system

Abstract

The system according to the embodiment aims to automate the scheduling of participants and the selection of restaurants. [Solution] A system according to an embodiment includes a schedule acquisition unit, a restaurant selection unit, and an email sending unit. The schedule acquisition unit acquires the schedules of participants. The restaurant selection unit selects a restaurant based on specific conditions based on the schedule acquired by the schedule acquisition unit. The email sending unit sends emails to specific participants based on information about the restaurant selected by the restaurant selection 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 had the problem of being inefficient because participants' schedules and restaurant selection had to be done manually.

[0005] The system according to the embodiment aims to automate the scheduling of participants and the selection of restaurants. [Means for solving the problem]

[0006] The system according to the embodiment includes a schedule acquisition unit, a restaurant selection unit, and an email sending unit. The schedule acquisition unit acquires the schedules of participants. The restaurant selection unit selects a restaurant based on specific conditions and the schedule acquired by the schedule acquisition unit. The email sending unit sends emails to specific participants based on information about the restaurant selected by the restaurant selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the scheduling of participants and the selection of restaurants. [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) A schedule adjustment system according to an embodiment of the present invention allows users to set participants, budget, and location through a user interface (UI), reference Golden Week (GW) schedules to set candidate dates, select restaurants that meet the criteria, and automate schedule adjustments with participants via email. This system contributes to streamlining schedule adjustment work. First, a user sets participants, budget, and location through the UI. For example, the user sets "Mr. Tanaka" and "Mr. Suzuki" as participants, "within 5,000 yen" as budget, and "a specific area" as location. This information is entered into the system. Next, the system references the Golden Week schedule to set candidate dates. For example, the system sets "May 3, May 4, and May 5" as candidate dates. These candidate dates are then compared with the participants' schedules to select the optimal date. The system then selects restaurants that meet the set criteria. For example, the system searches for restaurants in a specific area within a budget of 5,000 yen and creates a list of restaurants that meet the criteria. This list is presented to the user. Finally, the system automates schedule adjustments with participants via email. For example, the system sends participants an email saying, "Please let us know which of May 3rd, May 4th, or May 5th is convenient for you," and receives their replies. Based on the replies, the system determines the optimal date and notifies them again by email. This system makes coordination work more efficient, allowing users to adjust schedules without hassle. In this way, the schedule coordination system makes coordination work more efficient, allowing users to adjust schedules without hassle.

[0029] A schedule adjustment system according to an embodiment includes a schedule acquisition unit, a restaurant selection unit, and an email sending unit. The schedule acquisition unit acquires the schedules of participants. For example, the schedule acquisition unit acquires the schedules of participants using a synchronization function of a calendar app. The schedule acquisition unit can also acquire the schedules of participants by manual input. The schedule acquisition unit can also acquire external schedule data using API integration. For example, the schedule acquisition unit automatically acquires the schedules of participants using the API of a calendar app. The restaurant selection unit selects a restaurant based on specific conditions based on the schedule acquired by the schedule acquisition unit. For example, the restaurant selection unit selects a restaurant based on conditions such as budget, location, and participant preferences. The restaurant selection unit searches for restaurants in a specific area within a budget of 5,000 yen, for example, and lists restaurants that meet the conditions. The restaurant selection unit can also select a restaurant based on participant preferences. For example, the restaurant selection unit selects the most suitable restaurant based on participants' past reviews. The email sending unit sends an email to a specific participant based on information about the restaurant selected by the restaurant selection unit. For example, the email sending unit sends an email containing information about the selected restaurant to the participants. The email sending unit also automates schedule coordination with participants via email. For example, the email sending unit sends an email to participants such as, "Please let us know which of May 3rd, May 4th, or May 5th is convenient for you," and receives a reply. As a result, the schedule coordination system according to the embodiment automates obtaining participants' schedules, selecting restaurants that meet the conditions, and sending emails, thereby improving the efficiency of coordination work.

[0030] The schedule adjustment system includes a participant information acquisition unit that acquires participant information. The participant information acquisition unit acquires participant information. For example, the participant information acquisition unit acquires information such as the participant's name, contact information, job title, and areas of interest. The participant information acquisition unit can also acquire the participant's past participation history. For example, the participant information acquisition unit acquires information about events and conferences that the participant has attended in the past. Furthermore, the participant information acquisition unit can also acquire information about the participant's preferences. For example, the participant information acquisition unit acquires information about the participant's favorite foods and the atmosphere of the restaurant. Thus, acquiring the participant information improves the accuracy of schedule acquisition and restaurant selection.

[0031] The schedule adjustment system includes a budget setting unit that sets a budget. The budget setting unit sets the budget. For example, the budget setting unit sets an upper limit. The budget setting unit can also set expense items. For example, the budget setting unit sets expense items such as meals, drinks, and transportation. The budget setting unit can also set a budget range. For example, the budget setting unit sets a budget range such as within 5,000 yen or within 10,000 yen. By setting a budget, it becomes possible to select a restaurant that meets the conditions.

[0032] The schedule adjustment system includes a location setting unit that sets a location. The location setting unit sets a location. For example, the location setting unit sets a geographical range. The location setting unit can also set ease of access. For example, the location setting unit sets access conditions such as within a five-minute walk from a station or within a ten-minute walk from a bus stop. Furthermore, the location setting unit can also set a location taking into account the participant's place of residence. For example, the location setting unit sets a location close to the participant's place of residence. In this way, by setting a location, it becomes possible to select a store that meets the conditions.

[0033] The schedule acquisition unit can set candidate dates by referring to the schedule for a specific consecutive holiday period. The schedule acquisition unit can set candidate dates by referring to, for example, the schedule for Golden Week. For example, the schedule acquisition unit can set May 3rd, May 4th, and May 5th as candidate dates. The schedule acquisition unit can also refer to the schedule for a specific consecutive holiday period, such as the New Year holidays or summer vacation. For example, the schedule acquisition unit can set the period from December 29th to January 3rd as candidate dates. In this way, by referring to the schedule for a specific consecutive holiday period, the setting of candidate dates can be made more efficient.

[0034] The restaurant selection unit can select a restaurant based on set conditions. The restaurant selection unit selects a restaurant based on conditions such as budget, location, and participant preferences. For example, the restaurant selection unit searches for restaurants in a specific area with a budget of 5,000 yen or less and lists restaurants that meet the conditions. The restaurant selection unit can also select a restaurant based on the participant's preferences. For example, the restaurant selection unit selects a restaurant based on the participant's preferred food or the restaurant's atmosphere. Furthermore, the restaurant selection unit can select a restaurant by referring to past selection history. For example, the restaurant selection unit selects the optimal restaurant based on information about restaurants that participants have rated in the past. In this way, by selecting a restaurant based on set conditions, it is possible to select a restaurant that meets the conditions.

[0035] The email sending unit can automate schedule coordination with participants via email. The email sending unit, for example, sends schedule coordination emails to participants. For example, the email sending unit sends an email to participants saying, "Please let us know which of May 3rd, May 4th, or May 5th is convenient for you." The email sending unit can also receive replies from participants and determine the optimal date. For example, the email sending unit selects the optimal date based on the participants' replies and notifies them again by email. Furthermore, the email sending unit can adjust the timing of email transmission. For example, the email sending unit sends emails at the optimal time based on the participants' schedules. In this way, automating schedule coordination via email improves the efficiency of coordination work.

[0036] The schedule acquisition unit can analyze the participant's past schedule history and select the most appropriate acquisition method. The schedule acquisition unit, for example, acquires schedules by prioritizing time periods that the participant has frequently used in the past. For example, the schedule acquisition unit improves the efficiency of schedule acquisition by acquiring schedules by prioritizing time periods that the participant has frequently used in the past. The schedule acquisition unit can also select the most efficient acquisition method from the participant's past schedule history. For example, the schedule acquisition unit selects the optimal acquisition method based on the participant's past schedule history. Furthermore, the schedule acquisition unit can also select the optimal acquisition timing based on the participant's past schedule history. For example, the schedule acquisition unit analyzes the participant's past schedule history and selects the optimal acquisition timing. In this way, the optimal acquisition method can be selected by analyzing the past schedule history.

[0037] When acquiring a schedule, the schedule acquisition unit can perform filtering based on the participant's current project or area of ​​interest. The schedule acquisition unit, for example, prioritizes acquiring schedules related to the project the participant is currently working on. For example, the schedule acquisition unit improves the efficiency of schedule acquisition by prioritizing acquiring schedules related to the project the participant is currently working on. The schedule acquisition unit can also prioritize acquiring schedules related to the participant's area of ​​interest. For example, the schedule acquisition unit improves the accuracy of schedule acquisition by prioritizing acquiring schedules related to the participant's area of ​​interest. Furthermore, the schedule acquisition unit can filter schedules based on the progress of the participant's current project. For example, the schedule acquisition unit acquires highly relevant schedules by filtering schedules based on the progress of the participant's current project. In this way, highly relevant schedules can be acquired by filtering based on the participant's current project or area of ​​interest.

[0038] When acquiring a schedule, the schedule acquisition unit can prioritize acquiring highly relevant schedules by taking into account the geographical location information of the participant. The schedule acquisition unit, for example, prioritizes acquiring schedules close to the participant's current location. For example, the schedule acquisition unit improves the efficiency of schedule acquisition by prioritizing acquiring schedules close to the participant's current location. The schedule acquisition unit can also prioritize acquiring highly relevant schedules based on the participant's geographical location information. For example, the schedule acquisition unit improves the accuracy of schedule acquisition by prioritizing acquiring highly relevant schedules based on the participant's geographical location information. Furthermore, when a participant is traveling, the schedule acquisition unit can also prioritize acquiring schedules related to the participant's destination. For example, when a participant is traveling, the schedule acquisition unit prioritizes acquiring schedules related to the participant's destination to acquire highly relevant schedules. In this way, highly relevant schedules can be prioritized by taking into account the geographical location information.

[0039] The schedule acquisition unit can analyze the social media activities of participants when acquiring a schedule and acquire related schedules. The schedule acquisition unit, for example, acquires schedules of related events and meetings from the social media activities of participants. For example, the schedule acquisition unit improves the efficiency of schedule acquisition by acquiring schedules of related events and meetings from the social media activities of participants. The schedule acquisition unit can also acquire schedules based on the interests of participants on social media. For example, the schedule acquisition unit improves the accuracy of schedule acquisition by acquiring schedules based on the interests of participants on social media. Furthermore, the schedule acquisition unit can analyze the content of participants' posts on social media and acquire related schedules. For example, the schedule acquisition unit analyzes the content of participants' posts on social media and acquires related schedules, thereby acquiring highly relevant schedules. In this way, related schedules can be acquired by analyzing social media activities.

[0040] When selecting a restaurant, the restaurant selection unit can refer to past selection history and apply the most appropriate selection algorithm. The restaurant selection unit, for example, selects the optimal restaurant based on the participant's ratings of restaurants that the participant has visited in the past. For example, the restaurant selection unit improves the accuracy of restaurant selection by selecting the optimal restaurant based on the participant's ratings of restaurants that the participant has visited in the past. The restaurant selection unit can also select the restaurant with the highest rating from the participant's past selection history. For example, the restaurant selection unit improves the efficiency of restaurant selection by selecting the restaurant with the highest rating from the participant's past selection history. Furthermore, the restaurant selection unit can analyze the participant's past selection history and apply the optimal selection algorithm. For example, the restaurant selection unit analyzes the participant's past selection history and applies the optimal selection algorithm to select a highly relevant restaurant. In this way, the optimal selection algorithm can be applied by referring to the past selection history.

[0041] When selecting a restaurant, the restaurant selection unit can take into consideration the preferences and past ratings of the participants. The restaurant selection unit selects the optimal restaurant based on, for example, the preferences of the participants. For example, the restaurant selection unit improves the accuracy of restaurant selection by selecting the optimal restaurant based on the preferences of the participants. The restaurant selection unit can also select the restaurant with the highest rating based on the past ratings of the participants. For example, the restaurant selection unit improves the efficiency of restaurant selection by selecting the restaurant with the highest rating based on the past ratings of the participants. Furthermore, the restaurant selection unit can analyze the preferences and past ratings of the participants to select the optimal restaurant. For example, the restaurant selection unit selects a restaurant with high relevance by analyzing the preferences and past ratings of the participants and selecting the optimal restaurant. This allows a more appropriate restaurant to be selected by taking into consideration the preferences and past ratings of the participants.

[0042] When selecting a restaurant, the restaurant selection unit can select the optimal restaurant by taking geographical distribution into consideration. For example, the restaurant selection unit prioritizes selecting restaurants that are close to the participant's current location. For example, by prioritizing selecting restaurants that are close to the participant's current location, the restaurant selection unit improves the efficiency of restaurant selection. The restaurant selection unit can also select the optimal restaurant by taking into consideration the participant's travel range. For example, by selecting the optimal restaurant by taking into consideration the participant's travel range, the restaurant selection unit improves the accuracy of restaurant selection. Furthermore, the restaurant selection unit can also select the optimal restaurant based on the participant's geographical distribution. For example, by selecting the optimal restaurant based on the participant's geographical distribution, the restaurant selection unit selects a restaurant that is highly relevant. In this way, the optimal restaurant can be selected by taking geographical distribution into consideration.

[0043] The restaurant selection unit can improve the accuracy of the selection by referring to related reviews and ratings when selecting a restaurant. The restaurant selection unit, for example, selects the optimal restaurant based on reviews of restaurants previously rated by participants. For example, the restaurant selection unit improves the accuracy of restaurant selection by selecting the optimal restaurant based on reviews of restaurants previously rated by participants. The restaurant selection unit can also make a selection by referring to reviews of restaurants that match the preferences of the participants. For example, the restaurant selection unit improves the efficiency of restaurant selection by making a selection by referring to reviews of restaurants that match the preferences of the participants. Furthermore, the restaurant selection unit can also select the restaurant with the highest rating based on the participants' past ratings. For example, the restaurant selection unit selects a restaurant with the highest rating based on the participants' past ratings, thereby selecting a restaurant with the highest rating. In this way, the accuracy of the selection is improved by referring to related reviews and ratings.

[0044] When sending an email, the email sending unit can select the optimal sending method by referring to past sending history. The email sending unit selects the optimal sending method based on, for example, the email format that the participant has preferred in the past. For example, the email sending unit improves the accuracy of email sending by selecting the optimal sending method based on the email format that the participant has preferred in the past. The email sending unit can also select the most effective sending method from the participant's past sending history. For example, the email sending unit improves the efficiency of email sending by selecting the most effective sending method from the participant's past sending history. Furthermore, the email sending unit can analyze the participant's past sending history and select the optimal sending method. For example, the email sending unit analyzes the participant's past sending history and selects the optimal sending method, thereby sending highly relevant emails. In this way, the optimal sending method can be selected by referring to the past sending history.

[0045] When sending an email, the email sending unit can customize the expression style of the email according to the communication style of the participant. For example, if a participant prefers a formal communication style, the email sending unit sends the email using a formal expression style. For example, if a participant prefers a formal communication style, the email sending unit sends the email using a formal expression style, thereby improving the satisfaction of the participant. Furthermore, if a participant prefers a casual communication style, the email sending unit can also send the email using a casual expression style. For example, if a participant prefers a casual communication style, the email sending unit sends the email using a casual expression style, thereby improving the satisfaction of the participant. Furthermore, the email sending unit can also send the email using an optimal expression style according to the communication style of the participant. For example, the email sending unit sends a highly relevant email by sending the email using an optimal expression style according to the communication style of the participant. This enables more effective communication by sending the email using an expression style according to the communication style.

[0046] When sending an email, the email sending unit can select the optimal sending timing by taking into account the geographical location information of the participant. The email sending unit selects the optimal sending timing by taking into account, for example, the time zone of the participant's current location. For example, the email sending unit improves the efficiency of email sending by selecting the optimal sending timing by taking into account the time zone of the participant's current location. The email sending unit can also select the most effective sending timing based on the geographical location information of the participant. For example, the email sending unit improves the accuracy of email sending by selecting the most effective sending timing based on the geographical location information of the participant. Furthermore, if a participant is traveling, the email sending unit can also select the sending timing by taking into account the time zone of the destination. For example, if a participant is traveling, the email sending unit selects the sending timing by taking into account the time zone of the destination, thereby sending a highly relevant email. In this way, the optimal sending timing can be selected by taking into account the geographical location information.

[0047] The email sending unit can analyze the participants' social media activities to generate optimal email content when sending emails. The email sending unit, for example, generates emails including content of interest from the participants' social media activities. For example, the email sending unit improves the accuracy of email sending by generating emails including content of interest from the participants' social media activities. The email sending unit can also generate optimal email content based on the participants' social media posts. For example, the email sending unit improves the efficiency of email sending by generating optimal email content based on the participants' social media posts. Furthermore, the email sending unit can analyze the participants' social media activities to generate the most effective email content. For example, the email sending unit analyzes the participants' social media activities to generate the most effective email content, thereby sending highly relevant emails. In this way, optimal email content can be generated by analyzing social media activities.

[0048] The participant information acquisition unit can analyze the participant's past information history and select the optimal acquisition method. For example, the participant information acquisition unit prioritizes the selection of an information acquisition method that the participant has frequently used in the past. For example, the participant information acquisition unit improves the efficiency of information acquisition by prioritizing the selection of an information acquisition method that the participant has frequently used in the past. The participant information acquisition unit can also select the most efficient acquisition method from the participant's past information history. For example, the participant information acquisition unit selects the optimal acquisition method based on the participant's past information history. Furthermore, the participant information acquisition unit can also select the optimal acquisition timing based on the participant's past information history. For example, the participant information acquisition unit analyzes the participant's past information history and selects the optimal acquisition timing. In this way, the optimal acquisition method can be selected by analyzing the past information history.

[0049] When acquiring information, the participant information acquisition unit can prioritize acquiring highly relevant information by taking into account the geographical location information of the participant. The participant information acquisition unit, for example, prioritizes acquiring information related to the participant's current location. For example, the participant information acquisition unit improves the efficiency of information acquisition by prioritizing acquiring information related to the participant's current location. The participant information acquisition unit can also prioritize acquiring highly relevant information based on the participant's geographical location information. For example, the participant information acquisition unit improves the accuracy of information acquisition by prioritizing acquiring highly relevant information based on the participant's geographical location information. Furthermore, when a participant is traveling, the participant information acquisition unit can also prioritize acquiring information related to the participant's destination. For example, when a participant is traveling, the participant information acquisition unit prioritizes acquiring information related to the participant's destination to acquire highly relevant information. In this way, highly relevant information can be prioritized by taking into account the geographical location information.

[0050] The budget setting unit can select the optimal setting method by referring to past budget history when setting a budget. The budget setting unit, for example, sets the optimal budget based on the past budget history of the participants. For example, the budget setting unit improves the accuracy of budget setting by setting the optimal budget based on the past budget history of the participants. The budget setting unit can also set the optimal budget by referring to budgets set by the participants in the past. For example, the budget setting unit improves the efficiency of budget setting by setting the optimal budget by referring to budgets set by the participants in the past. Furthermore, the budget setting unit can analyze the past budget history of the participants and select the optimal setting method. For example, the budget setting unit analyzes the past budget history of the participants and selects the optimal setting method, thereby setting a highly relevant budget. In this way, the optimal setting method can be selected by referring to the past budget history.

[0051] When setting a budget, the budget setting unit can set an optimal budget taking into account the geographical location information of the participant. The budget setting unit, for example, sets the budget taking into account the prices in the location where the participant is currently located. For example, the budget setting unit improves the accuracy of budget setting by setting the budget taking into account the prices in the location where the participant is currently located. The budget setting unit can also set an optimal budget based on the geographical location information of the participant. For example, the budget setting unit improves the efficiency of budget setting by setting an optimal budget based on the geographical location information of the participant. Furthermore, when a participant is traveling, the budget setting unit can also set the budget taking into account the prices in the destination. For example, when a participant is traveling, the budget setting unit sets a highly relevant budget by taking into account the prices in the destination. In this way, the optimal budget can be set by taking into account the geographical location information.

[0052] When setting a location, the location setting unit can select an optimal setting method by referring to past location history. The location setting unit sets an optimal location based on, for example, the past location history of the participant. For example, the location setting unit improves the accuracy of location setting by setting an optimal location based on the past location history of the participant. The location setting unit can also set an optimal location by referring to places visited by the participant in the past. For example, the location setting unit improves the efficiency of location setting by setting an optimal location by referring to places visited by the participant in the past. Furthermore, the location setting unit can analyze the past location history of the participant and select an optimal setting method. For example, the location setting unit analyzes the past location history of the participant and selects an optimal setting method to set a highly relevant location. In this way, the optimal setting method can be selected by referring to the past location history.

[0053] When setting a location, the location setting unit can set an optimal location taking into consideration the geographical location information of the participant. For example, the location setting unit prioritizes setting a location near the participant's current location. For example, by prioritizing setting a location near the participant's current location, the location setting unit improves the efficiency of location setting. The location setting unit can also set an optimal location based on the participant's geographical location information. For example, by setting an optimal location based on the participant's geographical location information, the location setting unit improves the accuracy of location setting. Furthermore, when a participant is traveling, the location setting unit can also prioritize setting a location near the participant's destination. For example, when a participant is traveling, the location setting unit prioritizes setting a location near the participant's destination, thereby setting a highly relevant location. In this way, the optimal location can be set by taking geographical location information into consideration.

[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 schedule adjustment system can further include a hobby information acquisition unit that takes into account the hobbies and interests of participants. The hobby information acquisition unit acquires, for example, information about activities and events that participants like, and proposes a schedule based on this. For example, if a participant likes music concerts, the schedule can be adjusted to prioritize concert dates. Also, if a participant is interested in sporting events, the schedule can be set taking into account the dates of those events. Furthermore, the hobby information acquisition unit can analyze the participant's past hobby activity history and propose an optimal schedule. This makes it possible to adjust the schedule to reflect the participant's hobbies and interests.

[0056] The schedule adjustment system can further include a transportation consideration unit that considers the transportation means of the participants. The transportation consideration unit adjusts the schedule based on, for example, the transportation means used by the participants (car, train, bicycle, etc.). For example, if the participants will be using a car, the schedule can be set taking into consideration the availability of parking spaces and traffic congestion. Also, if the participants will be using a train, the schedule can be adjusted to match the train schedule. Furthermore, the transportation consideration unit can propose an optimal route to minimize the travel time of the participants. This makes it possible to adjust the schedule taking into consideration the transportation means of the participants.

[0057] The schedule adjustment system can further include a dietary restriction consideration unit that takes into account the dietary restrictions of participants. For example, if a participant has allergies or specific dietary restrictions (vegetarian, gluten-free, etc.), the dietary restriction consideration unit selects a restaurant based on this. For example, if a participant requires gluten-free food, it can prioritize restaurants that offer gluten-free menus. It can also select restaurants that offer safe meals by taking allergy information into consideration. Furthermore, the dietary restriction consideration unit can analyze the participant's past eating history and suggest the most suitable restaurant. This makes it possible to select a restaurant that takes into account the participant's dietary restrictions.

[0058] The schedule adjustment system can also analyze participants' past schedule history and propose optimal schedule patterns. For example, it can analyze the patterns of events and meetings that participants have preferred to attend in the past and propose schedules based on this. It can also set optimal schedules by taking into account time periods and dates that participants have avoided in the past. Furthermore, it can propose schedules that reflect participants' preferences and tendencies based on past schedule history. In this way, it is possible to provide more appropriate schedules by utilizing past schedule history.

[0059] The schedule adjustment system can further propose the optimal meeting place by taking into account the geographical location information of the participants. For example, if the participants live in different areas, it can propose an intermediate location that is easy for everyone to meet. It can also select the optimal meeting place to minimize the participants' travel time. Furthermore, it can propose a location with good transportation access based on the participants' geographical location information. This makes it possible to propose the optimal meeting place by taking into account the participants' geographical location information.

[0060] The schedule adjustment system can further analyze participants' social media activities to suggest related events and meetings. For example, it can suggest schedules based on events and meetings that participants have expressed interest in on social media. It can also suggest related events taking into account participants' social media interests. It can also analyze participants' social media activity history to suggest optimal schedules. This makes it possible to provide more relevant schedules by utilizing social media activities.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The schedule acquisition unit acquires the schedules of the participants. For example, it acquires the schedules of the participants using the synchronization function of a calendar app. It is also possible to acquire external schedule data by manually entering it or by using API integration. Step 2: The restaurant selection unit selects restaurants based on specific conditions, based on the schedule acquired by the schedule acquisition unit. For example, restaurants are selected based on conditions such as budget, location, and participants' preferences, and restaurants that meet the conditions are listed. The optimal restaurant can also be selected based on participants' past ratings. Step 3: The email sending unit sends emails to specific participants based on the information about the restaurant selected by the restaurant selection unit. For example, the email sending unit sends emails containing information about the selected restaurant to participants, automating schedule coordination with participants via email.

[0063] (Example 2) A schedule adjustment system according to an embodiment of the present invention allows users to set participants, budget, and location through a user interface (UI), reference Golden Week (GW) schedules to set candidate dates, select restaurants that meet the criteria, and automate schedule adjustments with participants via email. This system contributes to streamlining schedule adjustment work. First, a user sets participants, budget, and location through the UI. For example, the user sets "Mr. Tanaka" and "Mr. Suzuki" as participants, "within 5,000 yen" as budget, and "a specific area" as location. This information is entered into the system. Next, the system references the Golden Week schedule to set candidate dates. For example, the system sets "May 3, May 4, and May 5" as candidate dates. These candidate dates are then compared with the participants' schedules to select the optimal date. The system then selects restaurants that meet the set criteria. For example, the system searches for restaurants in a specific area within a budget of 5,000 yen and creates a list of restaurants that meet the criteria. This list is presented to the user. Finally, the system automates schedule adjustments with participants via email. For example, the system sends participants an email saying, "Please let us know which of May 3rd, May 4th, or May 5th is convenient for you," and receives their replies. Based on the replies, the system determines the optimal date and notifies them again by email. This system makes coordination work more efficient, allowing users to adjust schedules without hassle. In this way, the schedule coordination system makes coordination work more efficient, allowing users to adjust schedules without hassle.

[0064] A schedule adjustment system according to an embodiment includes a schedule acquisition unit, a restaurant selection unit, and an email sending unit. The schedule acquisition unit acquires the schedules of participants. For example, the schedule acquisition unit acquires the schedules of participants using a synchronization function of a calendar app. The schedule acquisition unit can also acquire the schedules of participants by manual input. The schedule acquisition unit can also acquire external schedule data using API integration. For example, the schedule acquisition unit automatically acquires the schedules of participants using the API of a calendar app. The restaurant selection unit selects a restaurant based on specific conditions based on the schedule acquired by the schedule acquisition unit. For example, the restaurant selection unit selects a restaurant based on conditions such as budget, location, and participant preferences. The restaurant selection unit searches for restaurants in a specific area within a budget of 5,000 yen, for example, and lists restaurants that meet the conditions. The restaurant selection unit can also select a restaurant based on participant preferences. For example, the restaurant selection unit selects the most suitable restaurant based on participants' past reviews. The email sending unit sends an email to a specific participant based on information about the restaurant selected by the restaurant selection unit. For example, the email sending unit sends an email containing information about the selected restaurant to the participants. The email sending unit also automates schedule coordination with participants via email. For example, the email sending unit sends an email to participants such as, "Please let us know which of May 3rd, May 4th, or May 5th is convenient for you," and receives a reply. As a result, the schedule coordination system according to the embodiment automates obtaining participants' schedules, selecting restaurants that meet the conditions, and sending emails, thereby improving the efficiency of coordination work.

[0065] The schedule adjustment system includes a participant information acquisition unit that acquires participant information. The participant information acquisition unit acquires participant information. For example, the participant information acquisition unit acquires information such as the participant's name, contact information, job title, and areas of interest. The participant information acquisition unit can also acquire the participant's past participation history. For example, the participant information acquisition unit acquires information about events and conferences that the participant has attended in the past. Furthermore, the participant information acquisition unit can also acquire information about the participant's preferences. For example, the participant information acquisition unit acquires information about the participant's favorite foods and the atmosphere of the restaurant. Thus, acquiring the participant information improves the accuracy of schedule acquisition and restaurant selection.

[0066] The schedule adjustment system includes a budget setting unit that sets a budget. The budget setting unit sets the budget. For example, the budget setting unit sets an upper limit. The budget setting unit can also set expense items. For example, the budget setting unit sets expense items such as meals, drinks, and transportation. The budget setting unit can also set a budget range. For example, the budget setting unit sets a budget range such as within 5,000 yen or within 10,000 yen. By setting a budget, it becomes possible to select a restaurant that meets the conditions.

[0067] The schedule adjustment system includes a location setting unit that sets a location. The location setting unit sets a location. For example, the location setting unit sets a geographical range. The location setting unit can also set ease of access. For example, the location setting unit sets access conditions such as within a five-minute walk from a station or within a ten-minute walk from a bus stop. Furthermore, the location setting unit can also set a location taking into account the participant's place of residence. For example, the location setting unit sets a location close to the participant's place of residence. In this way, by setting a location, it becomes possible to select a store that meets the conditions.

[0068] The schedule acquisition unit can set candidate dates by referring to the schedule for a specific consecutive holiday period. The schedule acquisition unit can set candidate dates by referring to, for example, the schedule for Golden Week. For example, the schedule acquisition unit can set May 3rd, May 4th, and May 5th as candidate dates. The schedule acquisition unit can also refer to the schedule for a specific consecutive holiday period, such as the New Year holidays or summer vacation. For example, the schedule acquisition unit can set the period from December 29th to January 3rd as candidate dates. In this way, by referring to the schedule for a specific consecutive holiday period, the setting of candidate dates can be made more efficient.

[0069] The restaurant selection unit can select a restaurant based on set conditions. The restaurant selection unit selects a restaurant based on conditions such as budget, location, and participant preferences. For example, the restaurant selection unit searches for restaurants in a specific area with a budget of 5,000 yen or less and lists restaurants that meet the conditions. The restaurant selection unit can also select a restaurant based on the participant's preferences. For example, the restaurant selection unit selects a restaurant based on the participant's preferred food or the restaurant's atmosphere. Furthermore, the restaurant selection unit can select a restaurant by referring to past selection history. For example, the restaurant selection unit selects the optimal restaurant based on information about restaurants that participants have rated in the past. In this way, by selecting a restaurant based on set conditions, it is possible to select a restaurant that meets the conditions.

[0070] The email sending unit can automate schedule coordination with participants via email. The email sending unit, for example, sends schedule coordination emails to participants. For example, the email sending unit sends an email to participants saying, "Please let us know which of May 3rd, May 4th, or May 5th is convenient for you." The email sending unit can also receive replies from participants and determine the optimal date. For example, the email sending unit selects the optimal date based on the participants' replies and notifies them again by email. Furthermore, the email sending unit can adjust the timing of email transmission. For example, the email sending unit sends emails at the optimal time based on the participants' schedules. In this way, automating schedule coordination via email improves the efficiency of coordination work.

[0071] The schedule acquisition unit can estimate the emotions of the participants and adjust the timing of schedule acquisition based on the estimated emotions. For example, if a participant is feeling stressed, the schedule acquisition unit acquires the schedule during a time period when the participant is relaxed. For example, the schedule acquisition unit acquires the schedule during a time period when the participant is relaxed, thereby reducing the burden on the participant. Furthermore, if a participant is busy, the schedule acquisition unit can acquire the schedule during a time period when the participant is free. For example, the schedule acquisition unit acquires the schedule during a time period when the participant is free, thereby improving the efficiency of schedule acquisition. Furthermore, the schedule acquisition unit can acquire the schedule immediately if the participant is relaxed. For example, the schedule acquisition unit acquires the schedule during a time period when the participant is relaxed, thereby improving the accuracy of schedule acquisition. In this way, by adjusting the timing of schedule acquisition based on the emotions of the participants, it is possible to acquire the schedule at a more appropriate time.

[0072] The schedule acquisition unit can analyze the participant's past schedule history and select the most appropriate acquisition method. The schedule acquisition unit, for example, acquires schedules by prioritizing time periods that the participant has frequently used in the past. For example, the schedule acquisition unit improves the efficiency of schedule acquisition by acquiring schedules by prioritizing time periods that the participant has frequently used in the past. The schedule acquisition unit can also select the most efficient acquisition method from the participant's past schedule history. For example, the schedule acquisition unit selects the optimal acquisition method based on the participant's past schedule history. Furthermore, the schedule acquisition unit can also select the optimal acquisition timing based on the participant's past schedule history. For example, the schedule acquisition unit analyzes the participant's past schedule history and selects the optimal acquisition timing. In this way, the optimal acquisition method can be selected by analyzing the past schedule history.

[0073] When acquiring a schedule, the schedule acquisition unit can perform filtering based on the participant's current project or area of ​​interest. The schedule acquisition unit, for example, prioritizes acquiring schedules related to the project the participant is currently working on. For example, the schedule acquisition unit improves the efficiency of schedule acquisition by prioritizing acquiring schedules related to the project the participant is currently working on. The schedule acquisition unit can also prioritize acquiring schedules related to the participant's area of ​​interest. For example, the schedule acquisition unit improves the accuracy of schedule acquisition by prioritizing acquiring schedules related to the participant's area of ​​interest. Furthermore, the schedule acquisition unit can filter schedules based on the progress of the participant's current project. For example, the schedule acquisition unit acquires highly relevant schedules by filtering schedules based on the progress of the participant's current project. In this way, highly relevant schedules can be acquired by filtering based on the participant's current project or area of ​​interest.

[0074] The schedule acquisition unit can estimate the emotions of the participants and determine the priority of schedules to be acquired based on the estimated emotions. For example, if a participant is feeling stressed, the schedule acquisition unit postpones less important schedules. For example, if a participant is feeling stressed, the schedule acquisition unit postpones less important schedules, thereby reducing the burden on the participant. The schedule acquisition unit can also prioritize acquiring more important schedules when a participant is relaxed. For example, if a participant is relaxed, the schedule acquisition unit prioritizes acquiring more important schedules, thereby improving the efficiency of schedule acquisition. Furthermore, if a participant is busy, the schedule acquisition unit can also prioritize acquiring the most important schedules. For example, if a participant is busy, the schedule acquisition unit prioritizes acquiring the most important schedules, thereby improving the accuracy of schedule acquisition. In this way, a more appropriate schedule can be acquired by determining the priority of schedules based on the emotions of the participants.

[0075] When acquiring a schedule, the schedule acquisition unit can prioritize acquiring highly relevant schedules by taking into account the geographical location information of the participant. The schedule acquisition unit, for example, prioritizes acquiring schedules close to the participant's current location. For example, the schedule acquisition unit improves the efficiency of schedule acquisition by prioritizing acquiring schedules close to the participant's current location. The schedule acquisition unit can also prioritize acquiring highly relevant schedules based on the participant's geographical location information. For example, the schedule acquisition unit improves the accuracy of schedule acquisition by prioritizing acquiring highly relevant schedules based on the participant's geographical location information. Furthermore, when a participant is traveling, the schedule acquisition unit can also prioritize acquiring schedules related to the participant's destination. For example, when a participant is traveling, the schedule acquisition unit prioritizes acquiring schedules related to the participant's destination to acquire highly relevant schedules. In this way, highly relevant schedules can be prioritized by taking into account the geographical location information.

[0076] The schedule acquisition unit can analyze the social media activities of participants when acquiring a schedule and acquire related schedules. The schedule acquisition unit, for example, acquires schedules of related events and meetings from the social media activities of participants. For example, the schedule acquisition unit improves the efficiency of schedule acquisition by acquiring schedules of related events and meetings from the social media activities of participants. The schedule acquisition unit can also acquire schedules based on the interests of participants on social media. For example, the schedule acquisition unit improves the accuracy of schedule acquisition by acquiring schedules based on the interests of participants on social media. Furthermore, the schedule acquisition unit can analyze the content of participants' posts on social media and acquire related schedules. For example, the schedule acquisition unit analyzes the content of participants' posts on social media and acquires related schedules, thereby acquiring highly relevant schedules. In this way, related schedules can be acquired by analyzing social media activities.

[0077] The store selection unit can estimate the emotions of the participants and adjust the store selection criteria based on the estimated emotions. For example, if the participants are relaxed, the store selection unit selects a store with a calm atmosphere. For example, if the participants are relaxed, the store selection unit selects a store with a calm atmosphere, thereby improving the participant's satisfaction. Furthermore, if the participants are excited, the store selection unit can select a lively store. For example, if the participants are excited, the store selection unit selects a lively store, thereby improving the participant's satisfaction. Furthermore, if the participants are stressed, the store selection unit can select a quiet store. For example, if the participants are stressed, the store selection unit selects a quiet store, thereby improving the participant's satisfaction. In this way, by adjusting the store selection criteria based on the participants' emotions, a more appropriate store can be selected.

[0078] When selecting a restaurant, the restaurant selection unit can refer to past selection history and apply the most appropriate selection algorithm. The restaurant selection unit, for example, selects the optimal restaurant based on the participant's ratings of restaurants that the participant has visited in the past. For example, the restaurant selection unit improves the accuracy of restaurant selection by selecting the optimal restaurant based on the participant's ratings of restaurants that the participant has visited in the past. The restaurant selection unit can also select the restaurant with the highest rating from the participant's past selection history. For example, the restaurant selection unit improves the efficiency of restaurant selection by selecting the restaurant with the highest rating from the participant's past selection history. Furthermore, the restaurant selection unit can analyze the participant's past selection history and apply the optimal selection algorithm. For example, the restaurant selection unit analyzes the participant's past selection history and applies the optimal selection algorithm to select a highly relevant restaurant. In this way, the optimal selection algorithm can be applied by referring to the past selection history.

[0079] When selecting a restaurant, the restaurant selection unit can take into consideration the preferences and past ratings of the participants. The restaurant selection unit selects the optimal restaurant based on, for example, the preferences of the participants. For example, the restaurant selection unit improves the accuracy of restaurant selection by selecting the optimal restaurant based on the preferences of the participants. The restaurant selection unit can also select the restaurant with the highest rating based on the past ratings of the participants. For example, the restaurant selection unit improves the efficiency of restaurant selection by selecting the restaurant with the highest rating based on the past ratings of the participants. Furthermore, the restaurant selection unit can analyze the preferences and past ratings of the participants to select the optimal restaurant. For example, the restaurant selection unit selects a restaurant with high relevance by analyzing the preferences and past ratings of the participants and selecting the optimal restaurant. This allows a more appropriate restaurant to be selected by taking into consideration the preferences and past ratings of the participants.

[0080] The establishment selection unit can estimate the emotions of the participants and determine a priority order for establishments to be selected based on the estimated emotions. For example, if the participants are relaxed, the establishment selection unit prioritizes selecting establishments that allow them to relax. For example, if the participants are relaxed, the establishment selection unit prioritizes selecting establishments that allow them to relax, thereby improving the participants' satisfaction. Furthermore, if the participants are excited, the establishment selection unit can also prioritize selecting establishments that are lively. For example, if the participants are excited, the establishment selection unit prioritizes selecting establishments that are lively, thereby improving the participants' satisfaction. Furthermore, if the participants are stressed, the establishment selection unit can also prioritize selecting establishments that are quiet. For example, if the participants are stressed, the establishment selection unit prioritizes selecting establishments that are quiet, thereby improving the participants' satisfaction. In this way, by prioritizing establishments based on the participants' emotions, a more appropriate establishment can be selected.

[0081] When selecting a restaurant, the restaurant selection unit can select the optimal restaurant by taking geographical distribution into consideration. For example, the restaurant selection unit prioritizes selecting restaurants that are close to the participant's current location. For example, by prioritizing selecting restaurants that are close to the participant's current location, the restaurant selection unit improves the efficiency of restaurant selection. The restaurant selection unit can also select the optimal restaurant by taking into consideration the participant's travel range. For example, by selecting the optimal restaurant by taking into consideration the participant's travel range, the restaurant selection unit improves the accuracy of restaurant selection. Furthermore, the restaurant selection unit can also select the optimal restaurant based on the participant's geographical distribution. For example, by selecting the optimal restaurant based on the participant's geographical distribution, the restaurant selection unit selects a restaurant that is highly relevant. In this way, the optimal restaurant can be selected by taking geographical distribution into consideration.

[0082] The restaurant selection unit can improve the accuracy of the selection by referring to related reviews and ratings when selecting a restaurant. The restaurant selection unit, for example, selects the optimal restaurant based on reviews of restaurants previously rated by participants. For example, the restaurant selection unit improves the accuracy of restaurant selection by selecting the optimal restaurant based on reviews of restaurants previously rated by participants. The restaurant selection unit can also make a selection by referring to reviews of restaurants that match the preferences of the participants. For example, the restaurant selection unit improves the efficiency of restaurant selection by making a selection by referring to reviews of restaurants that match the preferences of the participants. Furthermore, the restaurant selection unit can also select the restaurant with the highest rating based on the participants' past ratings. For example, the restaurant selection unit selects a restaurant with the highest rating based on the participants' past ratings, thereby selecting a restaurant with the highest rating. In this way, the accuracy of the selection is improved by referring to related reviews and ratings.

[0083] The email sending unit can estimate the emotions of the participants and adjust the content and sending timing of the email based on the estimated emotions. For example, if the participant is relaxed, the email sending unit sends detailed email content. For example, if the participant is relaxed, the email sending unit sends detailed email content, thereby deepening the participant's understanding. Furthermore, if the participant is busy, the email sending unit can also send concise email content. For example, if the participant is busy, the email sending unit sends concise email content, thereby reducing the participant's burden. Furthermore, if the participant is feeling stressed, the email sending unit can also send emails with content that will help them relax. For example, if the participant is feeling stressed, the email sending unit sends emails with content that will help them relax, thereby improving the participant's satisfaction. This enables more effective communication by adjusting the content and sending timing of the email based on the participants' emotions.

[0084] When sending an email, the email sending unit can select the optimal sending method by referring to past sending history. The email sending unit selects the optimal sending method based on, for example, the email format that the participant has preferred in the past. For example, the email sending unit improves the accuracy of email sending by selecting the optimal sending method based on the email format that the participant has preferred in the past. The email sending unit can also select the most effective sending method from the participant's past sending history. For example, the email sending unit improves the efficiency of email sending by selecting the most effective sending method from the participant's past sending history. Furthermore, the email sending unit can analyze the participant's past sending history and select the optimal sending method. For example, the email sending unit analyzes the participant's past sending history and selects the optimal sending method, thereby sending highly relevant emails. In this way, the optimal sending method can be selected by referring to the past sending history.

[0085] When sending an email, the email sending unit can customize the expression style of the email according to the communication style of the participant. For example, if a participant prefers a formal communication style, the email sending unit sends the email using a formal expression style. For example, if a participant prefers a formal communication style, the email sending unit sends the email using a formal expression style, thereby improving the satisfaction of the participant. Furthermore, if a participant prefers a casual communication style, the email sending unit can also send the email using a casual expression style. For example, if a participant prefers a casual communication style, the email sending unit sends the email using a casual expression style, thereby improving the satisfaction of the participant. Furthermore, the email sending unit can also send the email using an optimal expression style according to the communication style of the participant. For example, the email sending unit sends a highly relevant email by sending the email using an optimal expression style according to the communication style of the participant. This enables more effective communication by sending the email using an expression style according to the communication style.

[0086] The email sending unit can estimate the emotions of the participants and determine the priority of emails based on the estimated emotions. For example, if a participant is relaxed, the email sending unit prioritizes sending important emails. For example, if a participant is relaxed, the email sending unit prioritizes sending important emails, thereby deepening the participant's understanding. Furthermore, if a participant is busy, the email sending unit can prioritize sending the most important emails. For example, if a participant is busy, the email sending unit prioritizes sending the most important emails, thereby reducing the participant's burden. Furthermore, if a participant is feeling stressed, the email sending unit can prioritize sending emails with relaxing content. For example, if a participant is feeling stressed, the email sending unit prioritizes sending emails with relaxing content, thereby improving the participant's satisfaction. In this way, by determining the priority of emails based on the participants' emotions, more appropriate emails can be sent preferentially.

[0087] When sending an email, the email sending unit can select the optimal sending timing by taking into account the geographical location information of the participant. The email sending unit selects the optimal sending timing by taking into account, for example, the time zone of the participant's current location. For example, the email sending unit improves the efficiency of email sending by selecting the optimal sending timing by taking into account the time zone of the participant's current location. The email sending unit can also select the most effective sending timing based on the geographical location information of the participant. For example, the email sending unit improves the accuracy of email sending by selecting the most effective sending timing based on the geographical location information of the participant. Furthermore, if a participant is traveling, the email sending unit can also select the sending timing by taking into account the time zone of the destination. For example, if a participant is traveling, the email sending unit selects the sending timing by taking into account the time zone of the destination, thereby sending a highly relevant email. In this way, the optimal sending timing can be selected by taking into account the geographical location information.

[0088] The email sending unit can analyze the participants' social media activities to generate optimal email content when sending emails. The email sending unit, for example, generates emails including content of interest from the participants' social media activities. For example, the email sending unit improves the accuracy of email sending by generating emails including content of interest from the participants' social media activities. The email sending unit can also generate optimal email content based on the participants' social media posts. For example, the email sending unit improves the efficiency of email sending by generating optimal email content based on the participants' social media posts. Furthermore, the email sending unit can analyze the participants' social media activities to generate the most effective email content. For example, the email sending unit analyzes the participants' social media activities to generate the most effective email content, thereby sending highly relevant emails. In this way, optimal email content can be generated by analyzing social media activities.

[0089] The participant information acquisition unit can estimate the emotions of the participants and adjust the timing of information acquisition based on the estimated emotions. For example, if the participant is relaxed, the participant information acquisition unit acquires information immediately. For example, if the participant is relaxed, the participant information acquisition unit acquires information immediately, thereby improving the efficiency of information acquisition. Furthermore, if the participant is busy, the participant information acquisition unit can acquire information at a time when the participant is free. For example, if the participant is busy, the participant information acquisition unit can acquire information at a time when the participant is free, thereby improving the accuracy of information acquisition. Furthermore, if the participant is feeling stressed, the participant information acquisition unit can acquire information during a time when the participant is relaxed. For example, if the participant is feeling stressed, the participant information acquisition unit acquires information during a time when the participant is relaxed, thereby acquiring highly relevant information. In this way, by adjusting the timing of information acquisition based on the emotions of the participants, information can be acquired at a more appropriate time.

[0090] The participant information acquisition unit can analyze the participant's past information history and select the optimal acquisition method. For example, the participant information acquisition unit prioritizes the selection of an information acquisition method that the participant has frequently used in the past. For example, the participant information acquisition unit improves the efficiency of information acquisition by prioritizing the selection of an information acquisition method that the participant has frequently used in the past. The participant information acquisition unit can also select the most efficient acquisition method from the participant's past information history. For example, the participant information acquisition unit selects the optimal acquisition method based on the participant's past information history. Furthermore, the participant information acquisition unit can also select the optimal acquisition timing based on the participant's past information history. For example, the participant information acquisition unit analyzes the participant's past information history and selects the optimal acquisition timing. In this way, the optimal acquisition method can be selected by analyzing the past information history.

[0091] The participant information acquisition unit can estimate the emotions of the participants and determine the priority of information to be acquired based on the estimated emotions. For example, when a participant is relaxed, the participant information acquisition unit prioritizes acquisition of important information. For example, when a participant is relaxed, the participant information acquisition unit prioritizes acquisition of important information, thereby improving the efficiency of information acquisition. Furthermore, when a participant is busy, the participant information acquisition unit can also prioritize acquisition of the most important information. For example, when a participant is busy, the participant information acquisition unit prioritizes acquisition of the most important information, thereby improving the accuracy of information acquisition. Furthermore, when a participant is feeling stressed, the participant information acquisition unit can also prioritize acquisition of information that will help participants relax. For example, when a participant is feeling stressed, the participant information acquisition unit prioritizes acquisition of information that will help participants relax, thereby improving participant satisfaction. In this way, by determining the priority of information based on the emotions of the participants, more appropriate information can be acquired preferentially.

[0092] When acquiring information, the participant information acquisition unit can prioritize acquiring highly relevant information by taking into account the geographical location information of the participant. The participant information acquisition unit, for example, prioritizes acquiring information related to the participant's current location. For example, the participant information acquisition unit improves the efficiency of information acquisition by prioritizing acquiring information related to the participant's current location. The participant information acquisition unit can also prioritize acquiring highly relevant information based on the participant's geographical location information. For example, the participant information acquisition unit improves the accuracy of information acquisition by prioritizing acquiring highly relevant information based on the participant's geographical location information. Furthermore, when a participant is traveling, the participant information acquisition unit can also prioritize acquiring information related to the participant's destination. For example, when a participant is traveling, the participant information acquisition unit prioritizes acquiring information related to the participant's destination to acquire highly relevant information. In this way, highly relevant information can be prioritized by taking into account the geographical location information.

[0093] The budget setting unit can estimate the emotions of the participants and adjust the budget setting criteria based on the estimated emotions. For example, if the participants are relaxed, the budget setting unit sets the budget slightly higher. For example, if the participants are relaxed, the budget setting unit sets the budget slightly higher, thereby improving the satisfaction of the participants. Furthermore, the budget setting unit can also set the budget lower if the participants are stressed. For example, if the participants are stressed, the budget setting unit sets the budget lower, thereby reducing the burden on the participants. Furthermore, the budget setting unit can also set the budget flexibly if the participants are excited. For example, if the participants are excited, the budget setting unit sets the budget flexibly, thereby improving the satisfaction of the participants. In this way, a more appropriate budget can be set by adjusting the budget setting criteria based on the emotions of the participants.

[0094] The budget setting unit can select the optimal setting method by referring to past budget history when setting a budget. The budget setting unit, for example, sets the optimal budget based on the past budget history of the participants. For example, the budget setting unit improves the accuracy of budget setting by setting the optimal budget based on the past budget history of the participants. The budget setting unit can also set the optimal budget by referring to budgets set by the participants in the past. For example, the budget setting unit improves the efficiency of budget setting by setting the optimal budget by referring to budgets set by the participants in the past. Furthermore, the budget setting unit can analyze the past budget history of the participants and select the optimal setting method. For example, the budget setting unit analyzes the past budget history of the participants and selects the optimal setting method, thereby setting a highly relevant budget. In this way, the optimal setting method can be selected by referring to the past budget history.

[0095] The budget setting unit can estimate the emotions of the participants and determine budget priorities based on the estimated emotions. For example, if the participants are relaxed, the budget setting unit sets the budget slightly higher. For example, if the participants are relaxed, the budget setting unit sets the budget slightly higher, thereby improving the satisfaction of the participants. Furthermore, the budget setting unit can also set the budget lower if the participants are stressed. For example, if the participants are stressed, the budget setting unit sets the budget lower, thereby reducing the burden on the participants. Furthermore, the budget setting unit can also set the budget flexibly if the participants are excited. For example, if the participants are excited, the budget setting unit sets the budget flexibly, thereby improving the satisfaction of the participants. In this way, by determining budget priorities based on the emotions of the participants, a more appropriate budget can be set.

[0096] When setting a budget, the budget setting unit can set an optimal budget taking into account the geographical location information of the participant. The budget setting unit, for example, sets the budget taking into account the prices in the location where the participant is currently located. For example, the budget setting unit improves the accuracy of budget setting by setting the budget taking into account the prices in the location where the participant is currently located. The budget setting unit can also set an optimal budget based on the geographical location information of the participant. For example, the budget setting unit improves the efficiency of budget setting by setting an optimal budget based on the geographical location information of the participant. Furthermore, when a participant is traveling, the budget setting unit can also set the budget taking into account the prices in the destination. For example, when a participant is traveling, the budget setting unit sets a highly relevant budget by taking into account the prices in the destination. In this way, the optimal budget can be set by taking into account the geographical location information.

[0097] The place setting unit can estimate the emotions of the participants and adjust the criteria for place setting based on the estimated emotions. For example, if the participants are relaxed, the place setting unit sets a quiet place. For example, if the participants are relaxed, the place setting unit sets a quiet place, thereby improving the satisfaction of the participants. Furthermore, if the participants are excited, the place setting unit can set a lively place. For example, if the participants are excited, the place setting unit sets a lively place, thereby improving the satisfaction of the participants. Furthermore, if the participants are feeling stressed, the place setting unit can set a place where they can relax. For example, if the participants are feeling stressed, the place setting unit sets a place where they can relax, thereby improving the satisfaction of the participants. In this way, by adjusting the criteria for place setting based on the emotions of the participants, a more appropriate place can be set.

[0098] When setting a location, the location setting unit can select an optimal setting method by referring to past location history. The location setting unit sets an optimal location based on, for example, the past location history of the participant. For example, the location setting unit improves the accuracy of location setting by setting an optimal location based on the past location history of the participant. The location setting unit can also set an optimal location by referring to places visited by the participant in the past. For example, the location setting unit improves the efficiency of location setting by setting an optimal location by referring to places visited by the participant in the past. Furthermore, the location setting unit can analyze the past location history of the participant and select an optimal setting method. For example, the location setting unit analyzes the past location history of the participant and selects an optimal setting method to set a highly relevant location. In this way, the optimal setting method can be selected by referring to the past location history.

[0099] The location setting unit can estimate the emotions of the participants and determine the priority of locations based on the estimated emotions. For example, when the participants are relaxed, the location setting unit prioritizes quiet locations. For example, when the participants are relaxed, the location setting unit prioritizes quiet locations, thereby improving the satisfaction of the participants. Furthermore, when the participants are excited, the location setting unit can also prioritize lively locations. For example, when the participants are excited, the location setting unit prioritizes lively locations, thereby improving the satisfaction of the participants. Furthermore, when the participants are stressed, the location setting unit can also prioritize locations where the participants can relax. For example, when the participants are stressed, the location setting unit prioritizes locations where the participants can relax, thereby improving the satisfaction of the participants. In this way, by determining the priority of locations based on the emotions of the participants, a more appropriate location can be set.

[0100] When setting a location, the location setting unit can set an optimal location taking into consideration the geographical location information of the participant. For example, the location setting unit prioritizes setting a location near the participant's current location. For example, by prioritizing setting a location near the participant's current location, the location setting unit improves the efficiency of location setting. The location setting unit can also set an optimal location based on the participant's geographical location information. For example, by setting an optimal location based on the participant's geographical location information, the location setting unit improves the accuracy of location setting. Furthermore, when a participant is traveling, the location setting unit can also prioritize setting a location near the participant's destination. For example, when a participant is traveling, the location setting unit prioritizes setting a location near the participant's destination, thereby setting a highly relevant location. In this way, the optimal location can be set by taking geographical location information into consideration. === Hard Collateral 1-1 === Each of the multiple elements, including the schedule acquisition unit, restaurant selection unit, email sending unit, participant information acquisition unit, budget setting unit, and location setting 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 schedule acquisition unit is realized using a synchronization function or manual input function of a calendar app of the smart device 14. The restaurant selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects a restaurant based on the budget, location, and participant preferences. The email sending unit sends emails to participants using the communication I / F 44 of the smart device 14. The participant information acquisition unit acquires participant information using an input device of the smart device 14. The budget setting unit sets the budget using the control unit 46A of the smart device 14. The location setting unit sets the location using the location information function of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the schedule acquisition unit, restaurant selection unit, email sending unit, participant information acquisition unit, budget setting unit, and location setting 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 schedule acquisition unit is realized using a synchronization function or manual input function of a calendar app of the smart glasses 214. The restaurant selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects a restaurant based on the budget, location, and participant preferences. The email sending unit sends emails to participants using the communication I / F 44 of the smart glasses 214. The participant information acquisition unit acquires participant information using an input device of the smart glasses 214. The budget setting unit sets the budget using the control unit 46A of the smart glasses 214. The location setting unit sets the location using the location information function of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the schedule acquisition unit, restaurant selection unit, email sending unit, participant information acquisition unit, budget setting unit, and location setting unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the schedule acquisition unit is realized using a synchronization function or manual input function of a calendar app of the headset terminal 314. The restaurant selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects a restaurant based on the budget, location, and participant preferences. The email sending unit sends emails to participants using the communication I / F 44 of the headset terminal 314. The participant information acquisition unit acquires participant information using an input device of the headset terminal 314. The budget setting unit sets the budget using the control unit 46A of the headset terminal 314. The location setting unit sets the location using the location information function of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the schedule acquisition unit, restaurant selection unit, email sending unit, participant information acquisition unit, budget setting unit, and location setting unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the schedule acquisition unit is realized using a synchronization function or manual input function of a calendar app of the robot 414. The restaurant selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects a restaurant based on the budget, location, and preferences of the participants. The email sending unit sends emails to the participants using the communication I / F 44 of the robot 414. The participant information acquisition unit acquires participant information using an input device of the robot 414. The budget setting unit sets the budget using the control unit 46A of the robot 414. The location setting unit sets the location using the location information function of the robot 414.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The schedule adjustment system can further include a health condition monitoring unit that monitors the health condition of participants. The health condition monitoring unit measures, for example, the heart rate and stress level of the participants and adjusts the schedule based on this. For example, if a participant's heart rate is high, the schedule can be set to a time period when the participant can relax. Also, if the stress level is high, a schedule that is less stressful can be prioritized. Furthermore, the health condition monitoring unit can also suggest appropriate break times depending on the participant's health condition. This makes it possible to adjust the schedule taking into account the participant's health condition.

[0103] The schedule adjustment system can further include a hobby information acquisition unit that takes into account the hobbies and interests of participants. The hobby information acquisition unit acquires, for example, information about activities and events that participants like, and proposes a schedule based on this. For example, if a participant likes music concerts, the schedule can be adjusted to prioritize concert dates. Also, if a participant is interested in sporting events, the schedule can be set taking into account the dates of those events. Furthermore, the hobby information acquisition unit can analyze the participant's past hobby activity history and propose an optimal schedule. This makes it possible to adjust the schedule to reflect the participant's hobbies and interests.

[0104] The schedule adjustment system can further include a transportation consideration unit that considers the transportation means of the participants. The transportation consideration unit adjusts the schedule based on, for example, the transportation means used by the participants (car, train, bicycle, etc.). For example, if the participants will be using a car, the schedule can be set taking into consideration the availability of parking spaces and traffic congestion. Also, if the participants will be using a train, the schedule can be adjusted to match the train schedule. Furthermore, the transportation consideration unit can propose an optimal route to minimize the travel time of the participants. This makes it possible to adjust the schedule taking into consideration the transportation means of the participants.

[0105] The schedule adjustment system can further include a dietary restriction consideration unit that takes into account the dietary restrictions of participants. For example, if a participant has allergies or specific dietary restrictions (vegetarian, gluten-free, etc.), the dietary restriction consideration unit selects a restaurant based on this. For example, if a participant requires gluten-free food, it can prioritize restaurants that offer gluten-free menus. It can also select restaurants that offer safe meals by taking allergy information into consideration. Furthermore, the dietary restriction consideration unit can analyze the participant's past eating history and suggest the most suitable restaurant. This makes it possible to select a restaurant that takes into account the participant's dietary restrictions.

[0106] The schedule adjustment system can further estimate the emotions of participants and adjust the flexibility of the schedule based on the estimated emotions. For example, if a participant is feeling stressed, the system can reduce the burden on the participant by adding some leeway to the schedule. If a participant is feeling relaxed, the system can also tighten the schedule to allow the participant to proceed efficiently. Furthermore, if a participant is excited, the system can suggest a schedule that includes more active activities. This allows the system to provide a more appropriate schedule by adjusting the flexibility of the schedule based on the participant's emotions.

[0107] The schedule adjustment system can also analyze participants' past schedule history and propose optimal schedule patterns. For example, it can analyze the patterns of events and meetings that participants have preferred to attend in the past and propose schedules based on this. It can also set optimal schedules by taking into account time periods and dates that participants have avoided in the past. Furthermore, it can propose schedules that reflect participants' preferences and tendencies based on past schedule history. In this way, it is possible to provide more appropriate schedules by utilizing past schedule history.

[0108] The schedule adjustment system can further estimate the emotions of participants and adjust the communication method based on the estimated emotions. For example, if a participant is feeling stressed, it can send an email with gentle language and relaxing content. If a participant is relaxed, it can send an email with detailed information. Furthermore, if a participant is excited, it can send an email with lively content. This allows for more effective communication by adjusting the communication method based on the emotions of participants.

[0109] The schedule adjustment system can further propose the optimal meeting place by taking into account the geographical location information of the participants. For example, if the participants live in different areas, it can propose an intermediate location that is easy for everyone to meet. It can also select the optimal meeting place to minimize the participants' travel time. Furthermore, it can propose a location with good transportation access based on the participants' geographical location information. This makes it possible to propose the optimal meeting place by taking into account the participants' geographical location information.

[0110] The schedule adjustment system can also estimate participants' emotions and suggest event types based on the estimated emotions. For example, if participants are relaxed, it can suggest relaxing events (such as watching a movie or chatting at a cafe). If participants are excited, it can suggest active events (such as sports or adventures). Furthermore, if participants are stressed, it can suggest relaxation events (such as spa or yoga). This allows the system to provide more satisfying events by suggesting the most appropriate event type based on participants' emotions.

[0111] The schedule adjustment system can further analyze participants' social media activities to suggest related events and meetings. For example, it can suggest schedules based on events and meetings that participants have expressed interest in on social media. It can also suggest related events taking into account participants' social media interests. It can also analyze participants' social media activity history to suggest optimal schedules. This makes it possible to provide more relevant schedules by utilizing social media activities.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The schedule acquisition unit acquires the schedules of the participants. For example, it acquires the schedules of the participants using the synchronization function of a calendar app. It is also possible to acquire external schedule data by manually entering it or by using API integration. Step 2: The restaurant selection unit selects restaurants based on specific conditions, based on the schedule acquired by the schedule acquisition unit. For example, restaurants are selected based on conditions such as budget, location, and participants' preferences, and restaurants that meet the conditions are listed. The optimal restaurant can also be selected based on participants' past ratings. Step 3: The email sending unit sends emails to specific participants based on the information about the restaurant selected by the restaurant selection unit. For example, the email sending unit sends emails containing information about the selected restaurant to participants, automating schedule coordination with participants via email.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 schedule acquisition unit that acquires schedules of participants; a store selection unit that selects a store based on specific conditions on the basis of the schedule acquired by the schedule acquisition unit; an email sending unit that sends emails to specific participants based on the information about the establishment selected by the establishment selection unit; A system characterized by:

2. A participant information acquisition unit for acquiring participant information is provided.

2. The system of claim 1.

3. Have a budget setting department that sets the budget 2. The system of claim 1.

4. A location setting unit is provided to set the location.

2. The system of claim 1.

5. The schedule acquisition unit View the schedule for a specific holiday period and set suggested dates 2. The system of claim 1.

6. The store selection unit Select a store based on the set conditions 2. The system of claim 1.

7. The email sending unit Automate email scheduling with participants 2. The system of claim 1.

8. The schedule acquisition unit Estimate participants' emotions and adjust the timing of schedule retrieval based on the estimated emotions.

2. The system of claim 1.

9. The schedule acquisition unit Analyze participants' past schedule history and select the most appropriate acquisition method 2. The system of claim 1.

10. The schedule acquisition unit Filter schedules based on participants' current projects and interests 2. The system of claim 1.

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