system

Abstract

The system according to this embodiment aims to efficiently link schedule management and coupon provision. [Solution] The system according to the embodiment comprises a schedule collection unit, a sharing unit, a negotiation unit, and a coupon provision unit. The schedule collection unit collects schedule information. The sharing unit shares the schedule information collected by the schedule collection unit. The negotiation unit performs automatic negotiations based on the schedule information shared by the sharing unit. The coupon provision unit provides coupons obtained by the negotiation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the cooperation between schedule management and coupon provision is not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently cooperate schedule management and coupon provision.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a schedule collection unit, a sharing unit, a negotiation unit, and a coupon provision unit. The schedule collection unit collects schedule information. The sharing unit shares the schedule information collected by the schedule collection unit. The negotiation unit performs automatic negotiations based on the schedule information shared by the sharing unit. The coupon provision unit provides coupons obtained by the negotiation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently link schedule management and coupon provision. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The schedule management system according to an embodiment of the present invention is a system that realizes efficient schedule management in conjunction with a calendar service. This system not only provides a simple schedule sharing function with family, colleagues, and friends, but also accepts special coupon offers from restaurants, hair salons, hotels, etc. through automated negotiation using a generation AI. The automated negotiation using the generation AI proposes coupon offers that are effective in attracting customers, taking into account the number of users with free schedules in the surrounding area, based on weather data to predict the number of customers based on the weather. First, the user uses the calendar service to share schedules with family, colleagues, and friends. For example, family schedules can be integrated into one calendar so that everyone can easily check them. This makes scheduling smoother and reduces unnecessary communication. Next, the automated negotiation function using the generation AI is activated. For example, if a user wants to make a reservation at a restaurant on a specific date and time, the generation AI automatically negotiates with the restaurant and offers a special coupon. In this case, the generation AI uses weather data to predict the number of customers based on the weather. For example, since fewer customers are expected on rainy days, restaurants can offer special discount coupons. Furthermore, the generation AI also takes into account the number of users with free schedules in the surrounding area and proposes coupon offers that are effective in attracting customers. For example, if many users have free time in their schedules at a specific date and time, offering special coupons during that time slot can efficiently attract customers. This system allows users to efficiently manage their schedules and receive special coupons. Businesses such as restaurants, hair salons, and hotels can also efficiently attract customers and maximize their revenue. For example, a restaurant can increase its customer base by offering special coupons during off-peak hours. In this way, by integrating with a calendar service and utilizing an automated negotiation function powered by AI, a highly convenient system is provided for both users and businesses. As a result, the schedule management system can streamline users' schedule management and offer special coupons.

[0029] The schedule management system according to this embodiment comprises a schedule collection unit, a sharing unit, a negotiation unit, and a coupon provision unit. The schedule collection unit collects schedule information. Schedule information includes, but is not limited to, the date and time, location, and type of event. The schedule collection unit obtains schedule information from, for example, a calendar service. The schedule collection unit can also collect schedule information manually entered by users. For example, the schedule collection unit collects schedule information entered by users using a smartphone or personal computer. Furthermore, the schedule collection unit can also collect schedule information from other applications and services. For example, the schedule collection unit obtains schedule information from email applications and messaging applications. The sharing unit shares the schedule information collected by the schedule collection unit. For example, the sharing unit shares schedule information within groups such as family, colleagues, and friends. The sharing unit can also share schedule information with specific users. For example, the sharing unit shares schedule information with specific users designated by the user. Furthermore, the sharing unit can synchronize schedule information with a calendar service. For example, the sharing unit synchronizes schedule information in cooperation with a calendar service. The negotiation department conducts automated negotiations based on schedule information shared by the sharing department. The negotiation department conducts automated negotiations using, for example, generative AI. The generative AI conducts automated negotiations using, for example, text generation AI (e.g., LLM). The negotiation department can also conduct automated negotiations utilizing weather data. For example, the negotiation department predicts the number of customers based on weather data and conducts automated negotiations based on that prediction. The coupon provision department provides coupons obtained by the negotiation department. The coupon provision department notifies users of coupons, for example. The coupon provision department can also provide coupons via email or messaging applications. For example, the coupon provision department sends coupons to users' email addresses or messaging application accounts.This enables the schedule management system according to the embodiment to provide efficient schedule management and special coupons.

[0030] The schedule collection unit collects schedule information. This information includes, but is not limited to, date and time, location, and event type. For example, the schedule collection unit obtains schedule information from calendar services. Specifically, it integrates with online calendar services via APIs to automatically retrieve user schedule information. The schedule collection unit can also collect schedule information manually entered by users. For example, it collects schedule information entered by users using smartphones or personal computers. This includes providing a dedicated schedule management application with an interface that allows users to easily enter their schedules. Furthermore, the schedule collection unit can also collect schedule information from other applications and services. For example, it obtains schedule information from email applications and messaging applications. Specifically, it analyzes the content of emails to automatically detect meeting invitations and event notifications and add them to the schedule. In messaging applications, it can extract event information from chat content and reflect it in the schedule. As a result, the schedule collection unit can efficiently collect schedule information from diverse sources and centrally manage users' schedules.

[0031] The sharing unit shares schedule information collected by the schedule collection unit. The sharing unit shares schedule information within groups such as family, colleagues, and friends. Specifically, the sharing unit automatically distributes schedule information to user-defined groups, ensuring all group members can view the latest schedules. The sharing unit can also share schedule information with specific users. For example, the sharing unit shares schedule information with users specified by the user. This includes a function to select the schedule to share and specify the recipient. Furthermore, the sharing unit can synchronize schedule information with calendar services. For example, the sharing unit synchronizes schedule information with a calendar. This allows users to centrally manage their schedules, preventing duplication and omissions, even if they use multiple calendar services. The sharing unit also has a function to update schedule information in real time and immediately notify users of any changes. This enables the sharing unit to facilitate smooth schedule coordination among users and achieve efficient schedule management.

[0032] The negotiation department conducts automated negotiations based on schedule information shared by the sharing department. The negotiation department uses, for example, generative AI to conduct automated negotiations. The generative AI uses, for example, text generation AI (e.g., LLM) to conduct automated negotiations. Specifically, the generative AI analyzes the user's schedule information and generates optimal negotiation content. For example, when adjusting the date and time of a meeting, the generative AI considers the schedules of each participant and proposes the optimal date and time. The negotiation department can also conduct automated negotiations using weather data. For example, the negotiation department predicts the number of customers based on weather data and conducts automated negotiations based on that prediction. Specifically, when adjusting the schedule of an outdoor event, it proposes the optimal date and time considering weather data. Furthermore, the negotiation department can learn the user's past schedule history and behavior patterns to generate more accurate negotiation content. This allows the negotiation department to reduce the burden on the user and achieve efficient schedule adjustments.

[0033] The coupon distribution department provides coupons obtained by the negotiation department. The coupon distribution department, for example, notifies users of coupons. Specifically, it sends notifications to users' smartphones or personal computers to encourage coupon use. The coupon distribution department can also provide coupons via email or messaging applications. For example, it sends coupons to users' email addresses or messaging application accounts. This allows users to easily receive and use coupons. Furthermore, the coupon distribution department can provide coupons at the optimal time based on users' schedule information and behavioral patterns. For example, it can provide coupons related to a specific event before a user participates in that event. This allows the coupon distribution department to improve user convenience and increase coupon utilization rates. The coupon distribution department can also track coupon usage and collect data to develop effective coupon distribution strategies. This enables the coupon distribution department to provide valuable coupons to users and maximize the overall system's effectiveness.

[0034] The weather analysis unit can analyze weather data. For example, the weather analysis unit analyzes weather data such as temperature, precipitation, and wind speed. For example, the weather analysis unit can obtain weather data from a weather database and analyze that data. The weather analysis unit can also collect and analyze weather data in real time. For example, the weather analysis unit can collect weather data in real time from weather sensors and weather observation stations and analyze that data. Furthermore, the weather analysis unit can perform analysis based on past weather data. For example, the weather analysis unit can predict future weather based on past temperature and precipitation data. By analyzing weather data, it becomes possible to provide coupons more effectively. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input weather data obtained from a weather database into a generating AI and have the generating AI perform the analysis of the weather data.

[0035] The weather analysis unit can analyze weather data and predict customer numbers based on weather conditions. For example, the weather analysis unit can analyze weather data such as temperature, precipitation, and wind speed, and predict customer numbers based on that data. For example, the weather analysis unit can predict customer numbers for a specific date and time based on weather data obtained from a weather database. The weather analysis unit can also predict customer numbers based on past weather data. For example, the weather analysis unit can predict future customer numbers based on past temperature and precipitation data. Furthermore, the weather analysis unit can collect weather data in real time and predict customer numbers based on that data. For example, the weather analysis unit can collect weather data in real time from weather sensors and weather observation stations and predict customer numbers based on that data. This improves the accuracy of coupon provision by predicting customer numbers based on weather conditions. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input weather data obtained from a weather database into a generating AI and have the generating AI perform customer number predictions.

[0036] The negotiation department can conduct automated negotiations based on customer count forecasts obtained by the weather analysis department. The negotiation department can conduct automated negotiations using, for example, a generative AI. The generative AI can conduct automated negotiations using, for example, a text generation AI (e.g., LLM). The negotiation department negotiates for the provision of coupons at specific dates and times based on customer count forecasts obtained by the weather analysis department. For example, the negotiation department negotiates with restaurants, hair salons, hotels, etc., on days and times when the weather analysis department predicts low customer counts, and proposes that they provide special coupons. This makes it possible to provide coupons more effectively by conducting automated negotiations based on customer count forecasts. Some or all of the above processing in the negotiation department may be performed using, for example, a generative AI, or without using a generative AI. For example, the negotiation department can input customer count forecasts obtained by the weather analysis department into a generative AI and have the generative AI perform automated negotiations.

[0037] The coupon provider unit can notify users of coupons obtained by the negotiation unit. The coupon provider unit can notify users of coupons, for example, via email or messaging applications. The coupon provider unit can send coupons to users' email addresses or messaging application accounts, for example. The coupon provider unit can also notify users of coupons in conjunction with a calendar service. For example, the coupon provider unit can use the notification function of a calendar service to notify users of coupons. This allows users to receive special coupons by notifying them of the coupons. Some or all of the above processes in the coupon provider unit may be performed using AI, for example, or not using AI. For example, the coupon provider unit can input coupons obtained by the negotiation unit into a generation AI and have the generation AI execute the coupon notification.

[0038] The schedule collection unit can collect the number of users with free schedules in the surrounding area. For example, the schedule collection unit can obtain schedule information of nearby users from a calendar service and collect the number of users with free schedules based on that information. For example, the schedule collection unit can collect schedule information of nearby users at a specific date and time and identify the number of users with free schedules at that time. The schedule collection unit can also collect the number of nearby users based on schedule information manually entered by users. For example, the schedule collection unit can identify the number of nearby users based on schedule information entered by users using smartphones or personal computers. Furthermore, the schedule collection unit can collect schedule information from other applications and services and identify the number of nearby users based on that information. For example, the schedule collection unit can obtain schedule information from email applications or messaging applications and identify the number of nearby users based on that information. By collecting the number of users with free schedules in the surrounding area, it becomes possible to offer coupons that are effective in attracting customers. Some or all of the above processing in the schedule collection unit may be performed using AI, for example, or without using AI. For example, the schedule collection unit can input schedule information obtained from a calendar service into a generation AI, and have the generation AI identify the number of nearby users.

[0039] The schedule collection unit can analyze the user's past schedule history and select the optimal collection method. For example, the schedule collection unit may prioritize schedule collection methods that the user has frequently used in the past. For example, the schedule collection unit may concentrate collection during specific time periods based on the user's past schedule history. For example, the schedule collection unit may analyze the user's past schedule history and propose the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the past schedule history. Some or all of the above processing in the schedule collection unit may be performed using AI, for example, or without AI. For example, the schedule collection unit can input the user's past schedule history into a generating AI and have the generating AI select the optimal collection method.

[0040] The schedule collection unit can filter schedules based on the user's current projects and areas of interest during the collection process. For example, the schedule collection unit can prioritize collecting schedules related to projects the user is currently working on. For example, the schedule collection unit can filter and collect relevant schedules based on the user's areas of interest. For example, the schedule collection unit can collect necessary schedules according to the progress of the user's current projects. This allows for the collection of highly relevant schedules by filtering based on the current projects and areas of interest. Some or all of the above processing in the schedule collection unit may be performed using AI, for example, or without AI. For example, the schedule collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The schedule collection unit can prioritize the collection of highly relevant schedules by considering the user's geographical location information during schedule collection. For example, the schedule collection unit prioritizes the collection of schedules related to the user's current location. For example, the schedule collection unit collects relevant schedules based on the user's travel plans. For example, the schedule collection unit proposes the optimal schedule based on the user's geographical location information. This allows for the priority collection of highly relevant schedules by considering geographical location information. Some or all of the above-described processes in the schedule collection unit may be performed using AI, for example, or without AI. For example, the schedule collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant schedules.

[0042] The schedule collection unit can analyze the user's social media activity and collect relevant schedules during schedule collection. For example, the schedule collection unit can collect events and appointments mentioned by the user on social media. For example, the schedule collection unit can collect events of interest from the user's social media activity. For example, the schedule collection unit can analyze the content of the user's social media posts and suggest relevant schedules. In this way, relevant schedules can be collected by analyzing social media activity. Some or all of the above processing in the schedule collection unit may be performed using AI, for example, or without AI. For example, the schedule collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant schedules.

[0043] The sharing unit can adjust the level of detail of sharing based on the importance of the schedule. For example, the sharing unit will share high-importance schedules in detail. For example, it will share low-importance schedules in a simplified manner. The sharing unit adjusts the level of detail of sharing according to the importance of the schedule. This allows important schedules to be shared in detail by adjusting the level of detail of sharing based on the importance of the schedule. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input schedule importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of sharing.

[0044] The sharing function can apply different sharing algorithms depending on the schedule category when sharing. For example, the sharing function might share business-related schedules in detail, or share private schedules in a simplified manner. The sharing function can apply the most appropriate sharing algorithm depending on the schedule category. This allows for more appropriate sharing by applying the most appropriate sharing algorithm according to the schedule category. Some or all of the above processing in the sharing function may be performed using AI, or not. For example, the sharing function can input schedule category data into a generating AI and have the generating AI perform the application of the sharing algorithm.

[0045] The sharing unit can adjust the order of sharing based on the submission dates of the schedules. For example, the sharing unit can prioritize sharing schedules with earlier submission dates. For example, the sharing unit can postpone sharing schedules with later submission dates. The sharing unit adjusts the order of sharing based on the submission dates. This allows for prioritizing the sharing of schedules with earlier submission dates. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input schedule submission date data into a generating AI and have the generating AI perform the adjustment of the sharing order.

[0046] The sharing unit can adjust the order of sharing based on the relevance of the schedules. For example, the sharing unit prioritizes sharing highly relevant schedules. For example, the sharing unit postpones sharing less relevant schedules. The sharing unit adjusts the order of sharing based on the relevance of the schedules. This allows for prioritizing the sharing of highly relevant schedules by adjusting the order of sharing based on relevance. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input schedule relevance data into a generating AI and have the generating AI perform the adjustment of the sharing order.

[0047] The negotiating unit can adjust the level of detail in negotiations based on the importance of the coupons during negotiations. For example, the negotiating unit will negotiate in detail for high-importance coupons. For example, the negotiating unit will negotiate in a simplified manner for low-importance coupons. The negotiating unit adjusts the level of detail in negotiations according to the importance of the coupons. This allows important coupons to be negotiated in detail by adjusting the level of detail in negotiations based on the importance of the coupons. Some or all of the above processes in the negotiating unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiating unit can input coupon importance data into a generative AI and have the generative AI perform the adjustment of the level of detail in negotiations.

[0048] The negotiation unit can apply different negotiation algorithms depending on the coupon category during negotiations. For example, the negotiation unit will negotiate in detail for restaurant coupons, and simplify negotiations for hair salon coupons. The negotiation unit will apply the most suitable negotiation algorithm depending on the coupon category. This allows for more appropriate negotiations by applying the most suitable negotiation algorithm according to the coupon category. Some or all of the above processing in the negotiation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the negotiation unit can input coupon category data into a generative AI and have the generative AI perform the application of the negotiation algorithm.

[0049] The negotiating department can adjust the order of negotiations based on the submission date of the coupons. For example, the negotiating department may prioritize negotiations for coupons submitted earlier. For example, the negotiating department may postpone negotiations for coupons submitted later. The negotiating department adjusts the order of negotiations based on the submission date. This allows for prioritization of negotiations for coupons submitted earlier by adjusting the order of negotiations based on the submission date. Some or all of the above processes in the negotiating department may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiating department can input coupon submission date data into a generative AI and have the generative AI perform the adjustment of the negotiation order.

[0050] The negotiating department can adjust the order of negotiations based on the relevance of the coupons during negotiations. For example, the negotiating department may prioritize negotiating highly relevant coupons. For example, the negotiating department may postpone negotiating less relevant coupons. The negotiating department adjusts the order of negotiations based on the relevance of the coupons. This allows for prioritizing the negotiation of highly relevant coupons by adjusting the order of negotiations based on relevance. Some or all of the above processes in the negotiating department may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiating department can input coupon relevance data into a generative AI and have the generative AI perform the adjustment of the negotiation order.

[0051] The coupon provisioning unit can adjust the level of detail provided based on the importance of the coupon when providing it. For example, the coupon provisioning unit provides detailed information for high-importance coupons. For example, it provides simplified information for low-importance coupons. The coupon provisioning unit adjusts the level of detail provided according to the importance of the coupon. This allows important coupons to be provided in detail by adjusting the level of detail based on the importance of the coupon. Some or all of the above processing in the coupon provisioning unit may be performed using AI, for example, or without using AI. For example, the coupon provisioning unit can input coupon importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0052] The coupon provisioning unit can apply different provisioning algorithms depending on the coupon category when providing coupons. For example, the coupon provisioning unit may provide detailed coupons for restaurants, or simplified coupons for hair salons. The coupon provisioning unit can apply the most appropriate provisioning algorithm depending on the coupon category. This allows for more appropriate coupon provision by applying the most appropriate provisioning algorithm according to the coupon category. Some or all of the above processing in the coupon provisioning unit may be performed using AI, or not. For example, the coupon provisioning unit can input coupon category data into a generating AI and have the generating AI execute the application of the provisioning algorithm.

[0053] The coupon distribution unit can adjust the order of coupon distribution based on the submission date. For example, the coupon distribution unit may prioritize coupons submitted earlier. For example, the coupon distribution unit may postpone coupons submitted later. The coupon distribution unit adjusts the order of distribution based on the submission date. This allows for priority distribution of coupons submitted earlier by adjusting the order of distribution based on the submission date. Some or all of the above processing in the coupon distribution unit may be performed using AI, for example, or without AI. For example, the coupon distribution unit can input coupon submission date data into a generating AI and have the generating AI perform the adjustment of the distribution order.

[0054] The coupon distribution unit can adjust the order in which coupons are distributed based on their relevance. For example, the coupon distribution unit may prioritize the distribution of highly relevant coupons. For example, the coupon distribution unit may postpone the distribution of less relevant coupons. The coupon distribution unit adjusts the order in which coupons are distributed based on their relevance. This allows for the priority of providing highly relevant coupons by adjusting the order of distribution based on relevance. Some or all of the above processing in the coupon distribution unit may be performed using AI, for example, or without AI. For example, the coupon distribution unit can input coupon relevance data into a generating AI and have the generating AI perform the adjustment of the distribution order.

[0055] The weather analysis unit can optimize its analysis algorithm by referring to past weather data during weather analysis. For example, the weather analysis unit selects the optimal analysis algorithm based on past weather data. For example, the weather analysis unit improves the accuracy of the analysis by referring to past weather data. For example, the weather analysis unit analyzes past weather data and applies the most efficient analysis algorithm. In this way, the analysis algorithm can be optimized by referring to past weather data. Some or all of the above processes in the weather analysis unit may be performed using AI, for example, or without using AI. For example, the weather analysis unit can input past weather data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0056] The weather analysis unit can apply different analysis methods depending on the weather category during weather analysis. For example, the weather analysis unit applies a detailed analysis method during rainy weather. For example, the weather analysis unit applies a simplified analysis method during sunny weather. For example, the weather analysis unit applies the optimal analysis method depending on the weather category. This makes it possible to perform more appropriate weather analysis by applying the optimal analysis method according to the weather category. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input weather category data into a generating AI and have the generating AI execute the application of the analysis method.

[0057] The weather analysis unit can adjust the order of analysis based on the submission date of the weather data during weather analysis. For example, the weather analysis unit may prioritize the analysis of weather data submitted earlier. For example, the weather analysis unit may postpone the analysis of weather data submitted later. The weather analysis unit adjusts the order of analysis based on the submission date. This allows for the prioritization of analysis of weather data submitted earlier by adjusting the order of analysis based on the submission date. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input weather data submission date data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0058] The weather analysis unit can adjust the order of analysis based on the relevance of the weather data during weather analysis. For example, the weather analysis unit prioritizes the analysis of highly relevant weather data. For example, the weather analysis unit postpones the analysis of less relevant weather data. The weather analysis unit adjusts the order of analysis based on the relevance of the weather data. This allows for the prioritization of highly relevant weather data by adjusting the order of analysis based on relevance. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input relevance data of the weather data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The schedule management system can also collect user health data and use it to optimize schedules. For example, it can collect user sleep data and adjust schedules to avoid important meetings or tasks when the user is sleep-deprived. It can also incorporate exercise into the schedule when the user is inactive, based on their exercise data. Furthermore, it can collect user meal data and adjust schedules to match meal times. This enables schedule management tailored to the user's health condition.

[0061] The schedule management system can further suggest schedules based on the user's hobbies and interests. For example, if a user is interested in music, it can suggest nearby concerts and live events in their schedule. If a user is interested in sports, it can suggest nearby sporting events and matches. If a user is interested in art, it can suggest exhibition information at museums and galleries in their schedule. This makes it possible to suggest schedules that are tailored to the user's hobbies and interests.

[0062] The schedule management system can further utilize the user's geographical location to suggest the optimal schedule. For example, it can suggest nearby cafes and restaurants based on the user's current location. It can also suggest a schedule that takes travel time into account based on the user's planned destinations. If the user is traveling, it can suggest tourist attractions and event information in their destination. This enables the suggestion of the optimal schedule based on the user's geographical location.

[0063] The schedule management system can further analyze the user's past schedule history and provide optimal schedule suggestions. For example, it can prioritize suggesting schedule patterns that the user has frequently used in the past. It can concentrate schedules into specific time slots based on the user's past schedule history. By analyzing the user's past schedule history, it can provide the most efficient schedule suggestions. In this way, by analyzing past schedule history, it becomes possible to provide optimal schedule suggestions.

[0064] The schedule management system can further analyze users' social media activity and suggest relevant schedules. For example, it can suggest events and appointments mentioned by users on social media. It can suggest events of interest based on users' social media activity. It can analyze users' social media posts and suggest relevant schedules. In this way, it becomes possible to suggest relevant schedules by analyzing social media activity.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The schedule collection unit collects schedule information. This information includes the date and time, location, and type of event. In addition to obtaining schedule information from calendar services, the schedule collection unit also collects schedule information manually entered by users and schedule information from other applications and services. For example, it obtains schedule information from email applications and messaging applications. Step 2: The sharing unit shares the schedule information collected by the schedule collection unit. The sharing unit can share schedule information within groups such as family, colleagues, and friends, as well as with specific users. The sharing unit also synchronizes schedule information with calendar services. Step 3: The negotiation department conducts automated negotiations based on the schedule information shared by the sharing department. The negotiation department can use generative AI (e.g., text generation AI) to conduct automated negotiations, or it can predict the number of customers based on weather data and conduct automated negotiations based on that prediction. Step 4: The coupon provider provides the coupons obtained by the negotiation team. The coupon provider notifies users of the coupons and can also provide them via email or messaging applications. For example, they can send the coupons to the user's email address or messaging application account.

[0067] (Example of form 2) The schedule management system according to an embodiment of the present invention is a system that realizes efficient schedule management in conjunction with a calendar service. This system not only provides a simple schedule sharing function with family, colleagues, and friends, but also accepts special coupon offers from restaurants, hair salons, hotels, etc. through automated negotiation using a generation AI. The automated negotiation using the generation AI proposes coupon offers that are effective in attracting customers, taking into account the number of users with free schedules in the surrounding area, based on weather data to predict the number of customers based on the weather. First, the user uses the calendar service to share schedules with family, colleagues, and friends. For example, family schedules can be integrated into one calendar so that everyone can easily check them. This makes scheduling smoother and reduces unnecessary communication. Next, the automated negotiation function using the generation AI is activated. For example, if a user wants to make a reservation at a restaurant on a specific date and time, the generation AI automatically negotiates with the restaurant and offers a special coupon. In this case, the generation AI uses weather data to predict the number of customers based on the weather. For example, since fewer customers are expected on rainy days, restaurants can offer special discount coupons. Furthermore, the generation AI also takes into account the number of users with free schedules in the surrounding area and proposes coupon offers that are effective in attracting customers. For example, if many users have free time in their schedules at a specific date and time, offering special coupons during that time slot can efficiently attract customers. This system allows users to efficiently manage their schedules and receive special coupons. Businesses such as restaurants, hair salons, and hotels can also efficiently attract customers and maximize their revenue. For example, a restaurant can increase its customer base by offering special coupons during off-peak hours. In this way, by integrating with a calendar service and utilizing an automated negotiation function powered by AI, a highly convenient system is provided for both users and businesses. As a result, the schedule management system can streamline users' schedule management and offer special coupons.

[0068] The schedule management system according to this embodiment comprises a schedule collection unit, a sharing unit, a negotiation unit, and a coupon provision unit. The schedule collection unit collects schedule information. Schedule information includes, but is not limited to, the date and time, location, and type of event. The schedule collection unit obtains schedule information from, for example, a calendar service. The schedule collection unit can also collect schedule information manually entered by users. For example, the schedule collection unit collects schedule information entered by users using a smartphone or personal computer. Furthermore, the schedule collection unit can also collect schedule information from other applications and services. For example, the schedule collection unit obtains schedule information from email applications and messaging applications. The sharing unit shares the schedule information collected by the schedule collection unit. For example, the sharing unit shares schedule information within groups such as family, colleagues, and friends. The sharing unit can also share schedule information with specific users. For example, the sharing unit shares schedule information with specific users designated by the user. Furthermore, the sharing unit can synchronize schedule information with a calendar service. For example, the sharing unit synchronizes schedule information in cooperation with a calendar service. The negotiation department conducts automated negotiations based on schedule information shared by the sharing department. The negotiation department conducts automated negotiations using, for example, generative AI. The generative AI conducts automated negotiations using, for example, text generation AI (e.g., LLM). The negotiation department can also conduct automated negotiations utilizing weather data. For example, the negotiation department predicts the number of customers based on weather data and conducts automated negotiations based on that prediction. The coupon provision department provides coupons obtained by the negotiation department. The coupon provision department notifies users of coupons, for example. The coupon provision department can also provide coupons via email or messaging applications. For example, the coupon provision department sends coupons to users' email addresses or messaging application accounts.This enables the schedule management system according to the embodiment to provide efficient schedule management and special coupons.

[0069] The schedule collection unit collects schedule information. This information includes, but is not limited to, date and time, location, and event type. For example, the schedule collection unit obtains schedule information from calendar services. Specifically, it integrates with online calendar services via APIs to automatically retrieve user schedule information. The schedule collection unit can also collect schedule information manually entered by users. For example, it collects schedule information entered by users using smartphones or personal computers. This includes providing a dedicated schedule management application with an interface that allows users to easily enter their schedules. Furthermore, the schedule collection unit can also collect schedule information from other applications and services. For example, it obtains schedule information from email applications and messaging applications. Specifically, it analyzes the content of emails to automatically detect meeting invitations and event notifications and add them to the schedule. In messaging applications, it can extract event information from chat content and reflect it in the schedule. As a result, the schedule collection unit can efficiently collect schedule information from diverse sources and centrally manage users' schedules.

[0070] The sharing unit shares schedule information collected by the schedule collection unit. The sharing unit shares schedule information within groups such as family, colleagues, and friends. Specifically, the sharing unit automatically distributes schedule information to user-defined groups, ensuring all group members can view the latest schedules. The sharing unit can also share schedule information with specific users. For example, the sharing unit shares schedule information with users specified by the user. This includes a function to select the schedule to share and specify the recipient. Furthermore, the sharing unit can synchronize schedule information with calendar services. For example, the sharing unit synchronizes schedule information with a calendar. This allows users to centrally manage their schedules, preventing duplication and omissions, even if they use multiple calendar services. The sharing unit also has a function to update schedule information in real time and immediately notify users of any changes. This enables the sharing unit to facilitate smooth schedule coordination among users and achieve efficient schedule management.

[0071] The negotiation department conducts automated negotiations based on schedule information shared by the sharing department. The negotiation department uses, for example, generative AI to conduct automated negotiations. The generative AI uses, for example, text generation AI (e.g., LLM) to conduct automated negotiations. Specifically, the generative AI analyzes the user's schedule information and generates optimal negotiation content. For example, when adjusting the date and time of a meeting, the generative AI considers the schedules of each participant and proposes the optimal date and time. The negotiation department can also conduct automated negotiations using weather data. For example, the negotiation department predicts the number of customers based on weather data and conducts automated negotiations based on that prediction. Specifically, when adjusting the schedule of an outdoor event, it proposes the optimal date and time considering weather data. Furthermore, the negotiation department can learn the user's past schedule history and behavior patterns to generate more accurate negotiation content. This allows the negotiation department to reduce the burden on the user and achieve efficient schedule adjustments.

[0072] The coupon distribution department provides coupons obtained by the negotiation department. The coupon distribution department, for example, notifies users of coupons. Specifically, it sends notifications to users' smartphones or personal computers to encourage coupon use. The coupon distribution department can also provide coupons via email or messaging applications. For example, it sends coupons to users' email addresses or messaging application accounts. This allows users to easily receive and use coupons. Furthermore, the coupon distribution department can provide coupons at the optimal time based on users' schedule information and behavioral patterns. For example, it can provide coupons related to a specific event before a user participates in that event. This allows the coupon distribution department to improve user convenience and increase coupon utilization rates. The coupon distribution department can also track coupon usage and collect data to develop effective coupon distribution strategies. This enables the coupon distribution department to provide valuable coupons to users and maximize the overall system's effectiveness.

[0073] The weather analysis unit can analyze weather data. For example, the weather analysis unit analyzes weather data such as temperature, precipitation, and wind speed. For example, the weather analysis unit can obtain weather data from a weather database and analyze that data. The weather analysis unit can also collect and analyze weather data in real time. For example, the weather analysis unit can collect weather data in real time from weather sensors and weather observation stations and analyze that data. Furthermore, the weather analysis unit can perform analysis based on past weather data. For example, the weather analysis unit can predict future weather based on past temperature and precipitation data. By analyzing weather data, it becomes possible to provide coupons more effectively. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input weather data obtained from a weather database into a generating AI and have the generating AI perform the analysis of the weather data.

[0074] The weather analysis unit can analyze weather data and predict customer numbers based on weather conditions. For example, the weather analysis unit can analyze weather data such as temperature, precipitation, and wind speed, and predict customer numbers based on that data. For example, the weather analysis unit can predict customer numbers for a specific date and time based on weather data obtained from a weather database. The weather analysis unit can also predict customer numbers based on past weather data. For example, the weather analysis unit can predict future customer numbers based on past temperature and precipitation data. Furthermore, the weather analysis unit can collect weather data in real time and predict customer numbers based on that data. For example, the weather analysis unit can collect weather data in real time from weather sensors and weather observation stations and predict customer numbers based on that data. This improves the accuracy of coupon provision by predicting customer numbers based on weather conditions. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input weather data obtained from a weather database into a generating AI and have the generating AI perform customer number predictions.

[0075] The negotiation department can conduct automated negotiations based on customer count forecasts obtained by the weather analysis department. The negotiation department can conduct automated negotiations using, for example, a generative AI. The generative AI can conduct automated negotiations using, for example, a text generation AI (e.g., LLM). The negotiation department negotiates for the provision of coupons at specific dates and times based on customer count forecasts obtained by the weather analysis department. For example, the negotiation department negotiates with restaurants, hair salons, hotels, etc., on days and times when the weather analysis department predicts low customer counts, and proposes that they provide special coupons. This makes it possible to provide coupons more effectively by conducting automated negotiations based on customer count forecasts. Some or all of the above processing in the negotiation department may be performed using, for example, a generative AI, or without using a generative AI. For example, the negotiation department can input customer count forecasts obtained by the weather analysis department into a generative AI and have the generative AI perform automated negotiations.

[0076] The coupon provider unit can notify users of coupons obtained by the negotiation unit. The coupon provider unit can notify users of coupons, for example, via email or messaging applications. The coupon provider unit can send coupons to users' email addresses or messaging application accounts, for example. The coupon provider unit can also notify users of coupons in conjunction with a calendar service. For example, the coupon provider unit can use the notification function of a calendar service to notify users of coupons. This allows users to receive special coupons by notifying them of the coupons. Some or all of the above processes in the coupon provider unit may be performed using AI, for example, or not using AI. For example, the coupon provider unit can input coupons obtained by the negotiation unit into a generation AI and have the generation AI execute the coupon notification.

[0077] The schedule collection unit can collect the number of users with free schedules in the surrounding area. For example, the schedule collection unit can obtain schedule information of nearby users from a calendar service and collect the number of users with free schedules based on that information. For example, the schedule collection unit can collect schedule information of nearby users at a specific date and time and identify the number of users with free schedules at that time. The schedule collection unit can also collect the number of nearby users based on schedule information manually entered by users. For example, the schedule collection unit can identify the number of nearby users based on schedule information entered by users using smartphones or personal computers. Furthermore, the schedule collection unit can collect schedule information from other applications and services and identify the number of nearby users based on that information. For example, the schedule collection unit can obtain schedule information from email applications or messaging applications and identify the number of nearby users based on that information. By collecting the number of users with free schedules in the surrounding area, it becomes possible to offer coupons that are effective in attracting customers. Some or all of the above processing in the schedule collection unit may be performed using AI, for example, or without using AI. For example, the schedule collection unit can input schedule information obtained from a calendar service into a generation AI, and have the generation AI identify the number of nearby users.

[0078] The schedule collection unit can estimate the user's emotions and adjust the timing of schedule collection based on the estimated emotions. For example, if the user is stressed, the schedule collection unit can reduce the frequency of schedule collection and reduce notifications. For example, if the user is relaxed, the schedule collection unit can collect detailed schedules and provide proactive notifications. For example, if the user is busy, the schedule collection unit can prioritize collecting only important schedules and minimize notifications. This allows for more appropriate schedule management by adjusting the timing of schedule collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the schedule collection unit may be performed using AI or not. For example, the schedule collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The schedule collection unit can analyze the user's past schedule history and select the optimal collection method. For example, the schedule collection unit may prioritize schedule collection methods that the user has frequently used in the past. For example, the schedule collection unit may concentrate collection during specific time periods based on the user's past schedule history. For example, the schedule collection unit may analyze the user's past schedule history and propose the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the past schedule history. Some or all of the above processing in the schedule collection unit may be performed using AI, for example, or without AI. For example, the schedule collection unit can input the user's past schedule history into a generating AI and have the generating AI select the optimal collection method.

[0080] The schedule collection unit can filter schedules based on the user's current projects and areas of interest during the collection process. For example, the schedule collection unit can prioritize collecting schedules related to projects the user is currently working on. For example, the schedule collection unit can filter and collect relevant schedules based on the user's areas of interest. For example, the schedule collection unit can collect necessary schedules according to the progress of the user's current projects. This allows for the collection of highly relevant schedules by filtering based on the current projects and areas of interest. Some or all of the above processing in the schedule collection unit may be performed using AI, for example, or without AI. For example, the schedule collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0081] The schedule collection unit can estimate the user's emotions and determine the priority of schedules to collect based on the estimated emotions. For example, if the user is stressed, the schedule collection unit will prioritize collecting high-priority schedules. For example, if the user is relaxed, the schedule collection unit will collect detailed schedules and adjust their priorities. For example, if the user is busy, the schedule collection unit will prioritize collecting urgent schedules. This allows for the priority collection of important schedules by determining schedule priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the schedule collection unit may be performed using AI or not. For example, the schedule collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The schedule collection unit can prioritize the collection of highly relevant schedules by considering the user's geographical location information during schedule collection. For example, the schedule collection unit prioritizes the collection of schedules related to the user's current location. For example, the schedule collection unit collects relevant schedules based on the user's travel plans. For example, the schedule collection unit proposes the optimal schedule based on the user's geographical location information. This allows for the priority collection of highly relevant schedules by considering geographical location information. Some or all of the above-described processes in the schedule collection unit may be performed using AI, for example, or without AI. For example, the schedule collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant schedules.

[0083] The schedule collection unit can analyze the user's social media activity and collect relevant schedules during schedule collection. For example, the schedule collection unit can collect events and appointments mentioned by the user on social media. For example, the schedule collection unit can collect events of interest from the user's social media activity. For example, the schedule collection unit can analyze the content of the user's social media posts and suggest relevant schedules. In this way, relevant schedules can be collected by analyzing social media activity. Some or all of the above processing in the schedule collection unit may be performed using AI, for example, or without AI. For example, the schedule collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant schedules.

[0084] The sharing section can estimate the user's emotions and adjust the sharing method based on the estimated emotions. For example, if the user is stressed, the sharing section provides a simple sharing method. If the user is relaxed, the sharing section provides detailed sharing options. If the user is busy, the sharing section shares only important schedules. This allows for more appropriate sharing by adjusting the sharing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing section may be performed using AI or not using AI. For example, the sharing section can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The sharing unit can adjust the level of detail of sharing based on the importance of the schedule. For example, the sharing unit will share high-importance schedules in detail. For example, it will share low-importance schedules in a simplified manner. The sharing unit adjusts the level of detail of sharing according to the importance of the schedule. This allows important schedules to be shared in detail by adjusting the level of detail of sharing based on the importance of the schedule. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input schedule importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of sharing.

[0086] The sharing function can apply different sharing algorithms depending on the schedule category when sharing. For example, the sharing function might share business-related schedules in detail, or share private schedules in a simplified manner. The sharing function can apply the most appropriate sharing algorithm depending on the schedule category. This allows for more appropriate sharing by applying the most appropriate sharing algorithm according to the schedule category. Some or all of the above processing in the sharing function may be performed using AI, or not. For example, the sharing function can input schedule category data into a generating AI and have the generating AI perform the application of the sharing algorithm.

[0087] The sharing unit can estimate the user's emotions and determine sharing priorities based on the estimated emotions. For example, if the user is stressed, the sharing unit will prioritize sharing important schedules. If the user is relaxed, the sharing unit will prioritize sharing detailed schedules. If the user is busy, the sharing unit will prioritize sharing urgent schedules. This allows for the priority sharing of important schedules based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The sharing unit can adjust the order of sharing based on the submission dates of the schedules. For example, the sharing unit can prioritize sharing schedules with earlier submission dates. For example, the sharing unit can postpone sharing schedules with later submission dates. The sharing unit adjusts the order of sharing based on the submission dates. This allows for prioritizing the sharing of schedules with earlier submission dates. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input schedule submission date data into a generating AI and have the generating AI perform the adjustment of the sharing order.

[0089] The sharing unit can adjust the order of sharing based on the relevance of the schedules. For example, the sharing unit prioritizes sharing highly relevant schedules. For example, the sharing unit postpones sharing less relevant schedules. The sharing unit adjusts the order of sharing based on the relevance of the schedules. This allows for prioritizing the sharing of highly relevant schedules by adjusting the order of sharing based on relevance. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input schedule relevance data into a generating AI and have the generating AI perform the adjustment of the sharing order.

[0090] The negotiation unit can estimate the user's emotions and adjust the negotiation method based on the estimated emotions. For example, if the user is stressed, the negotiation unit will offer a simple negotiation method. If the user is relaxed, the negotiation unit will offer detailed negotiation options. If the user is busy, the negotiation unit will prioritize only important negotiations. This allows for more appropriate negotiations by adjusting the negotiation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using or without generative AI. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The negotiating unit can adjust the level of detail in negotiations based on the importance of the coupons during negotiations. For example, the negotiating unit will negotiate in detail for high-importance coupons. For example, the negotiating unit will negotiate in a simplified manner for low-importance coupons. The negotiating unit adjusts the level of detail in negotiations according to the importance of the coupons. This allows important coupons to be negotiated in detail by adjusting the level of detail in negotiations based on the importance of the coupons. Some or all of the above processes in the negotiating unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiating unit can input coupon importance data into a generative AI and have the generative AI perform the adjustment of the level of detail in negotiations.

[0092] The negotiation unit can apply different negotiation algorithms depending on the coupon category during negotiations. For example, the negotiation unit will negotiate in detail for restaurant coupons, and simplify negotiations for hair salon coupons. The negotiation unit will apply the most suitable negotiation algorithm depending on the coupon category. This allows for more appropriate negotiations by applying the most suitable negotiation algorithm according to the coupon category. Some or all of the above processing in the negotiation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the negotiation unit can input coupon category data into a generative AI and have the generative AI perform the application of the negotiation algorithm.

[0093] The negotiation unit can estimate the user's emotions and determine negotiation priorities based on the estimated emotions. For example, if the user is stressed, the negotiation unit will prioritize important negotiations. For example, if the user is relaxed, the negotiation unit will conduct detailed negotiations. For example, if the user is busy, the negotiation unit will prioritize urgent negotiations. This allows important negotiations to be prioritized by determining negotiation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using a generative AI, or not using a generative AI. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The negotiating department can adjust the order of negotiations based on the submission date of the coupons. For example, the negotiating department may prioritize negotiations for coupons submitted earlier. For example, the negotiating department may postpone negotiations for coupons submitted later. The negotiating department adjusts the order of negotiations based on the submission date. This allows for prioritization of negotiations for coupons submitted earlier by adjusting the order of negotiations based on the submission date. Some or all of the above processes in the negotiating department may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiating department can input coupon submission date data into a generative AI and have the generative AI perform the adjustment of the negotiation order.

[0095] The negotiating department can adjust the order of negotiations based on the relevance of the coupons during negotiations. For example, the negotiating department may prioritize negotiating highly relevant coupons. For example, the negotiating department may postpone negotiating less relevant coupons. The negotiating department adjusts the order of negotiations based on the relevance of the coupons. This allows for prioritizing the negotiation of highly relevant coupons by adjusting the order of negotiations based on relevance. Some or all of the above processes in the negotiating department may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiating department can input coupon relevance data into a generative AI and have the generative AI perform the adjustment of the negotiation order.

[0096] The coupon distribution unit can estimate the user's emotions and adjust the method of coupon distribution based on the estimated emotions. For example, if the user is stressed, the coupon distribution unit may provide a simple coupon distribution method. For example, if the user is relaxed, the coupon distribution unit may provide detailed coupon distribution options. For example, if the user is busy, the coupon distribution unit may prioritize providing only important coupons. This allows for more appropriate coupon distribution by adjusting the method of coupon distribution according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coupon distribution unit may be performed using AI or not using AI. For example, the coupon distribution unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The coupon provisioning unit can adjust the level of detail provided based on the importance of the coupon when providing it. For example, the coupon provisioning unit provides detailed information for high-importance coupons. For example, it provides simplified information for low-importance coupons. The coupon provisioning unit adjusts the level of detail provided according to the importance of the coupon. This allows important coupons to be provided in detail by adjusting the level of detail based on the importance of the coupon. Some or all of the above processing in the coupon provisioning unit may be performed using AI, for example, or without using AI. For example, the coupon provisioning unit can input coupon importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0098] The coupon provisioning unit can apply different provisioning algorithms depending on the coupon category when providing coupons. For example, the coupon provisioning unit may provide detailed coupons for restaurants, or simplified coupons for hair salons. The coupon provisioning unit can apply the most appropriate provisioning algorithm depending on the coupon category. This allows for more appropriate coupon provision by applying the most appropriate provisioning algorithm according to the coupon category. Some or all of the above processing in the coupon provisioning unit may be performed using AI, or not. For example, the coupon provisioning unit can input coupon category data into a generating AI and have the generating AI execute the application of the provisioning algorithm.

[0099] The coupon distribution unit can estimate the user's emotions and determine the priority of coupon distribution based on the estimated emotions. For example, if the user is stressed, the coupon distribution unit will prioritize providing important coupons. For example, if the user is relaxed, the coupon distribution unit will provide detailed coupons. For example, if the user is busy, the coupon distribution unit will prioritize providing urgent coupons. In this way, by determining the priority of coupon distribution according to the user's emotions, important coupons can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coupon distribution unit may be performed using AI or not using AI. For example, the coupon distribution unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The coupon distribution unit can adjust the order of coupon distribution based on the submission date. For example, the coupon distribution unit may prioritize coupons submitted earlier. For example, the coupon distribution unit may postpone coupons submitted later. The coupon distribution unit adjusts the order of distribution based on the submission date. This allows for priority distribution of coupons submitted earlier by adjusting the order of distribution based on the submission date. Some or all of the above processing in the coupon distribution unit may be performed using AI, for example, or without AI. For example, the coupon distribution unit can input coupon submission date data into a generating AI and have the generating AI perform the adjustment of the distribution order.

[0101] The coupon distribution unit can adjust the order in which coupons are distributed based on their relevance. For example, the coupon distribution unit may prioritize the distribution of highly relevant coupons. For example, the coupon distribution unit may postpone the distribution of less relevant coupons. The coupon distribution unit adjusts the order in which coupons are distributed based on their relevance. This allows for the priority of providing highly relevant coupons by adjusting the order of distribution based on relevance. Some or all of the above processing in the coupon distribution unit may be performed using AI, for example, or without AI. For example, the coupon distribution unit can input coupon relevance data into a generating AI and have the generating AI perform the adjustment of the distribution order.

[0102] The weather analysis unit can estimate the user's emotions and adjust the weather analysis method based on the estimated emotions. For example, if the user is stressed, the weather analysis unit provides a simple weather analysis method. For example, if the user is relaxed, the weather analysis unit provides detailed weather analysis options. For example, if the user is busy, the weather analysis unit prioritizes analyzing only important weather information. This allows for more appropriate weather analysis by adjusting the weather analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the weather analysis unit may be performed using AI or not using AI. For example, the weather analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The weather analysis unit can optimize its analysis algorithm by referring to past weather data during weather analysis. For example, the weather analysis unit selects the optimal analysis algorithm based on past weather data. For example, the weather analysis unit improves the accuracy of the analysis by referring to past weather data. For example, the weather analysis unit analyzes past weather data and applies the most efficient analysis algorithm. In this way, the analysis algorithm can be optimized by referring to past weather data. Some or all of the above processes in the weather analysis unit may be performed using AI, for example, or without using AI. For example, the weather analysis unit can input past weather data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0104] The weather analysis unit can apply different analysis methods depending on the weather category during weather analysis. For example, the weather analysis unit applies a detailed analysis method during rainy weather. For example, the weather analysis unit applies a simplified analysis method during sunny weather. For example, the weather analysis unit applies the optimal analysis method depending on the weather category. This makes it possible to perform more appropriate weather analysis by applying the optimal analysis method according to the weather category. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input weather category data into a generating AI and have the generating AI execute the application of the analysis method.

[0105] The weather analysis unit can estimate the user's emotions and determine the priority of weather analysis based on the estimated user emotions. For example, if the user is stressed, the weather analysis unit will prioritize analyzing important weather information. For example, if the user is relaxed, the weather analysis unit will prioritize analyzing detailed weather information. For example, if the user is busy, the weather analysis unit will prioritize analyzing urgent weather information. In this way, by determining the priority of weather analysis according to the user's emotions, important weather information can be analyzed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The weather analysis unit can adjust the order of analysis based on the submission date of the weather data during weather analysis. For example, the weather analysis unit may prioritize the analysis of weather data submitted earlier. For example, the weather analysis unit may postpone the analysis of weather data submitted later. The weather analysis unit adjusts the order of analysis based on the submission date. This allows for the prioritization of analysis of weather data submitted earlier by adjusting the order of analysis based on the submission date. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input weather data submission date data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0107] The weather analysis unit can adjust the order of analysis based on the relevance of the weather data during weather analysis. For example, the weather analysis unit prioritizes the analysis of highly relevant weather data. For example, the weather analysis unit postpones the analysis of less relevant weather data. The weather analysis unit adjusts the order of analysis based on the relevance of the weather data. This allows for the prioritization of highly relevant weather data by adjusting the order of analysis based on relevance. Some or all of the above processing in the weather analysis unit may be performed using AI, for example, or without AI. For example, the weather analysis unit can input relevance data of the weather data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The schedule management system can also collect user health data and use it to optimize schedules. For example, it can collect user sleep data and adjust schedules to avoid important meetings or tasks when the user is sleep-deprived. It can also incorporate exercise into the schedule when the user is inactive, based on their exercise data. Furthermore, it can collect user meal data and adjust schedules to match meal times. This enables schedule management tailored to the user's health condition.

[0110] The schedule management system can also estimate the user's emotions and adjust schedule reminders based on those emotions. For example, if the user is stressed, reminder notifications will be less frequent. If the user is relaxed, detailed reminders will be provided. If the user is busy, only important reminders will be prioritized. This allows for reminder adjustments based on the user's emotions, resulting in more effective schedule management.

[0111] The schedule management system can further suggest schedules based on the user's hobbies and interests. For example, if a user is interested in music, it can suggest nearby concerts and live events in their schedule. If a user is interested in sports, it can suggest nearby sporting events and matches. If a user is interested in art, it can suggest exhibition information at museums and galleries in their schedule. This makes it possible to suggest schedules that are tailored to the user's hobbies and interests.

[0112] The schedule management system can further estimate the user's emotions and adjust schedule priorities based on those emotions. For example, if the user is stressed, high-priority tasks are prioritized in the schedule. If the user is relaxed, detailed tasks are scheduled. If the user is busy, urgent tasks are scheduled first. This allows for schedule prioritization according to the user's emotions, resulting in more efficient schedule management.

[0113] The schedule management system can further utilize the user's geographical location to suggest the optimal schedule. For example, it can suggest nearby cafes and restaurants based on the user's current location. It can also suggest a schedule that takes travel time into account based on the user's planned destinations. If the user is traveling, it can suggest tourist attractions and event information in their destination. This enables the suggestion of the optimal schedule based on the user's geographical location.

[0114] The schedule management system can further estimate the user's emotions and adjust the way schedule notifications are delivered based on those emotions. For example, if the user is stressed, notifications will be less frequent. If the user is relaxed, detailed notifications will be provided. If the user is busy, only important notifications will be prioritized. This allows for adjustments to notification methods according to the user's emotions, resulting in more effective schedule management.

[0115] The schedule management system can further analyze the user's past schedule history and provide optimal schedule suggestions. For example, it can prioritize suggesting schedule patterns that the user has frequently used in the past. It can concentrate schedules into specific time slots based on the user's past schedule history. By analyzing the user's past schedule history, it can provide the most efficient schedule suggestions. In this way, by analyzing past schedule history, it becomes possible to provide optimal schedule suggestions.

[0116] The schedule management system can also estimate the user's emotions and adjust the timing of schedule reminders based on those emotions. For example, if the user is stressed, the reminder timing can be delayed. If the user is relaxed, the reminder timing can be brought forward. If the user is busy, only important reminders will be prioritized. This allows for reminder timing adjustments in response to the user's emotions, resulting in more effective schedule management.

[0117] The schedule management system can further analyze users' social media activity and suggest relevant schedules. For example, it can suggest events and appointments mentioned by users on social media. It can suggest events of interest based on users' social media activity. It can analyze users' social media posts and suggest relevant schedules. In this way, it becomes possible to suggest relevant schedules by analyzing social media activity.

[0118] The schedule management system can further estimate the user's emotions and prioritize schedules based on those emotions. For example, if the user is stressed, it will prioritize collecting high-priority schedules. If the user is relaxed, it will collect detailed schedules and adjust their priorities. If the user is busy, it will prioritize collecting urgent schedules. This allows for the prioritization of important schedules by determining schedule priorities according to the user's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The schedule collection unit collects schedule information. This information includes the date and time, location, and type of event. In addition to obtaining schedule information from calendar services, the schedule collection unit also collects schedule information manually entered by users and schedule information from other applications and services. For example, it obtains schedule information from email applications and messaging applications. Step 2: The sharing unit shares the schedule information collected by the schedule collection unit. The sharing unit can share schedule information within groups such as family, colleagues, and friends, as well as with specific users. The sharing unit also synchronizes schedule information with calendar services. Step 3: The negotiation department conducts automated negotiations based on the schedule information shared by the sharing department. The negotiation department can use generative AI (e.g., text generation AI) to conduct automated negotiations, or it can predict the number of customers based on weather data and conduct automated negotiations based on that prediction. Step 4: The coupon provider provides the coupons obtained by the negotiation team. The coupon provider notifies users of the coupons and can also provide them via email or messaging applications. For example, they can send the coupons to the user's email address or messaging application account.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the schedule collection unit, sharing unit, negotiation unit, coupon provision unit, and weather analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the schedule collection unit is implemented by the control unit 46A of the smart device 14 and acquires schedule information from a calendar service. The sharing unit is implemented by the control unit 46A of the smart device 14 and shares schedule information with family and colleagues. The negotiation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs automated negotiation using a generating AI. The coupon provision unit is implemented by the control unit 46A of the smart device 14 and notifies the user of coupons. The weather analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes weather data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 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.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the schedule collection unit, sharing unit, negotiation unit, coupon provision unit, and weather analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the schedule collection unit is implemented by the control unit 46A of the smart glasses 214 and acquires schedule information from a calendar service. The sharing unit is implemented by the control unit 46A of the smart glasses 214 and shares schedule information with family and colleagues. The negotiation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs automated negotiation using a generating AI. The coupon provision unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user of coupons. The weather analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes weather data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the schedule collection unit, sharing unit, negotiation unit, coupon provision unit, and weather analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the schedule collection unit is implemented by the control unit 46A of the headset terminal 314 and acquires schedule information from a calendar service. The sharing unit is implemented by the control unit 46A of the headset terminal 314 and shares schedule information with family and colleagues. The negotiation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs automated negotiation using a generation AI. The coupon provision unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of coupons. The weather analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes weather data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the schedule collection unit, sharing unit, negotiation unit, coupon provision unit, and weather analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the schedule collection unit is implemented by the control unit 46A of the robot 414 and acquires schedule information from a calendar service. The sharing unit is implemented by, for example, the control unit 46A of the robot 414 and shares schedule information with family and colleagues. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs automated negotiation using a generating AI. The coupon provision unit is implemented by, for example, the control unit 46A of the robot 414 and notifies the user of coupons. The weather analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes weather data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The schedule collection department collects schedule information, A sharing unit that shares the schedule information collected by the aforementioned schedule collection unit, A negotiation unit that performs automated negotiations based on the schedule information shared by the aforementioned sharing unit, The system includes a coupon providing unit that provides coupons obtained by the negotiation unit. A system characterized by the following features. (Note 2) It includes a weather analysis unit that analyzes weather data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned weather analysis unit, We analyze weather data to predict customer numbers based on weather conditions. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned negotiating body said, The system automatically conducts negotiations based on customer count forecasts obtained by the weather analysis department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned coupon provision department, The coupons obtained by the negotiation department will be notified to the users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned schedule collection unit, Collect the number of users in the surrounding area who have free schedules. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned schedule collection unit, It estimates the user's emotions and adjusts the timing of scheduled data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned schedule collection unit, Analyze the user's past schedule history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned schedule collection unit, When collecting schedules, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned schedule collection unit, It estimates user sentiment and determines the priority of the collection schedule based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned schedule collection unit, When collecting schedules, the system prioritizes collecting highly relevant schedules by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned schedule collection unit, When collecting schedules, the system analyzes users' social media activity and collects relevant schedules. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned shared portion is, It estimates the user's emotions and adjusts the sharing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned shared portion is, When sharing, adjust the level of detail based on the importance of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned shared portion is, When sharing, different sharing algorithms are applied depending on the schedule category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned shared portion is, It estimates the user's emotions and determines sharing priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned shared portion is, When sharing, adjust the sharing order based on the submission dates of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned shared portion is, When sharing, adjust the sharing order based on schedule relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the negotiation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned negotiating body said, During negotiations, adjust the level of detail based on the importance of the coupon. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned negotiating body said, During negotiations, different negotiation algorithms are applied depending on the coupon category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned negotiating body said, The system estimates the user's emotions and determines negotiation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned negotiating body said, During negotiations, the order of negotiations will be adjusted based on when the coupons were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned negotiating body said, During negotiations, adjust the order of negotiations based on the relevance of the coupons. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned coupon provision department, We estimate the user's emotions and adjust the coupon delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned coupon provision department, When providing coupons, adjust the level of detail provided based on the importance of the coupon. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned coupon provision department, When providing coupons, different distribution algorithms are applied depending on the coupon category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned coupon provision department, The system estimates user sentiment and prioritizes coupon distribution based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned coupon provision department, When issuing coupons, the order in which they are issued will be adjusted based on when the coupon was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned coupon provision department, When providing coupons, the order in which they are provided will be adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned weather analysis unit, The system estimates user sentiment and adjusts the weather analysis method based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned weather analysis unit, When analyzing weather conditions, we optimize the analysis algorithm by referring to past weather data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned weather analysis unit, When analyzing weather conditions, different analysis methods are applied depending on the weather category. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned weather analysis unit, The system estimates user sentiment and prioritizes weather analysis based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned weather analysis unit, During weather analysis, the order of analysis is adjusted based on when the weather data was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned weather analysis unit, During weather analysis, the order of analysis is adjusted based on the relevance of the weather data. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The schedule collection department collects schedule information, A sharing unit that shares the schedule information collected by the aforementioned schedule collection unit, A negotiation unit that performs automated negotiations based on the schedule information shared by the aforementioned sharing unit, The system includes a coupon providing unit that provides coupons obtained by the negotiation unit. A system characterized by the following features.

2. It includes a weather analysis unit that analyzes weather data. The system according to feature 1.

3. The aforementioned weather analysis unit, We analyze weather data to predict customer numbers based on weather conditions. The system according to feature 2.

4. The aforementioned negotiating body said, The system automatically conducts negotiations based on the customer count forecast obtained from the weather analysis unit. The system according to feature 2.

5. The aforementioned coupon provision department, The coupon obtained by the aforementioned negotiation department will be notified to the user. The system according to feature 1.

6. The aforementioned schedule collection unit, Collect the number of users in the surrounding area who have free schedules. The system according to feature 1.

7. The aforementioned schedule collection unit, It estimates the user's emotions and adjusts the timing of scheduled data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned schedule collection unit, Analyze the user's past schedule history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned schedule collection unit, When collecting schedules, filter them based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned schedule collection unit, It estimates the user's emotions and determines the priority of the collection schedule based on the estimated user emotions. The system according to feature 1.

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