system

Abstract

The system according to this embodiment aims to efficiently facilitate scheduling, discount negotiations, and reservations for users who utilize multiple calendars and communication tools. [Solution] The system according to this embodiment comprises a collection unit, an adjustment unit, a negotiation unit, and a reservation unit. The collection unit collects information from various calendars and communication tools. The adjustment unit adjusts the schedule based on the information collected by the collection unit. The negotiation unit negotiates discounts based on the schedule adjusted by the adjustment unit. The reservation unit makes reservations by applying the discounts negotiated 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 character of the chatbot, 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, there is a problem that it is difficult for users who use a plurality of calendars and communication tools to efficiently perform schedule adjustment, discount negotiation, and reservation.

[0005] The system according to the embodiment aims to enable users who use a plurality of calendars and communication tools to efficiently perform schedule adjustment, discount negotiation, and reservation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an adjustment unit, a negotiation unit, and a reservation unit. The collection unit collects information from various calendars and communication tools. The adjustment unit adjusts the schedule based on the information collected by the collection unit. The negotiation unit negotiates discounts based on the schedule adjusted by the adjustment unit. The reservation unit makes reservations by applying the discounts negotiated by the negotiation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently handle scheduling, discount negotiations, and reservations for users who utilize multiple calendars and communication tools. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 scheduling system according to an embodiment of the present invention is a flexible scheduling service using an AI agent. This scheduling system can be linked with various calendar services and communication tools. For example, the generating AI can handle schedule sharing with family, colleagues, and friends, as well as communication with restaurants, hair salons, hotels, etc., and can smoothly obtain discounts and make reservations through automated negotiation using the generating AI. This scheduling system makes life significantly more convenient for users who have difficulty adjusting their schedules, and provides businesses with effective promotional opportunities based on customer schedules. For example, the AI ​​agent collects and analyzes information from various calendars and communication tools. For example, it obtains schedule information from the calendar service or messaging app used by the user. Next, the generating AI adjusts schedules based on this information. For example, it adjusts schedules with family and colleagues to find common free time. The generating AI also automatically handles reservations and discount negotiations with restaurants, hair salons, hotels, etc. For example, it makes reservations for the date and time desired by the user and obtains discount coupons. Furthermore, the generating AI considers weather data and the user's schedule availability to propose effective promotions to businesses. For example, if fewer customers are expected on rainy days, it can attract customers by distributing discount coupons during specific time slots. In this way, we provide a scheduling service that benefits both users and businesses. This scheduling system is extremely convenient for users who struggle with scheduling, such as parents in dual-income households, busy business people, and young people who frequently make plans to socialize with friends. It is also beneficial for businesses that seek efficient customer acquisition and promotional activities based on customer schedules, such as restaurants, hair salons, hotels, real estate agencies, financial institutions, and car dealerships. As a result, the scheduling system streamlines scheduling for users and provides businesses with effective promotional opportunities.

[0029] The scheduling system according to this embodiment comprises a collection unit, an adjustment unit, a negotiation unit, and a reservation unit. The collection unit can collect information from various calendars and communication tools. For example, the collection unit obtains schedule information from calendar services and messaging apps used by the user. The collection unit can obtain data using APIs. The collection unit can also collect information using scraping techniques. The adjustment unit can adjust schedules based on the information collected by the collection unit. For example, the adjustment unit can adjust schedules with family and colleagues to find common free time. The adjustment unit adjusts schedules using methods for detecting free time and setting priorities. For example, the adjustment unit detects free time from the user's calendar and proposes an optimal schedule. The adjustment unit can also set priorities to prioritize important appointments. The negotiation unit can negotiate discounts based on the schedule adjusted by the adjustment unit. For example, the negotiation unit makes reservations and negotiates discounts with restaurants, hair salons, hotels, etc. The negotiation unit negotiates discounts using negotiation processes and negotiation techniques. For example, the negotiation unit negotiates with restaurants to provide discount coupons. The negotiation department can also negotiate discounts on appointments with hair salons. The booking department can apply the discounts negotiated by the negotiation department when making reservations. For example, the booking department can make a reservation for the user's desired date and time and obtain a discount coupon. The booking department makes reservations using different types of booking systems and procedures. For example, the booking department can make reservations using an online booking system. The booking department can also make reservations using a telephone booking system. This allows the scheduling system to collect information from various calendars and communication tools, adjust schedules, negotiate discounts, and make reservations.

[0030] The data collection unit can collect information from various calendars and communication tools. Specifically, it obtains schedule information from the calendar services and messaging apps used by users. The data collection unit can obtain data using APIs. This allows for an accurate understanding of users' schedules and availability. The data collection unit can also collect information using scraping technology. By using scraping technology, it is possible to obtain necessary information even from services that do not provide APIs. This allows for an understanding of users' communication history and meeting schedules. Furthermore, the data collection unit can centrally manage this information and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the coordination and negotiation departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The scheduling unit can adjust schedules based on information collected by the data collection unit. Specifically, it can coordinate schedules with family and colleagues to find common free time. The scheduling unit adjusts schedules using methods for detecting free time and setting priorities. For example, it can detect free time from a user's calendar and suggest the optimal schedule. The scheduling unit can also set priorities to prioritize important appointments. For example, it can prioritize high-priority appointments such as business meetings or important family events, and then arrange other appointments around them. Furthermore, the scheduling unit can optimize schedules using AI. The AI ​​learns from past schedule data and user behavior patterns to suggest the optimal schedule. For example, the AI ​​analyzes when a user has previously scheduled appointments and generates the most efficient schedule. In addition, the scheduling unit can coordinate schedules among multiple users. For example, it can refer to the calendars of all members of a project team and find a meeting time that everyone can attend. This allows the scheduling unit to efficiently coordinate users' schedules and minimize wasted time.

[0032] The Negotiation Department can conduct discount negotiations based on schedules coordinated by the Coordination Department. Specifically, it negotiates reservations and discounts with restaurants, hair salons, hotels, and other businesses. The Negotiation Department uses negotiation processes and techniques to conduct discount negotiations. For example, the Negotiation Department can negotiate with restaurants to provide discount coupons. The Negotiation Department can also negotiate with hair salons for discounts on reservations. The Negotiation Department can optimize negotiations using AI. The AI ​​analyzes past negotiation data and market trends to propose the most effective negotiation strategy. For example, the AI ​​learns patterns from successful past negotiations and proposes the best negotiation method in similar situations. The Negotiation Department also presents multiple options, allowing users to choose the most suitable discount. For example, it can compare discount offers from multiple restaurants, allowing users to choose the most attractive offer. Furthermore, the Negotiation Department can conduct real-time negotiations. For example, if a user wishes to change their plans at short notice, the Negotiation Department can immediately negotiate and provide a new discount offer to the user. This allows the Negotiation Department to provide users with the best possible discounts and reduce costs.

[0033] The reservation department can make reservations using discounts negotiated by the negotiation department. Specifically, users make reservations for their desired date and time and obtain discount coupons. The reservation department uses various reservation systems and procedures to make reservations. For example, the reservation department can use an online reservation system. The online reservation system allows users to easily complete reservations by selecting their desired date, time, and service. The reservation department can also make reservations using a telephone reservation system. The telephone reservation system is a method in which users make reservations by calling the store directly, and is particularly suitable for elderly users and users unfamiliar with the internet. Furthermore, the reservation department can also manage reservations, including confirmation, modification, and cancellation. For example, if a user wishes to change a reservation, the reservation department will respond quickly and arrange a new reservation. The reservation department can also send reservation reminders to users to prevent them from forgetting their reservations. This allows the reservation department to provide users with a smooth reservation experience and promote service use. In addition, the reservation department can manage reservation history and analyze user preferences and usage trends to provide more personalized services. This allows the reservation department to improve user satisfaction and contribute to acquiring repeat customers.

[0034] The scheduling system includes a weather-aware unit that takes weather data into consideration. This unit enables more appropriate schedule adjustments by taking weather data into account. For example, the weather-aware unit acquires weather data such as temperature, precipitation, and wind speed. Based on this weather data, the unit adjusts the schedule. For example, it adjusts the schedule to avoid outdoor events on rainy days. The weather-aware unit can also acquire weather data in real time and incorporate it into schedule adjustments. For example, it acquires weather forecast data and incorporates it into schedule adjustments. This allows for more appropriate schedule adjustments by considering weather data.

[0035] The scheduling system includes an availability-considering unit that takes into account the user's schedule availability. This unit enables more efficient scheduling by considering the user's schedule availability. For example, it retrieves available time slots and bookable time slots from the calendar. Based on this availability, the unit adjusts the schedule. For instance, it detects available time slots from the user's calendar and proposes the optimal schedule. The availability-considering unit can also acquire schedule availability in real time and reflect this in the scheduling adjustment. For example, it retrieves available time data from a calendar service and reflects this in the scheduling adjustment. This allows for more efficient scheduling by considering the user's schedule availability.

[0036] The scheduling system includes a promotion proposal department that proposes effective promotions to businesses. By proposing effective promotions to businesses, the promotion proposal department streamlines customer acquisition and promotional activities. For example, the department proposes promotions such as discount campaigns and special offers. The department makes proposals based on the content and criteria of the promotion. For example, it might propose a promotion that distributes discount coupons during specific time periods. The department can also analyze the effectiveness of promotions and propose the most suitable ones. For example, it might analyze past promotion data to propose effective promotions. This streamlines customer acquisition and promotional activities by proposing effective promotions to businesses.

[0037] The data collection unit can obtain schedule information from the calendar services and messaging apps used by the user. The data collection unit can obtain data using APIs. Furthermore, the data collection unit can also collect information using scraping techniques. This allows for more efficient schedule management by obtaining schedule information from the calendar services and messaging apps used by the user.

[0038] The scheduling unit can coordinate schedules with family and colleagues and find common free time. For example, the scheduling unit can detect free time from the user's calendar and suggest the best schedule. The scheduling unit can also set priorities to prioritize important appointments. The scheduling unit can also coordinate schedules using a shared calendar. For example, the scheduling unit can use a shared calendar with family and colleagues to find common free time. The scheduling unit can also coordinate schedules using a scheduling tool. For example, the scheduling unit can use a scheduling tool to coordinate schedules with family and colleagues. This streamlines scheduling by coordinating schedules with family and colleagues and finding common free time.

[0039] The negotiating department can make reservations and negotiate discounts with restaurants, hair salons, hotels, and other establishments. For example, the negotiating department can negotiate with restaurants to provide discount coupons. The negotiating department can also negotiate with hair salons for reservation discounts. The negotiating department uses negotiation processes and techniques to conduct discount negotiations. For example, the negotiating department can negotiate with restaurants to provide discount coupons. The negotiating department can also negotiate with hair salons for reservation discounts. The negotiating department can also negotiate with hotels for discounts on accommodation rates. This streamlines scheduling by allowing reservations and discount negotiations with restaurants, hair salons, hotels, and other establishments.

[0040] The reservation department allows users to make reservations for their desired date and time and obtain discount coupons. For example, the reservation department allows users to make reservations for their desired date and time and obtain discount coupons. The reservation department uses various reservation systems and procedures to make reservations. For example, the reservation department uses an online reservation system to make reservations. The reservation department can also make reservations using a telephone reservation system. The reservation department can also simplify the reservation process to allow users to make reservations smoothly. For example, the reservation department provides a system that allows users to complete reservations with a single click. This streamlines scheduling by allowing users to make reservations for their desired date and time and obtain discount coupons.

[0041] The data collection unit can analyze the user's past schedule history and select the optimal data acquisition method. For example, the data collection unit can prioritize acquiring information from calendar services that the user has frequently used in the past. The data collection unit can also select information to acquire during specific time periods from the user's past schedule history. The data collection unit can also analyze the user's past schedule history and prioritize acquiring important events. In this way, by analyzing the user's past schedule history and selecting the optimal data acquisition method, the acquisition of schedule information becomes more efficient.

[0042] The data collection unit can filter schedule information based on the user's current projects and areas of interest. For example, the unit can prioritize obtaining schedule information related to the user's current projects. The unit can also obtain relevant event information based on the user's areas of interest. The unit can also obtain necessary schedule information according to the progress of the user's current projects. This allows the system to obtain highly relevant information by filtering schedule information based on the user's current projects and areas of interest.

[0043] The data collection unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring schedule information. For example, the data collection unit prioritizes acquiring schedule information related to the user's current location. The data collection unit can also acquire information on nearby events based on the user's geographical location. The data collection unit can also acquire relevant schedule information based on the user's travel plans. This streamlines schedule adjustment by prioritizing the acquisition of highly relevant information by considering the user's geographical location.

[0044] The data collection unit can analyze the user's social media activity and obtain relevant information when acquiring schedule information. For example, the data collection unit can acquire event information that the user has mentioned on social media. The data collection unit can also acquire event information that the user's social media followers are participating in. The data collection unit can also acquire relevant schedule information based on the content of the user's social media posts. This streamlines schedule adjustment by analyzing the user's social media activity and acquiring relevant information.

[0045] The scheduling unit can adjust the level of detail in scheduling based on the importance of each schedule. For example, it can perform detailed scheduling for important schedules, and simplified scheduling for lower-priority schedules. The scheduling unit can also gradually change the level of detail in scheduling depending on the importance of each schedule. This allows for efficient scheduling by adjusting the level of detail based on the importance of each schedule.

[0046] The scheduling unit can apply different scheduling algorithms depending on the schedule category during scheduling. For example, it can apply an efficient scheduling algorithm to business-related schedules, and a more flexible one to private schedules. The scheduling unit can also select and apply the most suitable scheduling algorithm for each category. This allows for efficient scheduling by applying different scheduling algorithms depending on the schedule category.

[0047] The scheduling department can determine the priority of schedule adjustments based on the submission deadlines. For example, the scheduling department can prioritize schedules with approaching deadlines. It can also postpone schedules with ample time before their submission deadlines. The scheduling department can also gradually change the priority of adjustments based on the submission deadlines. This allows for efficient schedule adjustments by determining the priority of adjustments based on the submission deadlines.

[0048] The scheduling unit can adjust the order of scheduling based on the relevance of the schedules. For example, the scheduling unit can prioritize scheduling that is highly relevant. It can also postpone scheduling that is less relevant. The scheduling unit can also change the order of scheduling in stages based on the relevance of the schedules. This allows for efficient scheduling by adjusting the order of scheduling based on the relevance of the schedules.

[0049] The negotiation department can improve the accuracy of negotiations by referring to past negotiation history. For example, the negotiation department can select the optimal negotiation method based on past successful negotiation history. The negotiation department can also analyze past unsuccessful negotiation history and incorporate improvements. The negotiation department can improve the accuracy of negotiations by referring to past negotiation history. This allows for more efficient negotiations by improving the accuracy of negotiations by referring to past negotiation history.

[0050] The negotiating department can conduct negotiations while considering the attributes of the negotiating party. For example, the negotiating department can select the most appropriate negotiation method depending on the industry and position of the negotiating party. The negotiating department can also improve the accuracy of negotiations by referring to the negotiating party's past negotiation history. The negotiating department can also adjust the negotiation process based on the negotiating party's attributes. In this way, conducting negotiations while considering the attributes of the negotiating party makes efficient negotiations possible.

[0051] The negotiating department can conduct negotiations while considering the geographical distribution of the negotiating parties. For example, if the negotiating parties are located in different regions, the negotiating department will consider the characteristics of each region when conducting negotiations. The negotiating department can also select the optimal negotiation method based on the geographical distribution of the negotiating parties. The negotiating department can also conduct negotiations while considering the culture and business practices of each region. In this way, conducting negotiations while considering the geographical distribution of the negotiating parties makes efficient negotiations possible.

[0052] The negotiating department can improve the accuracy of negotiations by referring to relevant literature on the negotiating party during the negotiation process. For example, the negotiating department can improve the accuracy of negotiations by referring to relevant literature on the negotiating party's industry. The negotiating department can also improve the accuracy of negotiations by referring to literature on the negotiating party's past statements and actions. Based on the relevant literature on the negotiating party, the negotiating department can also select the most appropriate negotiation method. In this way, by improving the accuracy of negotiations by referring to relevant literature on the negotiating party, more efficient negotiations become possible.

[0053] The reservation department can select the optimal reservation method by referring to past reservation history when a reservation is made. For example, the reservation department can suggest the optimal reservation method based on the reservation method the user has used in the past. The reservation department can also prioritize suggesting stores and services that the user frequently uses based on their past reservation history. The reservation department can also analyze the user's past reservation history and select the most efficient reservation method. As a result, efficient reservations become possible by selecting the optimal reservation method by referring to past reservation history.

[0054] The reservation system can customize the reservation process based on the user's current lifestyle. For example, if the user is busy, the system can provide a way to complete the reservation quickly. If the user is relaxed, the system can also provide detailed reservation options. The system can also suggest the most suitable reservation method according to the user's lifestyle. This allows for efficient reservations by customizing the reservation process based on the user's current lifestyle.

[0055] The reservation system can select the most suitable reservation method by considering the user's geographical location during the reservation process. For example, the reservation system can prioritize reservations related to the user's current location. The reservation system can also suggest nearby stores and services based on the user's geographical location. The reservation system can also select the most suitable reservation method based on the user's travel plans. This allows for more efficient reservations by selecting the most suitable reservation method based on the user's geographical location.

[0056] The reservation department can analyze a user's social media activity and suggest reservation methods during the reservation process. For example, the reservation department can prioritize suggesting stores and services mentioned by the user on social media. It can also suggest stores and services used by the user's social media followers. The reservation department can also suggest the most suitable reservation method based on the content of the user's social media posts. This allows for more efficient reservations by analyzing the user's social media activity and suggesting reservation methods accordingly.

[0057] The weather analysis unit can predict the current weather by referring to past weather data when considering weather data. For example, the weather analysis unit predicts the current weather based on past weather data. The weather analysis unit can also predict changes in weather by referring to past weather data. The weather analysis unit can also analyze past weather data to make the most accurate weather forecast. This enables efficient consideration of weather data by predicting the current weather by referring to past weather data.

[0058] The weather analysis unit can acquire weather data while considering the user's geographical location. For example, the weather analysis unit prioritizes acquiring weather data for the user's current location. The weather analysis unit can also acquire nearby weather data based on the user's geographical location. The weather analysis unit can also acquire relevant weather data based on the user's travel plans. This enables efficient weather data analysis by acquiring weather data while considering the user's geographical location.

[0059] The availability analysis unit can predict the current availability by referring to past availability data when considering availability. For example, the availability analysis unit predicts the current availability based on past availability data. The availability analysis unit can also predict changes in availability by referring to past availability data. The availability analysis unit can also perform the most accurate availability prediction by analyzing past availability data. This makes it possible to efficiently consider availability by predicting the current availability by referring to past availability data.

[0060] The availability consideration unit can acquire availability data while considering the user's geographical location information. For example, the availability consideration unit prioritizes acquiring availability data for the user's current location. The availability consideration unit can also acquire nearby availability data based on the user's geographical location information. The availability consideration unit can also acquire relevant availability data based on the user's travel plans. This enables efficient availability consideration by acquiring availability data while considering the user's geographical location information.

[0061] The promotion proposal department can select the optimal proposal method by referring to past promotion history when making a promotion proposal. For example, the promotion proposal department can select the optimal proposal method based on the user's past promotion history. The promotion proposal department can also select an effective proposal method from the user's past promotion history. The promotion proposal department can also analyze the user's past promotion history and select the most effective proposal method. This allows for efficient promotion proposals by selecting the optimal proposal method by referring to past promotion history.

[0062] The promotion proposal department can select the most suitable proposal method when proposing promotions, taking into account the user's geographical location. For example, the department can propose promotions related to the user's current location. Based on the user's geographical location, the department can also propose promotions for nearby stores and services. Based on the user's travel plans, the department can also propose the most suitable promotions. By selecting the most suitable proposal method while considering the user's geographical location, efficient promotion proposals become possible.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] A scheduling system can include a health-conscious unit that takes user health data into consideration. This unit, for example, acquires user heart rate and sleep data and adjusts the schedule based on this data. For instance, if a user has a high heart rate, the schedule can be adjusted to a more relaxing time. It can also adjust the schedule to ensure sufficient rest based on the user's sleep data. This enables scheduling adjustments that take the user's health condition into account.

[0065] A scheduling system can include a hobby-considering unit that takes into account the user's hobbies and interests. This unit, for example, prioritizes scheduling events and activities that the user is interested in. For instance, if the user is interested in music concerts, the system can prioritize scheduling concerts. Similarly, if the user is interested in sporting events, the system can prioritize scheduling sporting events. This allows for scheduling that takes the user's hobbies and interests into account.

[0066] A scheduling system can include a behavioral analysis unit that analyzes the user's past behavioral patterns. For example, the behavioral analysis unit can analyze what kind of schedules the user preferred in the past and propose an optimal schedule. For instance, it can prioritize scheduling time slots that the user frequently used in the past. It can also adjust the schedule considering time slots the user avoided in the past. This enables schedule adjustments that take into account the user's past behavioral patterns.

[0067] A scheduling system may include a location-aware unit that takes into account the user's geographical location. This unit, for example, prioritizes obtaining scheduling information related to the user's current location. For instance, it can obtain information about events taking place near the user's current location. It can also obtain relevant scheduling information based on the user's travel plans. This enables scheduling adjustments that take the user's geographical location into account.

[0068] The scheduling system may include a social media analysis unit that analyzes the user's social media activity. For example, the social media analysis unit can retrieve event information mentioned by the user on social media. For instance, it can prioritize scheduling events mentioned by the user on social media. It can also retrieve event information that the user's social media followers will attend. This enables scheduling adjustments that take the user's social media activity into consideration.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit gathers information from various calendars and communication tools. For example, the data collection unit obtains schedule information from the calendar service and messaging app used by the user. The data collection unit can obtain data using APIs. The data collection unit can also gather information using scraping techniques. Step 2: The scheduling unit adjusts the schedule based on the information collected by the collection unit. For example, it adjusts schedules with family and colleagues to find common free time. The scheduling unit adjusts the schedule using methods for detecting free time and setting priorities. For example, the scheduling unit detects free time from the user's calendar and suggests the best schedule. The scheduling unit can also set priorities to prioritize important appointments. Step 3: The Negotiation Department conducts discount negotiations based on the schedule set up by the Coordination Department. For example, they negotiate reservations and discounts with restaurants, hair salons, hotels, etc. The Negotiation Department conducts discount negotiations using negotiation processes and techniques. For example, the Negotiation Department negotiates with restaurants to provide discount coupons. The Negotiation Department can also negotiate with hair salons for discounts on reservations. Step 4: The reservations department makes the reservation, applying the discount negotiated by the negotiation department. For example, the user makes a reservation for their desired date and time and obtains a discount coupon. The reservations department makes the reservation using the type of reservation system and reservation procedure. For example, the reservations department makes reservations using an online reservation system. The reservations department can also make reservations using a telephone reservation system.

[0071] (Example of form 2) The scheduling system according to an embodiment of the present invention is a flexible scheduling service using an AI agent. This scheduling system can be linked with various calendar services and communication tools. For example, the generating AI can handle schedule sharing with family, colleagues, and friends, as well as communication with restaurants, hair salons, hotels, etc., and can smoothly obtain discounts and make reservations through automated negotiation using the generating AI. This scheduling system makes life significantly more convenient for users who have difficulty adjusting their schedules, and provides businesses with effective promotional opportunities based on customer schedules. For example, the AI ​​agent collects and analyzes information from various calendars and communication tools. For example, it obtains schedule information from the calendar service or messaging app used by the user. Next, the generating AI adjusts schedules based on this information. For example, it adjusts schedules with family and colleagues to find common free time. The generating AI also automatically handles reservations and discount negotiations with restaurants, hair salons, hotels, etc. For example, it makes reservations for the date and time desired by the user and obtains discount coupons. Furthermore, the generating AI considers weather data and the user's schedule availability to propose effective promotions to businesses. For example, if fewer customers are expected on rainy days, it can attract customers by distributing discount coupons during specific time slots. In this way, we provide a scheduling service that benefits both users and businesses. This scheduling system is extremely convenient for users who struggle with scheduling, such as parents in dual-income households, busy business people, and young people who frequently make plans to socialize with friends. It is also beneficial for businesses that seek efficient customer acquisition and promotional activities based on customer schedules, such as restaurants, hair salons, hotels, real estate agencies, financial institutions, and car dealerships. As a result, the scheduling system streamlines scheduling for users and provides businesses with effective promotional opportunities.

[0072] The scheduling system according to this embodiment comprises a collection unit, an adjustment unit, a negotiation unit, and a reservation unit. The collection unit can collect information from various calendars and communication tools. For example, the collection unit obtains schedule information from calendar services and messaging apps used by the user. The collection unit can obtain data using APIs. The collection unit can also collect information using scraping techniques. The adjustment unit can adjust schedules based on the information collected by the collection unit. For example, the adjustment unit can adjust schedules with family and colleagues to find common free time. The adjustment unit adjusts schedules using methods for detecting free time and setting priorities. For example, the adjustment unit detects free time from the user's calendar and proposes an optimal schedule. The adjustment unit can also set priorities to prioritize important appointments. The negotiation unit can negotiate discounts based on the schedule adjusted by the adjustment unit. For example, the negotiation unit makes reservations and negotiates discounts with restaurants, hair salons, hotels, etc. The negotiation unit negotiates discounts using negotiation processes and negotiation techniques. For example, the negotiation unit negotiates with restaurants to provide discount coupons. The negotiation department can also negotiate discounts on appointments with hair salons. The booking department can apply the discounts negotiated by the negotiation department when making reservations. For example, the booking department can make a reservation for the user's desired date and time and obtain a discount coupon. The booking department makes reservations using different types of booking systems and procedures. For example, the booking department can make reservations using an online booking system. The booking department can also make reservations using a telephone booking system. This allows the scheduling system to collect information from various calendars and communication tools, adjust schedules, negotiate discounts, and make reservations.

[0073] The data collection unit can collect information from various calendars and communication tools. Specifically, it obtains schedule information from the calendar services and messaging apps used by users. The data collection unit can obtain data using APIs. This allows for an accurate understanding of users' schedules and availability. The data collection unit can also collect information using scraping technology. By using scraping technology, it is possible to obtain necessary information even from services that do not provide APIs. This allows for an understanding of users' communication history and meeting schedules. Furthermore, the data collection unit can centrally manage this information and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the coordination and negotiation departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0074] The scheduling unit can adjust schedules based on information collected by the data collection unit. Specifically, it can coordinate schedules with family and colleagues to find common free time. The scheduling unit adjusts schedules using methods for detecting free time and setting priorities. For example, it can detect free time from a user's calendar and suggest the optimal schedule. The scheduling unit can also set priorities to prioritize important appointments. For example, it can prioritize high-priority appointments such as business meetings or important family events, and then arrange other appointments around them. Furthermore, the scheduling unit can optimize schedules using AI. The AI ​​learns from past schedule data and user behavior patterns to suggest the optimal schedule. For example, the AI ​​analyzes when a user has previously scheduled appointments and generates the most efficient schedule. In addition, the scheduling unit can coordinate schedules among multiple users. For example, it can refer to the calendars of all members of a project team and find a meeting time that everyone can attend. This allows the scheduling unit to efficiently coordinate users' schedules and minimize wasted time.

[0075] The Negotiation Department can conduct discount negotiations based on schedules coordinated by the Coordination Department. Specifically, it negotiates reservations and discounts with restaurants, hair salons, hotels, and other businesses. The Negotiation Department uses negotiation processes and techniques to conduct discount negotiations. For example, the Negotiation Department can negotiate with restaurants to provide discount coupons. The Negotiation Department can also negotiate with hair salons for discounts on reservations. The Negotiation Department can optimize negotiations using AI. The AI ​​analyzes past negotiation data and market trends to propose the most effective negotiation strategy. For example, the AI ​​learns patterns from successful past negotiations and proposes the best negotiation method in similar situations. The Negotiation Department also presents multiple options, allowing users to choose the most suitable discount. For example, it can compare discount offers from multiple restaurants, allowing users to choose the most attractive offer. Furthermore, the Negotiation Department can conduct real-time negotiations. For example, if a user wishes to change their plans at short notice, the Negotiation Department can immediately negotiate and provide a new discount offer to the user. This allows the Negotiation Department to provide users with the best possible discounts and reduce costs.

[0076] The reservation department can make reservations using discounts negotiated by the negotiation department. Specifically, users make reservations for their desired date and time and obtain discount coupons. The reservation department uses various reservation systems and procedures to make reservations. For example, the reservation department can use an online reservation system. The online reservation system allows users to easily complete reservations by selecting their desired date, time, and service. The reservation department can also make reservations using a telephone reservation system. The telephone reservation system is a method in which users make reservations by calling the store directly, and is particularly suitable for elderly users and users unfamiliar with the internet. Furthermore, the reservation department can also manage reservations, including confirmation, modification, and cancellation. For example, if a user wishes to change a reservation, the reservation department will respond quickly and arrange a new reservation. The reservation department can also send reservation reminders to users to prevent them from forgetting their reservations. This allows the reservation department to provide users with a smooth reservation experience and promote service use. In addition, the reservation department can manage reservation history and analyze user preferences and usage trends to provide more personalized services. This allows the reservation department to improve user satisfaction and contribute to acquiring repeat customers.

[0077] The scheduling system includes a weather-aware unit that takes weather data into consideration. This unit enables more appropriate schedule adjustments by taking weather data into account. For example, the weather-aware unit acquires weather data such as temperature, precipitation, and wind speed. Based on this weather data, the unit adjusts the schedule. For example, it adjusts the schedule to avoid outdoor events on rainy days. The weather-aware unit can also acquire weather data in real time and incorporate it into schedule adjustments. For example, it acquires weather forecast data and incorporates it into schedule adjustments. This allows for more appropriate schedule adjustments by considering weather data.

[0078] The scheduling system includes an availability-considering unit that takes into account the user's schedule availability. This unit enables more efficient scheduling by considering the user's schedule availability. For example, it retrieves available time slots and bookable time slots from the calendar. Based on this availability, the unit adjusts the schedule. For instance, it detects available time slots from the user's calendar and proposes the optimal schedule. The availability-considering unit can also acquire schedule availability in real time and reflect this in the scheduling adjustment. For example, it retrieves available time data from a calendar service and reflects this in the scheduling adjustment. This allows for more efficient scheduling by considering the user's schedule availability.

[0079] The scheduling system includes a promotion proposal department that proposes effective promotions to businesses. By proposing effective promotions to businesses, the promotion proposal department streamlines customer acquisition and promotional activities. For example, the department proposes promotions such as discount campaigns and special offers. The department makes proposals based on the content and criteria of the promotion. For example, it might propose a promotion that distributes discount coupons during specific time periods. The department can also analyze the effectiveness of promotions and propose the most suitable ones. For example, it might analyze past promotion data to propose effective promotions. This streamlines customer acquisition and promotional activities by proposing effective promotions to businesses.

[0080] The data collection unit can obtain schedule information from the calendar services and messaging apps used by the user. The data collection unit can obtain data using APIs. Furthermore, the data collection unit can also collect information using scraping techniques. This allows for more efficient schedule management by obtaining schedule information from the calendar services and messaging apps used by the user.

[0081] The scheduling unit can coordinate schedules with family and colleagues and find common free time. For example, the scheduling unit can detect free time from the user's calendar and suggest the best schedule. The scheduling unit can also set priorities to prioritize important appointments. The scheduling unit can also coordinate schedules using a shared calendar. For example, the scheduling unit can use a shared calendar with family and colleagues to find common free time. The scheduling unit can also coordinate schedules using a scheduling tool. For example, the scheduling unit can use a scheduling tool to coordinate schedules with family and colleagues. This streamlines scheduling by coordinating schedules with family and colleagues and finding common free time.

[0082] The negotiating department can make reservations and negotiate discounts with restaurants, hair salons, hotels, and other establishments. For example, the negotiating department can negotiate with restaurants to provide discount coupons. The negotiating department can also negotiate with hair salons for reservation discounts. The negotiating department uses negotiation processes and techniques to conduct discount negotiations. For example, the negotiating department can negotiate with restaurants to provide discount coupons. The negotiating department can also negotiate with hair salons for reservation discounts. The negotiating department can also negotiate with hotels for discounts on accommodation rates. This streamlines scheduling by allowing reservations and discount negotiations with restaurants, hair salons, hotels, and other establishments.

[0083] The reservation department allows users to make reservations for their desired date and time and obtain discount coupons. For example, the reservation department allows users to make reservations for their desired date and time and obtain discount coupons. The reservation department uses various reservation systems and procedures to make reservations. For example, the reservation department uses an online reservation system to make reservations. The reservation department can also make reservations using a telephone reservation system. The reservation department can also simplify the reservation process to allow users to make reservations smoothly. For example, the reservation department provides a system that allows users to complete reservations with a single click. This streamlines scheduling by allowing users to make reservations for their desired date and time and obtain discount coupons.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of acquiring schedule information based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit will acquire schedule information during times when the user can relax. If the user is busy, the data collection unit can also prioritize acquiring only important schedule information. If the user is relaxed, the data collection unit can also acquire detailed schedule information. This allows for the acquisition of more appropriate schedule information by adjusting the timing of schedule information acquisition 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The data collection unit can analyze the user's past schedule history and select the optimal data acquisition method. For example, the data collection unit can prioritize acquiring information from calendar services that the user has frequently used in the past. The data collection unit can also select information to acquire during specific time periods from the user's past schedule history. The data collection unit can also analyze the user's past schedule history and prioritize acquiring important events. In this way, by analyzing the user's past schedule history and selecting the optimal data acquisition method, the acquisition of schedule information becomes more efficient.

[0086] The data collection unit can filter schedule information based on the user's current projects and areas of interest. For example, the unit can prioritize obtaining schedule information related to the user's current projects. The unit can also obtain relevant event information based on the user's areas of interest. The unit can also obtain necessary schedule information according to the progress of the user's current projects. This allows the system to obtain highly relevant information by filtering schedule information based on the user's current projects and areas of interest.

[0087] The data collection unit can estimate the user's emotions and determine the priority of schedule information to retrieve based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize retrieving only important schedule information. If the user is relaxed, the data collection unit can also retrieve detailed schedule information. If the user is busy, the data collection unit can also prioritize retrieving the most important schedule information. This ensures that important information is retrieved preferentially by prioritizing schedule information 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The data collection unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring schedule information. For example, the data collection unit prioritizes acquiring schedule information related to the user's current location. The data collection unit can also acquire information on nearby events based on the user's geographical location. The data collection unit can also acquire relevant schedule information based on the user's travel plans. This streamlines schedule adjustment by prioritizing the acquisition of highly relevant information by considering the user's geographical location.

[0089] The data collection unit can analyze the user's social media activity and obtain relevant information when acquiring schedule information. For example, the data collection unit can acquire event information that the user has mentioned on social media. The data collection unit can also acquire event information that the user's social media followers are participating in. The data collection unit can also acquire relevant schedule information based on the content of the user's social media posts. This streamlines schedule adjustment by analyzing the user's social media activity and acquiring relevant information.

[0090] The adjustment unit can estimate the user's emotions and adjust the way schedule adjustments are presented based on those emotions. For example, if the user is stressed, the adjustment unit will use a simple and easy-to-understand presentation. If the user is relaxed, the adjustment unit may also use a presentation that includes detailed explanations. If the user is busy, the adjustment unit may also use a presentation that gets straight to the point. By adjusting the presentation of schedule adjustments based on the user's emotions, it becomes possible to create schedule adjustments that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0091] The scheduling unit can adjust the level of detail in scheduling based on the importance of each schedule. For example, it can perform detailed scheduling for important schedules, and simplified scheduling for lower-priority schedules. The scheduling unit can also gradually change the level of detail in scheduling depending on the importance of each schedule. This allows for efficient scheduling by adjusting the level of detail based on the importance of each schedule.

[0092] The scheduling unit can apply different scheduling algorithms depending on the schedule category during scheduling. For example, it can apply an efficient scheduling algorithm to business-related schedules, and a more flexible one to private schedules. The scheduling unit can also select and apply the most suitable scheduling algorithm for each category. This allows for efficient scheduling by applying different scheduling algorithms depending on the schedule category.

[0093] The adjustment unit can estimate the user's emotions and adjust the length of the schedule adjustment based on the estimated emotions. For example, if the user is stressed, the adjustment unit will complete the adjustment in a short time. If the user is relaxed, the adjustment unit can also perform a detailed adjustment. If the user is busy, the adjustment unit can also perform a quick adjustment. This allows for efficient schedule adjustment by adjusting the length of the schedule adjustment based on 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.

[0094] The scheduling department can determine the priority of schedule adjustments based on the submission deadlines. For example, the scheduling department can prioritize schedules with approaching deadlines. It can also postpone schedules with ample time before their submission deadlines. The scheduling department can also gradually change the priority of adjustments based on the submission deadlines. This allows for efficient schedule adjustments by determining the priority of adjustments based on the submission deadlines.

[0095] The scheduling unit can adjust the order of scheduling based on the relevance of the schedules. For example, the scheduling unit can prioritize scheduling that is highly relevant. It can also postpone scheduling that is less relevant. The scheduling unit can also change the order of scheduling in stages based on the relevance of the schedules. This allows for efficient scheduling by adjusting the order of scheduling based on the relevance of the schedules.

[0096] The negotiation unit can estimate the user's emotions and adjust the negotiation criteria based on those estimated emotions. For example, if the user is stressed, the negotiation unit can quickly conclude the negotiation. If the user is relaxed, the negotiation unit can conduct a detailed negotiation. If the user is busy, the negotiation unit can focus on the key points. This allows for efficient negotiation by adjusting the negotiation criteria based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The negotiation department can improve the accuracy of negotiations by referring to past negotiation history. For example, the negotiation department can select the optimal negotiation method based on past successful negotiation history. The negotiation department can also analyze past unsuccessful negotiation history and incorporate improvements. The negotiation department can improve the accuracy of negotiations by referring to past negotiation history. This allows for more efficient negotiations by improving the accuracy of negotiations by referring to past negotiation history.

[0098] The negotiating department can conduct negotiations while considering the attributes of the negotiating party. For example, the negotiating department can select the most appropriate negotiation method depending on the industry and position of the negotiating party. The negotiating department can also improve the accuracy of negotiations by referring to the negotiating party's past negotiation history. The negotiating department can also adjust the negotiation process based on the negotiating party's attributes. In this way, conducting negotiations while considering the attributes of the negotiating party makes efficient negotiations possible.

[0099] The negotiation function can estimate the user's emotions and adjust the order in which negotiation results are displayed based on the estimated emotions. For example, if the user is stressed, the negotiation function will display important results first. If the user is relaxed, the negotiation function can also display detailed results sequentially. If the user is busy, the negotiation function can also display concise results first. This allows for more efficient negotiation by adjusting the order in which negotiation results are displayed 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The negotiating department can conduct negotiations while considering the geographical distribution of the negotiating parties. For example, if the negotiating parties are located in different regions, the negotiating department will consider the characteristics of each region when conducting negotiations. The negotiating department can also select the optimal negotiation method based on the geographical distribution of the negotiating parties. The negotiating department can also conduct negotiations while considering the culture and business practices of each region. In this way, conducting negotiations while considering the geographical distribution of the negotiating parties makes efficient negotiations possible.

[0101] The negotiating department can improve the accuracy of negotiations by referring to relevant literature on the negotiating party during the negotiation process. For example, the negotiating department can improve the accuracy of negotiations by referring to relevant literature on the negotiating party's industry. The negotiating department can also improve the accuracy of negotiations by referring to literature on the negotiating party's past statements and actions. Based on the relevant literature on the negotiating party, the negotiating department can also select the most appropriate negotiation method. In this way, by improving the accuracy of negotiations by referring to relevant literature on the negotiating party, more efficient negotiations become possible.

[0102] The booking system can estimate the user's emotions and adjust the booking method based on those emotions. For example, if the user is stressed, the system can provide a simple booking method. If the user is relaxed, the system can also provide detailed booking options. If the user is busy, the system can provide a way to complete the booking quickly. This allows for efficient booking by adjusting the booking method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The reservation department can select the optimal reservation method by referring to past reservation history when a reservation is made. For example, the reservation department can suggest the optimal reservation method based on the reservation method the user has used in the past. The reservation department can also prioritize suggesting stores and services that the user frequently uses based on their past reservation history. The reservation department can also analyze the user's past reservation history and select the most efficient reservation method. As a result, efficient reservations become possible by selecting the optimal reservation method by referring to past reservation history.

[0104] The reservation system can customize the reservation process based on the user's current lifestyle. For example, if the user is busy, the system can provide a way to complete the reservation quickly. If the user is relaxed, the system can also provide detailed reservation options. The system can also suggest the most suitable reservation method according to the user's lifestyle. This allows for efficient reservations by customizing the reservation process based on the user's current lifestyle.

[0105] The booking system can estimate a user's emotions and prioritize bookings based on those emotions. For example, if a user is feeling stressed, the system will prioritize important bookings. If a user is relaxed, the system can also offer more detailed booking options. If a user is busy, the system can provide a way to complete bookings quickly. This allows for efficient booking by prioritizing bookings based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The reservation system can select the most suitable reservation method by considering the user's geographical location during the reservation process. For example, the reservation system can prioritize reservations related to the user's current location. The reservation system can also suggest nearby stores and services based on the user's geographical location. The reservation system can also select the most suitable reservation method based on the user's travel plans. This allows for more efficient reservations by selecting the most suitable reservation method based on the user's geographical location.

[0107] The reservation department can analyze a user's social media activity and suggest reservation methods during the reservation process. For example, the reservation department can prioritize suggesting stores and services mentioned by the user on social media. It can also suggest stores and services used by the user's social media followers. The reservation department can also suggest the most suitable reservation method based on the content of the user's social media posts. This allows for more efficient reservations by analyzing the user's social media activity and suggesting reservation methods accordingly.

[0108] The weather-aware unit can estimate the user's emotions and adjust how weather data is considered based on those emotions. For example, if the user is stressed, the weather-aware unit can simplify the display of weather data. If the user is relaxed, the weather-aware unit can also display detailed weather data. If the user is busy, the weather-aware unit can also display only the most important weather data. This allows for efficient weather data analysis by adjusting how weather data is considered 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The weather analysis unit can predict the current weather by referring to past weather data when considering weather data. For example, the weather analysis unit predicts the current weather based on past weather data. The weather analysis unit can also predict changes in weather by referring to past weather data. The weather analysis unit can also analyze past weather data to make the most accurate weather forecast. This enables efficient consideration of weather data by predicting the current weather by referring to past weather data.

[0110] The weather-aware unit can estimate the user's emotions and adjust the importance of weather data based on those emotions. For example, if the user is stressed, the unit will display only important weather data. If the user is relaxed, the unit can also display detailed weather data. If the user is busy, the unit can display simplified weather data. This allows for efficient consideration of weather data by adjusting its importance 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The weather analysis unit can acquire weather data while considering the user's geographical location. For example, the weather analysis unit prioritizes acquiring weather data for the user's current location. The weather analysis unit can also acquire nearby weather data based on the user's geographical location. The weather analysis unit can also acquire relevant weather data based on the user's travel plans. This enables efficient weather data analysis by acquiring weather data while considering the user's geographical location.

[0112] The availability consideration unit can estimate the user's emotions and adjust the availability consideration method based on the estimated user emotions. For example, if the user is feeling stressed, the availability consideration unit will only display important availability. If the user is relaxed, the availability consideration unit can also display detailed availability. If the user is busy, the availability consideration unit can also display simplified availability. This allows for efficient availability consideration by adjusting the availability consideration method based on 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.

[0113] The availability analysis unit can predict the current availability by referring to past availability data when considering availability. For example, the availability analysis unit predicts the current availability based on past availability data. The availability analysis unit can also predict changes in availability by referring to past availability data. The availability analysis unit can also perform the most accurate availability prediction by analyzing past availability data. This makes it possible to efficiently consider availability by predicting the current availability by referring to past availability data.

[0114] The availability consideration unit can estimate the user's emotions and adjust the importance of availability based on the estimated emotions. For example, if the user is feeling stressed, the availability consideration unit will only display important availability. If the user is relaxed, the availability consideration unit can also display detailed availability. If the user is busy, the availability consideration unit can also display simplified availability. This allows for efficient availability consideration by adjusting the importance of availability based on 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.

[0115] The availability consideration unit can acquire availability data while considering the user's geographical location information. For example, the availability consideration unit prioritizes acquiring availability data for the user's current location. The availability consideration unit can also acquire nearby availability data based on the user's geographical location information. The availability consideration unit can also acquire relevant availability data based on the user's travel plans. This enables efficient availability consideration by acquiring availability data while considering the user's geographical location information.

[0116] The promotion suggestion department can estimate the user's emotions and adjust the method of promotion suggestion based on the estimated emotions. For example, if the user is feeling stressed, the promotion suggestion department will make a simple promotion suggestion. If the user is relaxed, the promotion suggestion department may also make a detailed promotion suggestion. If the user is busy, the promotion suggestion department may also make only the most important promotion suggestion. This allows for efficient promotion suggestions by adjusting the method of promotion suggestion based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The promotion proposal department can select the optimal proposal method by referring to past promotion history when making a promotion proposal. For example, the promotion proposal department can select the optimal proposal method based on the user's past promotion history. The promotion proposal department can also select an effective proposal method from the user's past promotion history. The promotion proposal department can also analyze the user's past promotion history and select the most effective proposal method. This allows for efficient promotion proposals by selecting the optimal proposal method by referring to past promotion history.

[0118] The promotion proposal department can estimate the user's emotions and prioritize promotion proposals based on those emotions. For example, if the user is stressed, the department will prioritize important promotion proposals. If the user is relaxed, the department can also provide detailed promotion proposals. If the user is busy, the department can provide only important promotion proposals. This allows for efficient promotion proposals by prioritizing them 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The promotion proposal department can select the most suitable proposal method when proposing promotions, taking into account the user's geographical location. For example, the department can propose promotions related to the user's current location. Based on the user's geographical location, the department can also propose promotions for nearby stores and services. Based on the user's travel plans, the department can also propose the most suitable promotions. By selecting the most suitable proposal method while considering the user's geographical location, efficient promotion proposals become possible.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] A scheduling system can include a health-conscious unit that takes user health data into consideration. This unit, for example, acquires user heart rate and sleep data and adjusts the schedule based on this data. For instance, if a user has a high heart rate, the schedule can be adjusted to a more relaxing time. It can also adjust the schedule to ensure sufficient rest based on the user's sleep data. This enables scheduling adjustments that take the user's health condition into account.

[0122] A scheduling system can include a hobby-considering unit that takes into account the user's hobbies and interests. This unit, for example, prioritizes scheduling events and activities that the user is interested in. For instance, if the user is interested in music concerts, the system can prioritize scheduling concerts. Similarly, if the user is interested in sporting events, the system can prioritize scheduling sporting events. This allows for scheduling that takes the user's hobbies and interests into account.

[0123] A scheduling system can include a behavioral analysis unit that analyzes the user's past behavioral patterns. For example, the behavioral analysis unit can analyze what kind of schedules the user preferred in the past and propose an optimal schedule. For instance, it can prioritize scheduling time slots that the user frequently used in the past. It can also adjust the schedule considering time slots the user avoided in the past. This enables schedule adjustments that take into account the user's past behavioral patterns.

[0124] The scheduling system can estimate the user's emotions and adjust the way schedule notifications are delivered based on those emotions. For example, if the user is stressed, a simple and easy-to-understand notification method is used. If the user is relaxed, a notification method with detailed explanations may be used. If the user is busy, a notification method that gets straight to the point may be used. This allows for schedule notifications that are easy for the user to understand by adjusting the notification method based on the user's emotions.

[0125] The scheduling system can estimate the user's emotions and adjust schedule priorities based on those emotions. For example, if the user is stressed, important schedules can be prioritized. If the user is relaxed, detailed schedules can be adjusted. If the user is busy, adjustments can be made quickly. This allows for efficient scheduling by adjusting schedule priorities based on the user's emotions.

[0126] The scheduling system can estimate the user's emotions and adjust how the schedule is displayed based on those emotions. For example, if the user is stressed, a simple and easy-to-understand display method is used. If the user is relaxed, a display method with detailed explanations may be used. If the user is busy, a display method that focuses on the essentials may be used. By adjusting the schedule display method based on the user's emotions, it becomes possible to display the schedule in a way that is easy for the user to understand.

[0127] The scheduling system can estimate the user's emotions and adjust the schedule reminder method based on those emotions. For example, if the user is stressed, a simple and easy-to-understand reminder method can be used. If the user is relaxed, a reminder method with detailed explanations can be used. If the user is busy, a concise reminder method can be used. This allows for schedule reminders that are easy for the user to understand by adjusting the reminder method based on the user's emotions.

[0128] The scheduling system can estimate the user's emotions and adjust the timing of schedule notifications based on those emotions. For example, if the user is stressed, notifications can be sent during times when they can relax. If the user is relaxed, more detailed notifications can be sent. If the user is busy, only important notifications can be prioritized. This allows for efficient schedule notifications by adjusting the timing of schedule notifications based on the user's emotions.

[0129] A scheduling system may include a location-aware unit that takes into account the user's geographical location. This unit, for example, prioritizes obtaining scheduling information related to the user's current location. For instance, it can obtain information about events taking place near the user's current location. It can also obtain relevant scheduling information based on the user's travel plans. This enables scheduling adjustments that take the user's geographical location into account.

[0130] The scheduling system may include a social media analysis unit that analyzes the user's social media activity. For example, the social media analysis unit can retrieve event information mentioned by the user on social media. For instance, it can prioritize scheduling events mentioned by the user on social media. It can also retrieve event information that the user's social media followers will attend. This enables scheduling adjustments that take the user's social media activity into consideration.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data collection unit gathers information from various calendars and communication tools. For example, the data collection unit obtains schedule information from the calendar service and messaging app used by the user. The data collection unit can obtain data using APIs. The data collection unit can also gather information using scraping techniques. Step 2: The scheduling unit adjusts the schedule based on the information collected by the collection unit. For example, it adjusts schedules with family and colleagues to find common free time. The scheduling unit adjusts the schedule using methods for detecting free time and setting priorities. For example, the scheduling unit detects free time from the user's calendar and suggests the best schedule. The scheduling unit can also set priorities to prioritize important appointments. Step 3: The Negotiation Department conducts discount negotiations based on the schedule set up by the Coordination Department. For example, they negotiate reservations and discounts with restaurants, hair salons, hotels, etc. The Negotiation Department conducts discount negotiations using negotiation processes and techniques. For example, the Negotiation Department negotiates with restaurants to provide discount coupons. The Negotiation Department can also negotiate with hair salons for discounts on reservations. Step 4: The reservations department makes the reservation, applying the discount negotiated by the negotiation department. For example, the user makes a reservation for their desired date and time and obtains a discount coupon. The reservations department makes the reservation using the type of reservation system and reservation procedure. For example, the reservations department makes reservations using an online reservation system. The reservations department can also make reservations using a telephone reservation system.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, adjustment unit, negotiation unit, reservation unit, weather consideration unit, availability consideration unit, and promotion proposal unit, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information from various calendars and communication tools. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and adjusts the schedule based on the collected information. The negotiation unit is implemented by, for example, the control unit 46A of the smart device 14 and negotiates discounts with restaurants, hair salons, hotels, etc. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and makes reservations by applying the negotiated discounts. The weather consideration unit is implemented by, for example, the control unit 46A of the smart device 14 and adjusts the schedule considering weather data. The availability consideration unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and adjusts the schedule considering the user's schedule availability. The promotion proposal unit is implemented, for example, by the control unit 46A of the smart device 14, and proposes effective promotions to the business side. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the data collection unit, adjustment unit, negotiation unit, reservation unit, weather consideration unit, availability consideration unit, and promotion proposal unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information from various calendars and communication tools. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts the schedule based on the collected information. The negotiation unit is implemented by, for example, the control unit 46A of the smart glasses 214 and negotiates discounts with restaurants, hair salons, hotels, etc. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes reservations by applying the negotiated discounts. The weather consideration unit is implemented by, for example, the control unit 46A of the smart glasses 214 and adjusts the schedule considering weather data. The availability consideration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts the schedule considering the user's schedule availability. The promotion proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214, and proposes effective promotions to the business side. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the collection unit, adjustment unit, negotiation unit, reservation unit, weather consideration unit, availability consideration unit, and promotion proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information from various calendars and communication tools. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts the schedule based on the collected information. The negotiation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and negotiates discounts with restaurants, hair salons, hotels, etc. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes reservations by applying the negotiated discounts. The weather consideration unit is implemented by, for example, the control unit 46A of the headset terminal 314 and adjusts the schedule considering weather data. The availability consideration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts the schedule considering the user's schedule availability. The promotion proposal unit is implemented, for example, by the control unit 46A of the headset terminal 314, and proposes effective promotions to the business side. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the collection unit, adjustment unit, negotiation unit, reservation unit, weather consideration unit, availability consideration unit, and promotion proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information from various calendars and communication tools. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts the schedule based on the collected information. The negotiation unit is implemented by, for example, the control unit 46A of the robot 414 and negotiates discounts with restaurants, hair salons, hotels, etc. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes reservations by applying the negotiated discounts. The weather consideration unit is implemented by, for example, the control unit 46A of the robot 414 and adjusts the schedule considering weather data. The availability consideration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts the schedule considering the user's schedule availability. The promotion proposal unit is implemented, for example, by the control unit 46A of robot 414, and proposes effective promotions to the business side. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) The collection department collects information from various calendars and communication tools, An adjustment unit adjusts the schedule based on the information collected by the aforementioned collection unit, A negotiation unit conducts discount negotiations based on the schedule adjusted by the aforementioned adjustment unit, The system includes a reservation department that makes reservations by applying the discount negotiated by the aforementioned negotiation department. A system characterized by the following features. (Note 2) Equipped with a weather data consideration unit. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a section that considers the user's schedule availability. The system described in Appendix 1, characterized by the features described herein. (Note 4) We have a promotion proposal department that proposes effective promotions to businesses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Retrieve schedule information from the calendar service or messaging app the user is using. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, Coordinate schedules with family and colleagues and find common free time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned negotiating body said, Making reservations and negotiating discounts with restaurants, hair salons, hotels, etc. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reservation section is, Users make reservations for their desired date and time and obtain discount coupons. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of acquiring schedule information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past schedule history and select the optimal method for obtaining it. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When retrieving schedule information, 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 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of schedule information to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When retrieving schedule information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When retrieving schedule information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The adjustment unit is, The system estimates the user's emotions and adjusts the way scheduling is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The adjustment unit is, When scheduling, adjust the level of detail based on the importance of each schedule item. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, When scheduling, different scheduling algorithms are applied depending on the schedule category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, It estimates the user's emotions and adjusts the length of the schedule adjustment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, When scheduling, prioritize adjustments based on the submission date of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, When scheduling, adjust the order of adjustments based on the relevance of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned negotiating body said, It estimates the user's emotions and adjusts negotiation criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned negotiating body said, During negotiations, referencing past negotiation history improves the accuracy of the negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned negotiating body said, When negotiating, take into account the attributes of the other party. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the order in which negotiation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned negotiating body said, When negotiating, take into account the geographical distribution of the negotiating party. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned negotiating body said, During negotiations, referencing relevant literature on the negotiating partner can improve the accuracy of the negotiation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, It estimates the user's emotions and adjusts the booking method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reservation section is, When making a reservation, the system will refer to your past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation section is, When making a reservation, the reservation method will be customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reservation section is, When making a reservation, the system will select the most suitable reservation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reservation section is, When making a reservation, we analyze the user's social media activity and suggest a reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned weather consideration unit is, We estimate the user's emotions and adjust how weather data is considered based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned weather consideration unit is, When considering weather data, we refer to past weather data to predict the current weather. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned weather consideration unit is, It estimates the user's sentiment and adjusts the importance of weather data based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned weather consideration unit is, When considering weather data, the system retrieves weather data while taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned availability consideration unit is: The system estimates the user's emotions and adjusts how availability is considered based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned availability consideration unit is: When considering availability, we refer to past availability data to predict current availability. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned availability consideration unit is: The system estimates the user's emotions and adjusts the importance of availability based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned availability consideration unit is: When considering availability, we obtain availability data while taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned promotion proposal department, We estimate user sentiment and adjust promotional suggestions based on that estimated sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned promotion proposal department, When proposing a promotion, we will refer to past promotion history to select the most suitable proposal method. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned promotion proposal department, It estimates user sentiment and prioritizes promotional proposals based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned promotion proposal department, When proposing a promotion, the most suitable proposal method is selected by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0205] 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 collection department collects information from various calendars and communication tools, An adjustment unit adjusts the schedule based on the information collected by the aforementioned collection unit, A negotiation unit conducts discount negotiations based on the schedule adjusted by the aforementioned adjustment unit, The system includes a reservation department that makes reservations by applying the discount negotiated by the aforementioned negotiation department. A system characterized by the following features.

2. Equipped with a weather data consideration unit. The system according to feature 1.

3. It includes a section that considers the user's schedule availability. The system according to feature 1.

4. We have a promotion proposal department that proposes effective promotions to businesses. The system according to feature 1.

5. The aforementioned collection unit is Retrieve schedule information from the calendar service or messaging app the user is using. The system according to feature 1.

6. The adjustment unit is, Coordinate schedules with family and colleagues and find common free time. The system according to feature 1.

7. The aforementioned negotiating body said, Making reservations and negotiating discounts with restaurants, hair salons, hotels, etc. The system according to feature 1.

8. The aforementioned reservation section is, Users make reservations for their desired date and time and obtain discount coupons. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of acquiring schedule information based on the estimated emotions. The system according to feature 1.

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