activity includes information such as the content of posts and the number of likes. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's social media activity data into the generation AI and have the generation AI suggest a means of reservation.

[0051] The reservation unit can customize the reservation method by reflecting the user's past feedback at the time of reservation. The reservation unit uses the generation AI to customize the reservation method by reflecting the user's past feedback at the time of reservation. For example, the reservation unit customizes the reservation method based on feedback provided by the user in the past. The reservation unit can also analyze the user's past feedback and suggest the optimal reservation method, for example. The reservation unit can also customize the reservation method by reflecting the user's past feedback, for example. This makes it possible to customize the reservation method by reflecting the user's past feedback. The feedback includes information such as questionnaires and behavior logs. Some or all of the above-described processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input user feedback data into the generation AI and have the generation AI customize the reservation method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The search unit can analyze a user's past search history and select an optimal search algorithm. For example, the search unit can prioritize displaying related events and circles based on keywords the user has previously searched for. The search unit can also identify categories of interest from the user's past search history and display events and circles related to those categories. The search unit can also analyze the user's past search history and display the most relevant events and circles. This allows the user's past search history to be analyzed and an optimal search algorithm to be selected. The search algorithm can be realized using technologies such as TF-IDF and PageRank. Some or all of the above-mentioned processing in the search unit can be performed using, for example, AI, or without AI. For example, the search unit can input the user's past search history data into a generation AI and have the generation AI select an optimal search algorithm.

[0054] The search unit can perform filtering based on the user's current living situation and areas of interest during a search. For example, when the user inputs their current living situation, the search unit displays events and circles that match that situation. The search unit can also filter and display related events and circles based on the user's areas of interest, for example. The search unit can also display optimal events and circles taking into account the user's current living situation and areas of interest, for example. This allows filtering based on the user's current living situation and areas of interest. Living situations include information such as occupation, family environment, and health status. Areas of interest include information such as hobbies and research topics. Some or all of the above-described processing in the search unit can be performed using, or without, AI. For example, the search unit can input data on the user's living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0055] The suggestion unit can adjust the content of the suggestion based on the importance of the event or circle when making a suggestion. For example, the suggestion unit can provide detailed information for events or circles with high importance. For example, the suggestion unit can provide concise information for events or circles with low importance. The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the event or circle. This allows the content of the suggestion to be adjusted based on the importance of the event or circle. The importance is evaluated based on criteria such as the number of participants and frequency of events. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of events or circles into a generation AI and cause the generation AI to adjust the content of the suggestion.

[0056] When making a suggestion, the suggestion unit can adjust the use of suggested terms according to the user's level of expertise. For example, if the user is a beginner, the suggestion unit can avoid technical terms when making a suggestion. For example, if the user is an intermediate user, the suggestion unit can also use appropriate technical terms when making a suggestion. For example, if the user is an advanced user, the suggestion unit can also use a lot of technical terms when making a suggestion. This allows the use of suggested terms to be adjusted according to the user's level of expertise. The level of expertise includes information such as survey results and past activity history. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of suggested terms.

[0057] When making a proposal, the suggestion unit can determine the order of proposals based on the timing of events and circles. For example, the suggestion unit prioritizes the proposal of upcoming events and circles. For example, the suggestion unit can postpone events and circles that are far away. The suggestion unit can also determine the priority of proposals based on the timing of events and circles. This makes it possible to determine the order of proposals based on the timing of events and circles. The timing includes information such as date, time period, and season. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the timing of events and circles to the generation AI and have the generation AI determine the order of proposals.

[0058] The suggestion unit can adjust the order of suggestions based on the relevance of events and circles when making suggestions. For example, the suggestion unit prioritizes suggesting events and circles that are most relevant to the user's interests. The suggestion unit can also postpone less relevant events and circles, for example. The suggestion unit can also adjust the order of suggestions based on the relevance of events and circles, for example. This makes it possible to adjust the order of suggestions based on the relevance of events and circles. Relevance is evaluated based on criteria such as a common theme or the attributes of participants. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of events and circles into a generation AI and cause the generation AI to adjust the order of suggestions.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The search unit searches for events and groups based on the user's interests and hobbies. For example, it searches for related events and groups based on keywords entered by the user, and can narrow down the search results by setting filtering conditions. It can also analyze the user's past search history and select the optimal search algorithm. Step 2: The suggestion unit uses the generation AI to analyze the user's conversation based on the information collected by the search unit and suggest appropriate events and clubs. For example, if a user says, "I want to do something fun this weekend," the generation AI analyzes the conversation and suggests events and clubs that match the user's interests. It can also collect feedback based on the information obtained from the user's conversation to be used in making next suggestions. Step 3: The reservation unit makes reservations for events and circles suggested by the suggestion unit. For example, the reservation unit displays details of the suggested events and circles, allowing the user to make reservations with one click. The unit can also analyze the user's past reservation history and select the optimal reservation method.

[0061] (Example 2) An information platform system according to an embodiment of the present invention searches for events and groups based on a user's interests and hobbies, and a generation AI analyzes the user's conversation to suggest appropriate events and groups and allow the user to make a reservation. The information platform system allows users to easily experience new things and build relationships by allowing them to search for events and groups based on their interests and hobbies, and then the generation AI analyzes the user's conversation to suggest appropriate events and groups and allow the user to make a reservation. For example, the information platform system displays related events and groups when a user simply enters a keyword. For example, entering keywords such as "yoga" or "cooking class" displays a list of related events and groups. The information platform system then uses the generation AI to analyze the user's conversation and suggest appropriate events and groups. For example, if a user says, "I want to do something fun this weekend," the generation AI analyzes the conversation and suggests events and groups that match the user's interests. The information platform system then displays details of the suggested events and groups, allowing the user to make a reservation with one click. This allows users to easily enjoy new experiences. The information platform system thus centralizes and provides information to help users spend their holidays more meaningfully, making it easier for them to find friends and places that share their hobbies and interests. Furthermore, by utilizing generative AI, appropriate events and circles can be suggested based on user conversations, allowing for a smooth process of booking participation. This allows the information platform system to provide users with centralized information to help them spend their holidays meaningfully, making it easier for them to find friends and places that share their hobbies and interests. Furthermore, by utilizing generative AI, appropriate events and circles can be suggested based on user conversations, allowing for a smooth process of booking participation.

[0062] An information platform system according to an embodiment includes a search unit, a suggestion unit, and a reservation unit. The search unit searches for events and clubs based on a user's interests and hobbies. For example, the search unit searches for related events and clubs based on keywords entered by the user. The search unit can also set filtering conditions to narrow search results. For example, when a user enters keywords such as "yoga" or "cooking class," the search unit displays a list of related events and clubs. The search unit can also analyze the user's past search history and select an optimal search algorithm. The suggestion unit uses a generation AI to analyze the user's conversation based on information collected by the search unit and suggest appropriate events and clubs. For example, when a user says, "I want to do something fun this weekend," the suggestion unit analyzes the conversation and suggests events and clubs that match the user's interests. The suggestion unit can also collect feedback based on information obtained from the user's conversation to be used in future suggestions. For example, the suggestion unit analyzes the content of the user's conversation and collects data to be reflected in future suggestions. The reservation unit makes reservations for participation in events and circles suggested by the suggestion unit. The reservation unit, for example, displays details of the suggested events and circles, allowing the user to make a reservation for participation with one click. The reservation unit can also analyze the user's past reservation history and select the optimal reservation method. For example, the reservation unit suggests the optimal reservation method based on the reservation methods used by the user in the past. This allows the information platform system according to the embodiment to search for, suggest, and make reservations for events and circles based on the user's interests and hobbies.

[0063] The search unit can search for related events and circles based on keywords entered by the user. For example, the search unit searches for related events and circles based on keywords entered by the user. For example, when a user enters keywords such as "yoga" or "cooking class," the search unit displays a list of related events and circles. The search unit can also set filtering conditions to narrow down search results. For example, the search unit narrows down search results by setting conditions such as the date, time, location, and participation fee of the event. Furthermore, the search unit can analyze the user's past search history and select the optimal search algorithm. For example, the search unit prioritizes displaying related events and circles based on keywords searched for by the user in the past. This makes it possible to search for related events and circles based on the keywords entered by the user.

[0064] The suggestion unit can analyze a user's conversation and suggest events and circles that match the user's interests. The suggestion unit uses a generation AI to analyze a user's conversation and suggest events and circles that match the user's interests. For example, when a user has a conversation such as, "I want to do something fun this weekend," the suggestion unit analyzes the conversation and suggests events and circles that match the user's interests. The suggestion unit can also collect feedback based on information obtained from the user's conversation to be used in future suggestions. For example, the suggestion unit analyzes the content of the user's conversation and collects data to be reflected in future suggestions. Furthermore, the suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion unit improves the accuracy of suggestions based on feedback from events and circles the user has participated in in the past. This makes it possible to analyze a user's conversation and suggest events and circles that match the user's interests.

[0065] The reservation unit displays details of proposed events and circles, allowing users to make reservations for participation. The reservation unit displays details of proposed events and circles, allowing users to make reservations for participation with one click. For example, the reservation unit displays details such as the date, time, location, and participation fee of proposed events and circles. The reservation unit can also analyze the user's past reservation history and select the optimal reservation method. For example, the reservation unit can suggest the optimal reservation method based on the reservation methods used by the user in the past. Furthermore, the reservation unit can reflect user feedback to improve the reservation method. For example, the reservation unit can improve the reservation method based on feedback provided by the user. This allows users to display details of proposed events and circles, allowing them to make reservations for participation with one click.

[0066] The suggestion unit can collect feedback to be used in the next suggestion based on information obtained from the user's conversation. The suggestion unit uses a generative AI to collect feedback to be used in the next suggestion based on information obtained from the user's conversation. For example, the suggestion unit analyzes the content of the user's conversation and collects data to be reflected in the next suggestion. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion based on feedback from events and circles that the user has previously participated in. Furthermore, the suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit makes suggestions using calmer expressions. This makes it possible to collect feedback to be used in the next suggestion based on information obtained from the user's conversation.

[0067] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user's emotions. For example, if the user is feeling stressed, the search unit can prioritize displaying relaxing events and circles. For example, if the user is excited, the search unit can prioritize displaying active events and circles. For example, if the user is tired, the search unit can prioritize displaying relaxing events and circles. This allows the display order of search results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the search unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the search unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display order of search results based on the emotion.

[0068] The search unit can analyze the user's past search history and select the optimal search algorithm. For example, the search unit can prioritize displaying related events and circles based on keywords the user has previously searched for. For example, the search unit can identify categories of interest from the user's past search history and display events and circles related to those categories. For example, the search unit can analyze the user's past search history and display the most relevant events and circles. This allows the user's past search history to be analyzed and the optimal search algorithm to be selected. The search algorithm can be realized using technologies such as TF-IDF and PageRank. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or without AI. For example, the search unit can input the user's past search history data into a generation AI and have the generation AI select the optimal search algorithm.

[0069] The search unit can perform filtering based on the user's current living situation and areas of interest during a search. For example, when the user inputs their current living situation, the search unit displays events and circles that match that situation. The search unit can also filter and display related events and circles based on the user's areas of interest, for example. The search unit can also display optimal events and circles taking into account the user's current living situation and areas of interest, for example. This allows filtering based on the user's current living situation and areas of interest. Living situations include information such as occupation, family environment, and health status. Areas of interest include information such as hobbies and research topics. Some or all of the above-described processing in the search unit can be performed using, or without, AI. For example, the search unit can input data on the user's living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0070] The search unit can select a search method according to the user's input method during a search. For example, when a user searches by voice, the search unit uses voice recognition technology to display optimal search results. For example, when a user searches by text, the search unit can also display optimal search results based on the input keywords. For example, when a user searches by image, the search unit can also display optimal search results using image recognition technology. This makes it possible to select an optimal search method according to the user's input method. Input methods include, for example, voice input, text input, and image input. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's input data into a generation AI and have the generation AI select the optimal search method.

[0071] The search unit can estimate the user's emotions and prioritize search results based on the estimated user emotions. For example, if the user is relaxed, the search unit can prioritize displaying relaxing events and circles. For example, if the user is excited, the search unit can prioritize displaying active events and circles. For example, if the user is tired, the search unit can prioritize displaying relaxing events and circles. This allows the prioritization of search results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the search unit can be performed using AI, for example, or without AI. For example, the search unit can input user emotion data into the generation AI and cause the generation AI to prioritize search results based on emotions.

[0072] During a search, the search unit can prioritize displaying highly relevant events and circles by taking into account the user's geographical location information. For example, the search unit can prioritize displaying events and circles held nearby based on the user's current location. For example, the search unit can also display events and circles that are easily accessible based on the user's geographical location information. For example, the search unit can also display optimal events and circles by taking into account the user's geographical location information. This allows highly relevant events and circles to be prioritized by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data, IP address, etc. Some or all of the above-described processing in the search unit may be performed using, or without, AI. For example, the search unit can input the user's geographical location information data into a generation AI and cause the generation AI to display highly relevant events and circles.

[0073] The search unit can analyze the user's social media activity during a search and display related events and circles. For example, the search unit can analyze the content of the user's social media posts and display related events and circles. For example, the search unit can also display related events and circles based on the activity of the user's friends on social media. For example, the search unit can display related events and circles based on the user's social media check-in information. In this way, the user's social media activity can be analyzed and related events and circles can be displayed. Social media activity includes information such as the content of posts and the number of likes. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's social media activity data into a generation AI and cause the generation AI to display related events and circles.

[0074] The search unit can customize the search method by reflecting the user's past feedback during a search. The search unit customizes search results based on, for example, feedback provided by the user in the past. The search unit can also analyze, for example, the user's past feedback and suggest an optimal search method. The search unit can also adjust the display order of search results by reflecting, for example, the user's past feedback. This allows the search method to be customized by reflecting the user's past feedback. The feedback includes, for example, information such as questionnaires and behavior logs. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's feedback data into a generation AI and cause the generation AI to customize the search method.

[0075] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. The suggestion unit can estimate the user's emotions using a generation AI and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can make suggestions using calm expressions. For example, if the user is excited, the suggestion unit can make suggestions using lively expressions. For example, if the user is tired, the suggestion unit can make suggestions using gentle expressions. This allows the way the suggestions are expressed to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0076] The suggestion unit can adjust the content of the suggestion based on the importance of the event or circle when making the suggestion. The suggestion unit uses the generation AI to adjust the content of the suggestion based on the importance of the event or circle when making the suggestion. For example, the suggestion unit provides detailed information for events or circles with high importance. The suggestion unit can also provide concise information for events or circles with low importance. The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the event or circle. This allows the content of the suggestion to be adjusted based on the importance of the event or circle. The importance is evaluated based on criteria such as the number of participants and frequency of events. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of events or circles into the generation AI and cause the generation AI to adjust the content of the suggestion.

[0077] The suggestion unit can apply multiple suggestion algorithms depending on the category of the event or club when making a suggestion. The suggestion unit uses the generation AI to apply multiple suggestion algorithms depending on the category of the event or club when making a suggestion. For example, the suggestion unit can apply an active suggestion algorithm to events or clubs in the sports category. The suggestion unit can also apply a moderate suggestion algorithm to events or clubs in the culture category. The suggestion unit can also apply an interesting suggestion algorithm to events or clubs in the hobby category. This allows different suggestion algorithms to be applied depending on the category of the event or club. Categories include definitions such as sports, music, and academics. The suggestion algorithm is realized using technologies such as collaborative filtering and content-based filtering. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without AI. For example, the suggestion unit can input event or club category data to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0078] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The suggestion unit uses the generation AI to improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion based on feedback from events or circles the user has previously participated in. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. This allows the accuracy of the suggestion to be improved by referring to the user's past suggestion results. The suggestion results include information such as the success rate of the suggestion and user feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0079] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion using a generation AI and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. For example, the suggestion unit can provide concise suggestions when the user is excited. For example, the suggestion unit can provide short suggestions when the user is tired. This allows the length of the suggestion to be adjusted based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0080] The suggestion unit can determine the order of proposals based on the timing of events and circles when making suggestions. The suggestion unit, using the generation AI, can determine the order of proposals based on the timing of events and circles when making suggestions. For example, the suggestion unit prioritizes upcoming events and circles. The suggestion unit can also postpone events and circles that are far away, for example. The suggestion unit can also determine the priority of proposals based on the timing of events and circles. This makes it possible to determine the order of proposals based on the timing of events and circles. The timing includes information such as date, time period, and season. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input event and circle timing data into the generation AI and have the generation AI determine the order of proposals.

[0081] The suggestion unit can adjust the order of suggestions based on the relevance of events and circles when making suggestions. The suggestion unit uses the generation AI to adjust the order of suggestions based on the relevance of events and circles when making suggestions. For example, the suggestion unit prioritizes suggesting events and circles that are most relevant to the user's interests. The suggestion unit can also postpone less relevant events and circles, for example. The suggestion unit can also adjust the order of suggestions based on the relevance of events and circles, for example. This makes it possible to adjust the order of suggestions based on the relevance of events and circles. Relevance is evaluated based on criteria such as a common theme or participant attributes. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of events and circles into the generation AI and cause the generation AI to adjust the order of suggestions.

[0082] The suggestion unit can adjust the use of proposed terms according to the user's expertise level when making a suggestion. The suggestion unit uses the generation AI to adjust the use of proposed terms according to the user's expertise level when making a suggestion. For example, if the user is a beginner, the suggestion unit can avoid technical terms when making a suggestion. For example, if the user is an intermediate user, the suggestion unit can also use appropriate technical terms when making a suggestion. For example, if the user is an advanced user, the suggestion unit can also use a lot of technical terms when making a suggestion. This allows the use of proposed terms to be adjusted according to the user's expertise level. The expertise level includes information such as survey results and past activity history. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without AI. For example, the suggestion unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the use of proposed terms.

[0083] The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated user emotions. The reservation unit can use a generation AI to estimate the user's emotions and adjust the reservation method based on the estimated user emotions. For example, the reservation unit can provide detailed reservation instructions when the user is relaxed. For example, the reservation unit can provide concise reservation instructions when the user is excited. For example, the reservation unit can provide short reservation instructions when the user is tired. This allows the reservation method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reservation unit can be performed using an AI, for example, or without an AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI adjust the reservation method.

[0084] The reservation unit can analyze the user's past reservation history and select the optimal reservation method when making a reservation. The reservation unit uses the generation AI to analyze the user's past reservation history and select the optimal reservation method when making a reservation. For example, the reservation unit can suggest the optimal reservation method based on reservation methods used by the user in the past. The reservation unit can also analyze the user's past reservation history and suggest the most efficient reservation method. The reservation unit can also select the optimal reservation method by referring to the user's past reservation history. This allows the optimal reservation method to be selected by analyzing the user's past reservation history. The reservation history includes information such as past reservation success rates and cancellation rates. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's past reservation history data into the generation AI and have the generation AI select the optimal reservation method.

[0085] The reservation unit can customize the reservation method based on the user's current living situation at the time of reservation. The reservation unit uses the generation AI to customize the reservation method based on the user's current living situation at the time of reservation. For example, the reservation unit can provide a simple reservation method when the user is busy. For example, the reservation unit can provide a detailed reservation method when the user is relaxed. The reservation unit can also provide the optimal reservation method based on the user's current living situation. This allows the reservation method to be customized based on the user's current living situation. The living situation includes information such as occupation, family environment, and health condition. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input the user's living situation data into the generation AI and have the generation AI customize the reservation method.

[0086] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit uses the generation AI to improve the reservation method by reflecting user feedback at the time of reservation. For example, the reservation unit improves the reservation method based on feedback provided by the user. The reservation unit can, for example, analyze user feedback and propose an optimal reservation method. The reservation unit can, for example, improve the reservation method by reflecting user feedback. This makes it possible to improve the reservation method by reflecting user feedback. Feedback includes, for example, information such as questionnaires and behavior logs. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, or may be performed without using AI. For example, the reservation unit can input user feedback data into the generation AI and have the generation AI improve the reservation method.

[0087] The reservation unit can estimate the user's emotions and determine the priority of reservations based on the estimated user emotions. The reservation unit can estimate the user's emotions using a generation AI and determine the priority of reservations based on the estimated user emotions. For example, if the user is relaxed, the reservation unit can prioritize reservations for relaxing events or clubs. For example, if the user is excited, the reservation unit can prioritize reservations for active events or clubs. For example, if the user is tired, the reservation unit can prioritize reservations for relaxing events or clubs. This allows the priority of reservations to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reservation unit can be performed using an AI, for example, or without an AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of reservations.

[0088] The reservation unit can select the optimal reservation method by taking into account the user's geographical location information when making a reservation. The reservation unit uses the generation AI to select the optimal reservation method by taking into account the user's geographical location information when making a reservation. For example, the reservation unit prioritizes reservations for events or clubs held nearby based on the user's current location. The reservation unit can also reserve events or clubs that are easily accessible based on the user's geographical location information. The reservation unit can also select the optimal reservation method by taking into account the user's geographical location information. This allows the optimal reservation method to be selected by taking into account the user's geographical location information. Geographical location information includes information such as GPS data and IP address. Some or all of the above-described processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal reservation method.

[0089] The reservation unit can analyze the user's social media activity and suggest a means of reservation at the time of reservation. The reservation unit uses a generation AI to analyze the user's social media activity and suggest a means of reservation at the time of reservation. For example, the reservation unit can analyze the user's social media posts and suggest reservations for related events or clubs. The reservation unit can also suggest reservations for related events or clubs based on the user's social media friends' activities, for example. The reservation unit can also suggest reservations for related events or clubs based on the user's social media check-in information, for example. In this way, the user's social media activity can be analyzed to suggest a means of reservation. Social media activity includes information such as the content of posts and the number of likes. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's social media activity data into the generation AI and have the generation AI suggest a means of reservation.

[0090] The reservation unit can customize the reservation method by reflecting the user's past feedback at the time of reservation. The reservation unit uses the generation AI to customize the reservation method by reflecting the user's past feedback at the time of reservation. For example, the reservation unit customizes the reservation method based on feedback provided by the user in the past. The reservation unit can also analyze the user's past feedback and suggest the optimal reservation method, for example. The reservation unit can also customize the reservation method by reflecting the user's past feedback, for example. This makes it possible to customize the reservation method by reflecting the user's past feedback. The feedback includes information such as questionnaires and behavior logs. Some or all of the above-described processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input user feedback data into the generation AI and have the generation AI customize the reservation method. === Hard Collateral 1-1 === Each of the multiple elements, including the search unit, suggestion unit, and reservation unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the search unit is implemented by the control unit 46A of the smart device 14 and searches for relevant events and circles based on keywords entered by the user. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's conversation using a generation AI to suggest appropriate events and circles. The reservation unit is implemented, for example, by the control unit 46A of the smart device 14 and displays details of the suggested events and circles, allowing the user to make a reservation for participation with one click. In addition, the search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. Emotion estimation is implemented, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the search unit, suggestion unit, and reservation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the smart glasses 214 and searches for relevant events and circles based on keywords entered by the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's conversation using a generation AI to suggest appropriate events and circles. The reservation unit is realized, for example, by the control unit 46A of the smart glasses 214 and displays details of the suggested events and circles, allowing the user to make a reservation for participation with one click. In addition, the search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. Emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the search unit, suggestion unit, and reservation unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the search unit is implemented by the control unit 46A of the headset-type terminal 314 and searches for relevant events and circles based on keywords entered by the user. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's conversation using a generation AI to suggest appropriate events and circles. The reservation unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and displays details of the suggested events and circles, allowing the user to make a reservation for participation with one click. In addition, the search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. Emotion estimation is implemented, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the search unit, suggestion unit, and reservation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the robot 414 and searches for relevant events and clubs based on keywords entered by the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's conversation using a generation AI to suggest appropriate events and clubs. The reservation unit is realized, for example, by the control unit 46A of the robot 414 and displays details of the suggested events and clubs, allowing the user to make a reservation for participation with one click. In addition, the search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. Emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The search unit can analyze a user's past search history and select an optimal search algorithm. For example, the search unit can prioritize displaying related events and circles based on keywords the user has previously searched for. The search unit can also identify categories of interest from the user's past search history and display events and circles related to those categories. The search unit can also analyze the user's past search history and display the most relevant events and circles. This allows the user's past search history to be analyzed and an optimal search algorithm to be selected. The search algorithm can be realized using technologies such as TF-IDF and PageRank. Some or all of the above-mentioned processing in the search unit can be performed using, for example, AI, or without AI. For example, the search unit can input the user's past search history data into a generation AI and have the generation AI select an optimal search algorithm.

[0093] The suggestion unit can collect feedback to be used in the next suggestion based on information obtained from the user's conversation. For example, the suggestion unit analyzes the content of the user's conversation and collects data to be reflected in the next suggestion. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can improve the accuracy of the suggestion based on feedback from events and circles that the user has previously participated in. Furthermore, the suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit makes the suggestion using calmer expressions. In this way, feedback to be used in the next suggestion can be collected based on information obtained from the user's conversation.

[0094] The search unit can perform filtering based on the user's current living situation and areas of interest during a search. For example, when the user inputs their current living situation, the search unit displays events and circles that match that situation. The search unit can also filter and display related events and circles based on the user's areas of interest, for example. The search unit can also display optimal events and circles taking into account the user's current living situation and areas of interest, for example. This allows filtering based on the user's current living situation and areas of interest. Living situations include information such as occupation, family environment, and health status. Areas of interest include information such as hobbies and research topics. Some or all of the above-described processing in the search unit can be performed using, or without, AI. For example, the search unit can input data on the user's living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0095] The suggestion unit can adjust the content of the suggestion based on the importance of the event or circle when making a suggestion. For example, the suggestion unit can provide detailed information for events or circles with high importance. For example, the suggestion unit can provide concise information for events or circles with low importance. The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the event or circle. This allows the content of the suggestion to be adjusted based on the importance of the event or circle. The importance is evaluated based on criteria such as the number of participants and frequency of events. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of events or circles into a generation AI and cause the generation AI to adjust the content of the suggestion.

[0096] When making a suggestion, the suggestion unit can adjust the use of suggested terms according to the user's level of expertise. For example, if the user is a beginner, the suggestion unit can avoid technical terms when making a suggestion. For example, if the user is an intermediate user, the suggestion unit can also use appropriate technical terms when making a suggestion. For example, if the user is an advanced user, the suggestion unit can also use a lot of technical terms when making a suggestion. This allows the use of suggested terms to be adjusted according to the user's level of expertise. The level of expertise includes information such as survey results and past activity history. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of suggested terms.

[0097] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. For example, if the user is feeling stressed, the search unit can prioritize displaying relaxing events and circles. For example, if the user is excited, the search unit can prioritize displaying active events and circles. For example, if the user is tired, the search unit can prioritize displaying relaxing events and circles. This allows the display order of search results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the search unit can be performed using an AI, for example, or without an AI. For example, the search unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display order of search results based on the emotion.

[0098] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make the suggestions using calm expressions. For example, if the user is excited, the suggestion unit can make the suggestions using lively expressions. For example, if the user is tired, the suggestion unit can make the suggestions using gentle expressions. This allows the way the suggestions are expressed to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0099] The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated user emotions. For example, the reservation unit can provide detailed reservation instructions when the user is relaxed. For example, the reservation unit can provide concise reservation instructions when the user is excited. For example, the reservation unit can provide short reservation instructions when the user is tired. This allows the reservation method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reservation unit can be performed using AI, for example, or without AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI adjust the reservation method.

[0100] When making a proposal, the suggestion unit can determine the order of proposals based on the timing of events and circles. For example, the suggestion unit prioritizes the proposal of upcoming events and circles. For example, the suggestion unit can postpone events and circles that are far away. The suggestion unit can also determine the priority of proposals based on the timing of events and circles. This makes it possible to determine the order of proposals based on the timing of events and circles. The timing includes information such as date, time period, and season. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the timing of events and circles to the generation AI and have the generation AI determine the order of proposals.

[0101] The suggestion unit can adjust the order of suggestions based on the relevance of events and circles when making suggestions. For example, the suggestion unit prioritizes suggesting events and circles that are most relevant to the user's interests. The suggestion unit can also postpone less relevant events and circles, for example. The suggestion unit can also adjust the order of suggestions based on the relevance of events and circles, for example. This makes it possible to adjust the order of suggestions based on the relevance of events and circles. Relevance is evaluated based on criteria such as a common theme or the attributes of participants. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of events and circles into a generation AI and cause the generation AI to adjust the order of suggestions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The search unit searches for events and groups based on the user's interests and hobbies. For example, it searches for related events and groups based on keywords entered by the user, and can narrow down the search results by setting filtering conditions. It can also analyze the user's past search history and select the optimal search algorithm. Step 2: The suggestion unit uses the generation AI to analyze the user's conversation based on the information collected by the search unit and suggest appropriate events and clubs. For example, if a user says, "I want to do something fun this weekend," the generation AI analyzes the conversation and suggests events and clubs that match the user's interests. It can also collect feedback based on the information obtained from the user's conversation to be used in making next suggestions. Step 3: The reservation unit makes reservations for events and circles suggested by the suggestion unit. For example, the reservation unit displays details of the suggested events and circles, allowing the user to make reservations with one click. The unit can also analyze the user's past reservation history and select the optimal reservation method.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A search section that searches for events and clubs based on the user's interests and hobbies; a suggestion unit that analyzes user conversations based on the information collected by the search unit and suggests events and clubs; a reservation unit that makes reservations for participation in the events and circles proposed by the proposal unit; A system characterized by:

2. The search unit Search for related events and groups based on keywords entered by the user 2. The system of claim 1.

3. The proposal unit Analyze user conversations and suggest events and clubs that match the user's interests 2. The system of claim 1.

4. The reservation unit Shows details of proposed events and clubs, and allows users to book in 2. The system of claim 1.

5. The proposal unit Use information from user conversations to gather feedback to inform future proposals 2. The system of claim 1.

6. The search unit Inferring user sentiment and adjusting the display order of search results based on the estimated user sentiment 2. The system of claim 1.

7. The search unit Analyze users' past search history and select search algorithms 2. The system of claim 1.

8. The search unit Filtering searches based on the user's current life situation and interests 2. The system of claim 1.

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