system

Abstract

The system according to the embodiment aims to automate the organizing work, efficiently collect the desired conditions of participants, and propose and reserve the optimal location and plan. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a reservation unit. The collection unit collects attendance confirmations and desired conditions of participants. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes an appropriate location and plan based on the results obtained by the analysis unit. The reservation unit reserves the location and plan proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the organizer's work involves collecting participants' desired conditions, proposing the optimal location and plan, and making reservations, which is a complicated process.

[0005] The system according to the embodiment aims to automate the organizing work, efficiently collect the desired conditions of participants, and propose and reserve the optimal location and plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a reservation unit. The collection unit collects attendance confirmations and desired conditions of participants. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes an appropriate location and plan based on the results obtained by the analysis unit. The reservation unit reserves the location and plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the organizing work, efficiently collects the desired conditions of participants, and can propose and reserve the most suitable location and plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The organizing task automation system according to an embodiment of the present invention collects attendees' attendance confirmations and desired conditions, analyzes them using a generation AI, proposes the optimal location and plan, and makes reservations. The organizing task automation system collects attendees' attendance confirmations and desired conditions, analyzes them using a generation AI, and proposes the optimal location and plan. Furthermore, the organizing task automation system considers the attendees' schedules, proposes the optimal date, time, and venue, and completes the reservation process. For example, the organizing task automation system collects attendees' attendance confirmations and desired conditions. For example, it collects information such as "I will attend," "My budget is within 3,000 yen," and "Japanese food is preferable." This information is input into the generation AI. Next, the organizing task automation system analyzes the collected information and proposes the optimal location and plan based on the attendees' preferences, budget, and desired conditions. For example, it proposes "a restaurant where you can enjoy Japanese food within a budget of 3,000 yen." Furthermore, the organizing task automation system considers the attendees' schedules, and proposes the optimal date, time, and venue. For example, it makes a proposal such as, "Taking everyone's schedules into consideration, I'll make a reservation for next Friday at 7 p.m." Finally, in the event planning automation system, the generative AI automatically handles venue reservation procedures, significantly reducing the time and effort required for event planners to reserve venues. This allows the event planning automation system to streamline event planning work, saving time and effort, while also providing support up to and including venue reservations. This allows the event planning automation system to significantly reduce the time and effort required for event planners to reserve venues. For example, the system can be used in a wide range of situations, such as when university students plan club drinking parties, when working adults plan company events, and when elderly people plan community gatherings.

[0029] The event organizer automation system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a reservation unit. The collection unit collects participants' attendance confirmations and desired conditions. Participants' attendance confirmations may be sent via, but are not limited to, email, telephone, online form, etc. The collection unit collects information such as participants' responses, such as "I will attend," "My budget is within 3,000 yen," and "I prefer Japanese food." The analysis unit analyzes the information collected by the collection unit. The analysis may be performed using, but is not limited to, methods such as data mining or statistical analysis. The analysis unit analyzes data based on, for example, the participants' preferences, budget, and desired conditions. The proposal unit proposes an optimal location and plan based on the results obtained by the analysis unit. The proposal may, for example, propose a "restaurant where you can enjoy Japanese food within a budget of 3,000 yen" based on the participants' desired conditions, but is not limited to this example. The proposal unit may also propose an optimal date, time, and venue, taking into account the participants' schedules. The reservation unit reserves the location and plan proposed by the proposal unit. Reservations are made, for example, online or by telephone, but are not limited to these examples. The reservation unit performs reservation procedures based on a suggestion such as, "Taking everyone's schedules into consideration, we will make a reservation for next Friday at 7 p.m." As a result, the organizing task automation system according to the embodiment can streamline organizing tasks, save time and effort, and provide support including venue reservations.

[0030] The collection unit can analyze the participants' past attendance history and select the optimal collection method. For example, if a participant has frequently attended in the past, the collection unit can select a simple confirmation method. Furthermore, if a participant has frequently been absent in the past, the collection unit can also select a detailed confirmation method. Furthermore, the collection unit can confirm attendance at the optimal time based on the participants' past attendance history. This enables efficient attendance confirmation by selecting the optimal collection method based on the participants' past attendance history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the participants' past attendance history data into the generation AI and have the generation AI select the optimal collection method.

[0031] The collection unit can filter participants' attendance based on their current projects and areas of interest when confirming their attendance. For example, the collection unit can confirm only events related to the project in which the participant is currently involved. The collection unit can also confirm only related events based on the participant's areas of interest. The collection unit can also determine the priority of attendance confirmations according to the progress of the participant's current project. This enables highly relevant attendance confirmations by filtering based on the participant's current project and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the participant's project data into a generation AI and have the generation AI perform the filtering.

[0032] The collection unit can select an appropriate collection means depending on the input method of the participants when confirming attendance. For example, if a participant prefers voice input, the collection unit can confirm attendance by voice. Furthermore, if a participant prefers text input, the collection unit can also confirm attendance by text. Furthermore, if a participant prefers image input, the collection unit can also confirm attendance by image. This improves the efficiency of attendance confirmation by selecting the optimal collection means depending on the participant's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the participant's input data into a generation AI and have the generation AI select the optimal collection means.

[0033] When confirming attendance, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the participants. For example, the collection unit prioritizes collecting event information related to the participant's current location. The collection unit can also prioritize collecting nearby event information based on the participant's geographical location information. The collection unit can also prioritize collecting highly relevant information based on the participant's movement history. This enables efficient information collection by prioritizing the collection of highly relevant information by taking into account the participant's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the participant's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0034] The collection unit can analyze the participants' social media activities and collect relevant information when confirming attendance. For example, the collection unit can collect information related to the locations where the participants checked in on social media. The collection unit can also analyze the participants' social media posts to collect relevant event information. The collection unit can also refer to the activities of the participants' friends on social media to collect relevant information. In this way, highly relevant information can be collected by analyzing the participants' social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the participants' social media data into the generation AI and cause the generation AI to collect relevant information.

[0035] The collection unit can customize the collection method by reflecting participants' past feedback when confirming attendance. The collection unit, for example, selects the optimal collection method based on feedback provided by participants in the past. The collection unit can also improve the collection means by reflecting participants' past feedback. The collection unit can also customize the collection method based on participants' feedback. In this way, the optimal collection method can be selected by reflecting participants' past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input participants' past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a simplified analysis on less important information. The analysis unit can also determine the priority of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a preference analysis algorithm to information about participant preferences. The analysis unit can also apply a budget analysis algorithm to information about budgets. The analysis unit can also apply a schedule analysis algorithm to information about schedules. By applying different analysis algorithms depending on the category of information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the participant's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the participant's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the participant's past analysis results. The analysis unit can also determine the priority of the analysis based on the participant's past analysis results. In this way, the accuracy of the analysis is improved by referring to the participant's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the participant's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the priority of information that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also determine the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the participants' level of expertise. For example, if a participant has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if a participant does not have technical expertise, the analysis unit can provide analysis results in simpler terms. The analysis unit can also adjust the way the analysis results are expressed according to the participants' level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the participants' level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input participants' level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0042] The suggestion unit can adjust the level of detail of the proposal based on the importance of the place or plan when making a proposal. For example, the suggestion unit makes a detailed proposal for an important place or plan. The suggestion unit can also make a simplified proposal for a less important place or plan. The suggestion unit can also determine the priority of the proposal according to the importance of the place or plan. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the place or plan. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the place or plan to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0043] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of the location or plan. For example, the suggestion unit can apply a restaurant suggestion algorithm to a proposal related to a restaurant. The suggestion unit can also apply an event suggestion algorithm to a proposal related to an event. The suggestion unit can also apply an accommodation suggestion algorithm to a proposal related to an accommodation. In this way, by applying different suggestion algorithms depending on the category of the location or plan, the accuracy of the proposal is improved. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input location or plan category data into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0044] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the participants' past proposal results. The proposal unit, for example, adjusts the proposal algorithm based on the participants' past proposal results. The proposal unit can also improve the accuracy of the proposal by referring to the participants' past proposal results. The proposal unit can also determine the priority of the proposal based on the participants' past proposal results. In this way, the accuracy of the proposal is improved by referring to the participants' past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the participants' past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0045] The proposal unit can determine the priority of proposals based on the time of submission of locations and plans when making proposals. For example, the proposal unit prioritizes the most recent locations and plans. The proposal unit can also lower the priority of locations and plans that have been submitted earlier. The proposal unit can also adjust the proposal schedule based on the time of submission. This enables efficient proposals by determining the priority of proposals based on the time of submission of locations and plans. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the time of submission of locations and plans into the generation AI and cause the generation AI to determine the priority of proposals.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of places and plans when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant places and plans. The suggestion unit can also postpone suggesting less relevant places and plans. The suggestion unit can also determine the order of suggestions based on the relevance of places and plans. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of places and plans. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of places and plans to a generation AI and cause the generation AI to adjust the order of suggestions.

[0047] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the participant's level of expertise. For example, if the participant has technical expertise, the suggestion unit can make a proposal that uses a lot of technical terminology. Also, if the participant does not have technical expertise, the suggestion unit can make a proposal in simple language. The suggestion unit can also adjust the way the proposal is expressed depending on the participant's level of expertise. This allows for more appropriate proposals to be made by adjusting the use of technical terminology in the proposal depending on the participant's level of expertise. 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 participant's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] When making a reservation, the reservation unit can analyze the participant's past reservation history and select an appropriate reservation method. The reservation unit, for example, selects the optimal reservation method based on the reservation methods used by the participant in the past. The reservation unit can also select a reservation method that avoids congestion from the participant's past reservation history. The reservation unit can also analyze the participant's past reservation history and select the most efficient reservation method. In this way, the optimal reservation method can be selected by analyzing the participant's past reservation history. 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 participant's past reservation history data into the generation AI and have the generation AI select the optimal reservation method.

[0049] The reservation unit can customize the reservation method based on the participant's current schedule when making a reservation. The reservation unit, for example, suggests the optimal reservation date and time based on the participant's schedule. The reservation unit can also customize the reservation method taking the participant's schedule into consideration. The reservation unit can also determine reservation priorities based on the participant's schedule. This enables efficient reservations by customizing the reservation method based on the participant's current schedule. 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 participant's schedule data into a generation AI and have the generation AI customize the reservation method.

[0050] The reservation unit can improve the reservation method by reflecting participant feedback at the time of reservation. The reservation unit can improve the reservation method, for example, based on participant past feedback. The reservation unit can also customize the reservation means by reflecting participant feedback. The reservation unit can also determine reservation priorities based on participant feedback. In this way, the reservation method can be improved by reflecting participant feedback. 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 participant feedback data into a generation AI and have the generation AI improve the reservation method.

[0051] When making a reservation, the reservation unit can select an appropriate reservation method by taking into account the geographical location information of the participant. The reservation unit selects the optimal reservation method based on, for example, the participant's current location. The reservation unit can also select a nearby reservation method based on the participant's geographical location information. The reservation unit can also select the optimal reservation method based on the participant's movement history. In this way, the optimal reservation method can be selected by taking into account the participant's geographical location information. 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 participant's geographical location data into the generation AI and have the generation AI select the optimal reservation method.

[0052] At the time of reservation, the reservation unit can analyze the participant's social media activity to suggest a reservation method. The reservation unit can suggest a reservation method, for example, based on the location where the participant checked in on social media. The reservation unit can also analyze the participant's social media posts to suggest the optimal reservation method. The reservation unit can also suggest a reservation method based on the activity of the participant's friends on social media. In this way, the optimal reservation method can be suggested by analyzing the participant's social media activity. 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 participant's social media data into a generation AI and have the generation AI suggest a reservation method.

[0053] The reservation unit can customize the reservation method by reflecting the participant's past feedback when making a reservation. The reservation unit, for example, selects the optimal reservation method based on the participant's past feedback. The reservation unit can also improve the reservation means by reflecting the participant's feedback. The reservation unit can also determine reservation priorities based on the participant's feedback. In this way, the optimal reservation method can be selected by reflecting the participant's past feedback. 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 participant's past feedback data into the generation AI and have the generation AI customize the reservation method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can also monitor the health status of participants and adjust the timing of attendance confirmation based on their health status. For example, if a participant is feeling unwell, the collection unit can postpone attendance confirmation. Alternatively, if a participant is in good health, the collection unit can confirm attendance at the usual time. Furthermore, the collection unit can change the method of attendance confirmation depending on the participant's health status. This allows for flexible attendance confirmation based on the participant's health status.

[0056] The suggestion unit can analyze the participants' past behavioral patterns and select the optimal suggestion method. For example, it makes suggestions based on the participants' past favorite locations and plans. The suggestion unit can also adjust the timing of suggestions based on the participants' past behavioral patterns. Furthermore, the suggestion unit can also customize the content of suggestions by referring to the participants' past behavioral patterns. This makes it possible to make optimal suggestions based on the participants' past behavioral patterns.

[0057] The collection unit can customize the content of the attendance confirmation taking into account the hobbies and interests of the participants. For example, if a participant is interested in music, the collection unit can send an attendance confirmation for a music event. If a participant is interested in sports, the collection unit can also send an attendance confirmation for a sports event. Furthermore, the collection unit can change the method of attendance confirmation based on the hobbies and interests of the participants. This allows for flexible attendance confirmation according to the hobbies and interests of the participants.

[0058] The reservation department can select the optimal reservation confirmation method based on the participant's past reservation history. For example, the reservation confirmation is performed based on the participant's preferred confirmation method in the past. The reservation department can also adjust the timing of confirmation based on the participant's past reservation history. Furthermore, the reservation department can also customize the content of confirmation by referring to the participant's past reservation history. This makes it possible to optimally confirm reservations based on the participant's past reservation history.

[0059] The suggestion unit can select the optimal suggestion method based on the geographical location information of the participant. For example, it can suggest nearby places or plans based on the participant's current location. The suggestion unit can also make suggestions that take travel time into consideration based on the participant's geographical location information. Furthermore, the suggestion unit can also customize the content of the suggestions based on the participant's geographical location information. This makes it possible to make optimal suggestions based on the participant's geographical location information.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection department collects participants' attendance confirmations and desired conditions. Participants' attendance confirmations can be made via email, phone, online forms, etc. The collection department collects information such as participants' responses such as "I will attend," "My budget is within 3,000 yen," and "I prefer Japanese food." Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as data mining and statistical analysis. The analysis unit analyzes the data based on the participants' preferences, budget, and desired conditions. Step 3: The proposal section proposes the optimal location and plan based on the results obtained by the analysis section. The proposal will suggest "restaurants where you can enjoy Japanese food within a budget of 3,000 yen" based on the participants' desired conditions. The proposal section can also suggest the optimal date, time, and venue taking into account the participants' schedules. Step 4: The Reservations Department reserves the location and plan proposed by the Proposal Department. Reservations are made online, by phone, or other methods. The Reservations Department processes reservations based on a proposal such as, "Taking everyone's schedules into consideration, we'll make a reservation for next Friday at 7 p.m."

[0062] (Example 2) The organizing task automation system according to an embodiment of the present invention collects attendees' attendance confirmations and desired conditions, analyzes them using a generation AI, proposes the optimal location and plan, and makes reservations. The organizing task automation system collects attendees' attendance confirmations and desired conditions, analyzes them using a generation AI, and proposes the optimal location and plan. Furthermore, the organizing task automation system considers the attendees' schedules, proposes the optimal date, time, and venue, and completes the reservation process. For example, the organizing task automation system collects attendees' attendance confirmations and desired conditions. For example, it collects information such as "I will attend," "My budget is within 3,000 yen," and "Japanese food is preferable." This information is input into the generation AI. Next, the organizing task automation system analyzes the collected information and proposes the optimal location and plan based on the attendees' preferences, budget, and desired conditions. For example, it proposes "a restaurant where you can enjoy Japanese food within a budget of 3,000 yen." Furthermore, the organizing task automation system considers the attendees' schedules, and proposes the optimal date, time, and venue. For example, it makes a proposal such as, "Taking everyone's schedules into consideration, I'll make a reservation for next Friday at 7 p.m." Finally, in the event planning automation system, the generative AI automatically handles venue reservation procedures, significantly reducing the time and effort required for event planners to reserve venues. This allows the event planning automation system to streamline event planning work, saving time and effort, while also providing support up to and including venue reservations. This allows the event planning automation system to significantly reduce the time and effort required for event planners to reserve venues. For example, the system can be used in a wide range of situations, such as when university students plan club drinking parties, when working adults plan company events, and when elderly people plan community gatherings.

[0063] The event organizer automation system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a reservation unit. The collection unit collects participants' attendance confirmations and desired conditions. Participants' attendance confirmations may be sent via, but are not limited to, email, telephone, online form, etc. The collection unit collects information such as participants' responses, such as "I will attend," "My budget is within 3,000 yen," and "I prefer Japanese food." The analysis unit analyzes the information collected by the collection unit. The analysis may be performed using, but is not limited to, methods such as data mining or statistical analysis. The analysis unit analyzes data based on, for example, the participants' preferences, budget, and desired conditions. The proposal unit proposes an optimal location and plan based on the results obtained by the analysis unit. The proposal may, for example, propose a "restaurant where you can enjoy Japanese food within a budget of 3,000 yen" based on the participants' desired conditions, but is not limited to this example. The proposal unit may also propose an optimal date, time, and venue, taking into account the participants' schedules. The reservation unit reserves the location and plan proposed by the proposal unit. Reservations are made, for example, online or by telephone, but are not limited to these examples. The reservation unit performs reservation procedures based on a suggestion such as, "Taking everyone's schedules into consideration, we will make a reservation for next Friday at 7 p.m." As a result, the organizing task automation system according to the embodiment can streamline organizing tasks, save time and effort, and provide support including venue reservations.

[0064] The collection unit can estimate the emotions of participants and adjust the timing of attendance confirmation based on the estimated emotions of the participants. For example, if a participant is feeling stressed, the collection unit can check the attendance at a time when the participant is relaxed. Furthermore, if a participant is busy, the collection unit can adjust the time so that the attendance confirmation is completed quickly. Furthermore, if a participant is relaxed, the collection unit can perform detailed attendance confirmation. This allows the timing of the attendance confirmation to be adjusted according to the participant's emotions, thereby enabling attendance confirmation at a more appropriate time. The emotion estimation is realized 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-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input facial expression data of participants into the generation AI and have the generation AI perform emotion estimation.

[0065] The collection unit can analyze the participants' past attendance history and select the optimal collection method. For example, if a participant has frequently attended in the past, the collection unit can select a simple confirmation method. Furthermore, if a participant has frequently been absent in the past, the collection unit can also select a detailed confirmation method. Furthermore, the collection unit can confirm attendance at the optimal time based on the participants' past attendance history. This enables efficient attendance confirmation by selecting the optimal collection method based on the participants' past attendance history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the participants' past attendance history data into the generation AI and have the generation AI select the optimal collection method.

[0066] The collection unit can filter participants' attendance based on their current projects and areas of interest when confirming their attendance. For example, the collection unit can confirm only events related to the project in which the participant is currently involved. The collection unit can also confirm only related events based on the participant's areas of interest. The collection unit can also determine the priority of attendance confirmations according to the progress of the participant's current project. This enables highly relevant attendance confirmations by filtering based on the participant's current project and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the participant's project data into a generation AI and have the generation AI perform the filtering.

[0067] The collection unit can select an appropriate collection means depending on the input method of the participants when confirming attendance. For example, if a participant prefers voice input, the collection unit can confirm attendance by voice. Furthermore, if a participant prefers text input, the collection unit can also confirm attendance by text. Furthermore, if a participant prefers image input, the collection unit can also confirm attendance by image. This improves the efficiency of attendance confirmation by selecting the optimal collection means depending on the participant's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the participant's input data into a generation AI and have the generation AI select the optimal collection means.

[0068] The collection unit can estimate the emotions of the participants and determine the priority of information to be collected based on the estimated emotions of the participants. For example, if a participant is feeling stressed, the collection unit can prioritize collecting only important information. Furthermore, if a participant is relaxed, the collection unit can also collect detailed information. Furthermore, if a participant is busy, the collection unit can prioritize information that can be collected in a short time. Thus, by determining the priority of information to be collected according to the emotions of the participants, important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input facial expression data of the participants into the generation AI and have the generation AI perform emotion estimation.

[0069] When confirming attendance, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the participants. For example, the collection unit prioritizes collecting event information related to the participant's current location. The collection unit can also prioritize collecting nearby event information based on the participant's geographical location information. The collection unit can also prioritize collecting highly relevant information based on the participant's movement history. This enables efficient information collection by prioritizing the collection of highly relevant information by taking into account the participant's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the participant's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0070] The collection unit can analyze the participants' social media activities and collect relevant information when confirming attendance. For example, the collection unit can collect information related to the locations where the participants checked in on social media. The collection unit can also analyze the participants' social media posts to collect relevant event information. The collection unit can also refer to the activities of the participants' friends on social media to collect relevant information. In this way, highly relevant information can be collected by analyzing the participants' social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the participants' social media data into the generation AI and cause the generation AI to collect relevant information.

[0071] The collection unit can customize the collection method by reflecting participants' past feedback when confirming attendance. The collection unit, for example, selects the optimal collection method based on feedback provided by participants in the past. The collection unit can also improve the collection means by reflecting participants' past feedback. The collection unit can also customize the collection method based on participants' feedback. In this way, the optimal collection method can be selected by reflecting participants' past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input participants' past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0072] The analysis unit can estimate the emotions of the participants and adjust the way the analysis is presented based on the estimated emotions of the participants. For example, if a participant is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if a participant is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if a participant is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the way the analysis is presented based on the emotions of the participants, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input facial expression data of the participants into the generation AI and have the generation AI perform emotion estimation.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a simplified analysis on less important information. The analysis unit can also determine the priority of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a preference analysis algorithm to information about participant preferences. The analysis unit can also apply a budget analysis algorithm to information about budgets. The analysis unit can also apply a schedule analysis algorithm to information about schedules. By applying different analysis algorithms depending on the category of information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the participant's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the participant's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the participant's past analysis results. The analysis unit can also determine the priority of the analysis based on the participant's past analysis results. In this way, the accuracy of the analysis is improved by referring to the participant's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the participant's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0076] The analysis unit can estimate the emotions of the participants and adjust the length of the analysis based on the estimated emotions of the participants. For example, if a participant is in a hurry, the analysis unit can provide a short and concise analysis result. If a participant is relaxed, the analysis unit can also provide a detailed analysis result. If a participant is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the emotions of the participants, more appropriate analysis results can be provided. The emotion estimation is realized 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 analysis unit can be performed using AI, or without AI. For example, the analysis unit can input facial expression data of the participants into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the priority of information that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also determine the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the participants' level of expertise. For example, if a participant has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if a participant does not have technical expertise, the analysis unit can provide analysis results in simpler terms. The analysis unit can also adjust the way the analysis results are expressed according to the participants' level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the participants' level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input participants' level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0080] The suggestion unit can estimate the emotions of the participants and adjust the way the suggestions are expressed based on the estimated emotions of the participants. For example, if the participants are nervous, the suggestion unit can make simple, highly visible suggestions. Furthermore, if the participants are relaxed, the suggestion unit can make detailed suggestions. Furthermore, if the participants are in a hurry, the suggestion unit can make suggestions that focus on the main points. By adjusting the way the suggestions are expressed based on the emotions of the participants, more appropriate suggestions can be made. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input facial expression data of the participants into the generation AI and cause the generation AI to estimate the emotions.

[0081] The suggestion unit can adjust the level of detail of the proposal based on the importance of the place or plan when making a proposal. For example, the suggestion unit makes a detailed proposal for an important place or plan. The suggestion unit can also make a simplified proposal for a less important place or plan. The suggestion unit can also determine the priority of the proposal according to the importance of the place or plan. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the place or plan. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the place or plan to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0082] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of the location or plan. For example, the suggestion unit can apply a restaurant suggestion algorithm to a proposal related to a restaurant. The suggestion unit can also apply an event suggestion algorithm to a proposal related to an event. The suggestion unit can also apply an accommodation suggestion algorithm to a proposal related to an accommodation. In this way, by applying different suggestion algorithms depending on the category of the location or plan, the accuracy of the proposal is improved. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input location or plan category data into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0083] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the participants' past proposal results. The proposal unit, for example, adjusts the proposal algorithm based on the participants' past proposal results. The proposal unit can also improve the accuracy of the proposal by referring to the participants' past proposal results. The proposal unit can also determine the priority of the proposal based on the participants' past proposal results. In this way, the accuracy of the proposal is improved by referring to the participants' past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the participants' past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0084] The suggestion unit can estimate the emotion of a participant and adjust the length of the suggestion based on the estimated emotion of the participant. For example, if a participant is in a hurry, the suggestion unit can make a short and to-the-point suggestion. Furthermore, if a participant is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if a participant is excited, the suggestion unit can make a visually stimulating suggestion. By adjusting the length of the suggestion according to the emotion of the participant, more appropriate suggestions can be made. The emotion estimation is realized 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 suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input facial expression data of the participant into the generation AI and cause the generation AI to estimate the emotion.

[0085] The proposal unit can determine the priority of proposals based on the time of submission of locations and plans when making proposals. For example, the proposal unit prioritizes the most recent locations and plans. The proposal unit can also lower the priority of locations and plans that have been submitted earlier. The proposal unit can also adjust the proposal schedule based on the time of submission. This enables efficient proposals by determining the priority of proposals based on the time of submission of locations and plans. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the time of submission of locations and plans into the generation AI and cause the generation AI to determine the priority of proposals.

[0086] The suggestion unit can adjust the order of suggestions based on the relevance of places and plans when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant places and plans. The suggestion unit can also postpone suggesting less relevant places and plans. The suggestion unit can also determine the order of suggestions based on the relevance of places and plans. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of places and plans. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of places and plans to a generation AI and cause the generation AI to adjust the order of suggestions.

[0087] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the participant's level of expertise. For example, if the participant has technical expertise, the suggestion unit can make a proposal that uses a lot of technical terminology. Also, if the participant does not have technical expertise, the suggestion unit can make a proposal in simple language. The suggestion unit can also adjust the way the proposal is expressed depending on the participant's level of expertise. This allows for more appropriate proposals to be made by adjusting the use of technical terminology in the proposal depending on the participant's level of expertise. 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 participant's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0088] The reservation unit can estimate the participant's emotions and adjust the reservation method based on the estimated participant's emotions. For example, if the participant is nervous, the reservation unit can provide a simple, highly visible reservation method. Furthermore, if the participant is relaxed, the reservation unit can provide a detailed reservation method. Furthermore, if the participant is in a hurry, the reservation unit can provide a reservation method that focuses on the key points. This allows for more appropriate reservations by adjusting the reservation method according to the participant'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 AI, for example, or without AI. For example, the reservation unit can input participant facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] When making a reservation, the reservation unit can analyze the participant's past reservation history and select an appropriate reservation method. The reservation unit, for example, selects the optimal reservation method based on the reservation methods used by the participant in the past. The reservation unit can also select a reservation method that avoids congestion from the participant's past reservation history. The reservation unit can also analyze the participant's past reservation history and select the most efficient reservation method. In this way, the optimal reservation method can be selected by analyzing the participant's past reservation history. 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 participant's past reservation history data into the generation AI and have the generation AI select the optimal reservation method.

[0090] The reservation unit can customize the reservation method based on the participant's current schedule when making a reservation. The reservation unit, for example, suggests the optimal reservation date and time based on the participant's schedule. The reservation unit can also customize the reservation method taking the participant's schedule into consideration. The reservation unit can also determine reservation priorities based on the participant's schedule. This enables efficient reservations by customizing the reservation method based on the participant's current schedule. 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 participant's schedule data into a generation AI and have the generation AI customize the reservation method.

[0091] The reservation unit can improve the reservation method by reflecting participant feedback at the time of reservation. The reservation unit can improve the reservation method, for example, based on participant past feedback. The reservation unit can also customize the reservation means by reflecting participant feedback. The reservation unit can also determine reservation priorities based on participant feedback. In this way, the reservation method can be improved by reflecting participant feedback. 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 participant feedback data into a generation AI and have the generation AI improve the reservation method.

[0092] The reservation unit can estimate the emotions of participants and prioritize reservations based on the estimated emotions of participants. For example, if a participant is nervous, the reservation unit can prioritize important reservations. Furthermore, if a participant is relaxed, the reservation unit can make detailed reservations. Furthermore, if a participant is in a hurry, the reservation unit can make reservations that focus on the essentials. This allows for more appropriate reservations by prioritizing reservations based on the emotions of participants. Emotion estimation is achieved using an emotion estimation function, such as 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, for example, AI, or without AI. For example, the reservation unit can input facial expression data of participants into the generation AI and have the generation AI perform emotion estimation.

[0093] When making a reservation, the reservation unit can select an appropriate reservation method by taking into account the geographical location information of the participant. The reservation unit selects the optimal reservation method based on, for example, the participant's current location. The reservation unit can also select a nearby reservation method based on the participant's geographical location information. The reservation unit can also select the optimal reservation method based on the participant's movement history. In this way, the optimal reservation method can be selected by taking into account the participant's geographical location information. 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 participant's geographical location data into the generation AI and have the generation AI select the optimal reservation method.

[0094] At the time of reservation, the reservation unit can analyze the participant's social media activity to suggest a reservation method. The reservation unit can suggest a reservation method, for example, based on the location where the participant checked in on social media. The reservation unit can also analyze the participant's social media posts to suggest the optimal reservation method. The reservation unit can also suggest a reservation method based on the activity of the participant's friends on social media. In this way, the optimal reservation method can be suggested by analyzing the participant's social media activity. 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 participant's social media data into a generation AI and have the generation AI suggest a reservation method.

[0095] The reservation unit can customize the reservation method by reflecting the participant's past feedback when making a reservation. The reservation unit, for example, selects the optimal reservation method based on the participant's past feedback. The reservation unit can also improve the reservation means by reflecting the participant's feedback. The reservation unit can also determine reservation priorities based on the participant's feedback. In this way, the optimal reservation method can be selected by reflecting the participant's past feedback. 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 participant's past 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 collection unit, analysis unit, proposal unit, and reservation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects participants' attendance confirmations and desired conditions using the reception device 38 of the smart device 14. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The proposal unit proposes an optimal location and plan based on the analysis results by the specific processing unit 290 of the data processing device 12. The reservation unit reserves the proposed location and plan using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, proposal 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 collection unit collects participants' attendance confirmations and desired conditions using the microphone 238 of the smart glasses 214. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The proposal unit proposes an optimal location and plan based on the analysis results by the specific processing unit 290 of the data processing device 12. The reservation unit reserves the proposed location and plan using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and reservation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects participants' attendance confirmations and desired conditions using the microphone 238 of the headset type terminal 314. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The proposal unit proposes an optimal location and plan based on the analysis results by the specific processing unit 290 of the data processing device 12. The reservation unit reserves the proposed location and plan using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal 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 collection unit collects participants' attendance confirmations and desired conditions using the microphone 238 of the robot 414. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The proposal unit proposes an optimal location and plan based on the analysis results by the specific processing unit 290 of the data processing device 12. The reservation unit reserves the proposed location and plan using the control unit 46A of the robot 414.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The collection unit can also monitor the health status of participants and adjust the timing of attendance confirmation based on their health status. For example, if a participant is feeling unwell, the collection unit can postpone attendance confirmation. Alternatively, if a participant is in good health, the collection unit can confirm attendance at the usual time. Furthermore, the collection unit can change the method of attendance confirmation depending on the participant's health status. This allows for flexible attendance confirmation based on the participant's health status.

[0098] The analysis unit can estimate the emotions of the participants and determine the priority of analysis based on the estimated emotions of the participants. For example, if a participant is feeling stressed, important analysis will be prioritized. The analysis unit can also perform detailed analysis if the participant is relaxed. Furthermore, the analysis unit can perform analysis that focuses on the main points if the participant is in a hurry. This allows for flexible analysis according to the emotions of the participants.

[0099] The suggestion unit can analyze the participants' past behavioral patterns and select the optimal suggestion method. For example, it makes suggestions based on the participants' past favorite locations and plans. The suggestion unit can also adjust the timing of suggestions based on the participants' past behavioral patterns. Furthermore, the suggestion unit can also customize the content of suggestions by referring to the participants' past behavioral patterns. This makes it possible to make optimal suggestions based on the participants' past behavioral patterns.

[0100] The reservation department can estimate the emotions of participants and adjust the reservation confirmation method based on the estimated emotions of the participants. For example, if a participant is nervous, it can provide a simple, highly visible confirmation method. If a participant is relaxed, the reservation department can also provide a detailed confirmation method. If a participant is in a hurry, the reservation department can also provide a confirmation method that focuses on the main points. This allows for flexible reservation confirmation according to the emotions of participants.

[0101] The collection unit can customize the content of the attendance confirmation taking into account the hobbies and interests of the participants. For example, if a participant is interested in music, the collection unit can send an attendance confirmation for a music event. If a participant is interested in sports, the collection unit can also send an attendance confirmation for a sports event. Furthermore, the collection unit can change the method of attendance confirmation based on the hobbies and interests of the participants. This allows for flexible attendance confirmation according to the hobbies and interests of the participants.

[0102] The analysis unit can estimate the emotions of the participants and adjust the analysis feedback method based on the estimated emotions of the participants. For example, if a participant is nervous, simple, highly visible feedback is provided. The analysis unit can also provide detailed feedback if a participant is relaxed. Furthermore, if a participant is in a hurry, the analysis unit can also provide feedback that focuses on the main points. This makes it possible to provide flexible feedback according to the emotions of the participants.

[0103] The suggestion unit can customize the content of the suggestion taking into account the participant's current mood and physical condition. For example, if the participant is tired, the suggestion unit can suggest a place or plan where the participant can relax. Alternatively, if the participant is energetic, the suggestion unit can suggest an active place or plan. Furthermore, the suggestion unit can adjust the timing of the suggestion depending on the participant's physical condition. This enables flexible suggestions to be made according to the participant's mood and physical condition.

[0104] The reservation department can select the optimal reservation confirmation method based on the participant's past reservation history. For example, the reservation confirmation is performed based on the participant's preferred confirmation method in the past. The reservation department can also adjust the timing of confirmation based on the participant's past reservation history. Furthermore, the reservation department can also customize the content of confirmation by referring to the participant's past reservation history. This makes it possible to optimally confirm reservations based on the participant's past reservation history.

[0105] The collection unit can estimate the emotions of the participants and adjust the format of the information to be collected based on the estimated emotions of the participants. For example, if a participant is nervous, the collection unit can collect information in a simple, highly visible format. If a participant is relaxed, the collection unit can also collect information in a detailed format. Furthermore, if a participant is in a hurry, the collection unit can also collect information in a format that focuses on the main points. This makes it possible to collect information flexibly according to the emotions of the participants.

[0106] The suggestion unit can select the optimal suggestion method based on the geographical location information of the participant. For example, it can suggest nearby places or plans based on the participant's current location. The suggestion unit can also make suggestions that take travel time into consideration based on the participant's geographical location information. Furthermore, the suggestion unit can also customize the content of the suggestions based on the participant's geographical location information. This makes it possible to make optimal suggestions based on the participant's geographical location information.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection department collects participants' attendance confirmations and desired conditions. Participants' attendance confirmations can be made via email, phone, online forms, etc. The collection department collects information such as participants' responses such as "I will attend," "My budget is within 3,000 yen," and "I prefer Japanese food." Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as data mining and statistical analysis. The analysis unit analyzes the data based on the participants' preferences, budget, and desired conditions. Step 3: The proposal section proposes the optimal location and plan based on the results obtained by the analysis section. The proposal will suggest "restaurants where you can enjoy Japanese food within a budget of 3,000 yen" based on the participants' desired conditions. The proposal section can also suggest the optimal date, time, and venue taking into account the participants' schedules. Step 4: The Reservations Department reserves the location and plan proposed by the Proposal Department. Reservations are made online, by phone, or other methods. The Reservations Department processes reservations based on a proposal such as, "Taking everyone's schedules into consideration, we'll make a reservation for next Friday at 7 p.m."

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects participants' attendance confirmations and desired conditions, an analysis unit that analyzes the information collected by the collection unit; a proposal unit that proposes an appropriate location and plan based on the results obtained by the analysis unit; a reservation unit that reserves the place or plan proposed by the proposal unit. A system characterized by:

2. The collecting unit Estimate participants' emotions and adjust the timing of attendance checks based on the estimated emotions of the participants.

2. The system of claim 1.

3. The collecting unit Analyze past attendance history of participants and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit Filter attendees based on their current projects and interests when RSVPing 2. The system of claim 1.

5. The collecting unit When confirming attendance, select the appropriate collection method depending on the participant's input method.

2. The system of claim 1.

6. The collecting unit Estimate participants' emotions and prioritize the information to be collected based on the estimated emotions of participants.

2. The system of claim 1.

7. The collecting unit Prioritize collection of relevant information when collecting attendance by taking into account attendees' geographic location 2. The system of claim 1.

8. The collecting unit Analyze attendees' social media activity and collect relevant information when RSVPing 2. The system of claim 1.

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