system

Abstract

The system according to the embodiment aims to efficiently manage and adjust the schedules of all family members. [Solution] A system according to an embodiment includes an input unit, an analysis unit, a reminder unit, and an adjustment unit. The input unit inputs family schedules and plans. The analysis unit analyzes the schedules and plans input by the input unit. The reminder unit reminds of events and tasks based on the information analyzed by the analysis unit. The adjustment unit assists in adjusting the schedules of all family members based on the information reminded by the reminder unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to efficiently manage and coordinate the schedules of all family members.

[0005] The system according to the embodiment aims to efficiently manage and adjust the schedules of all family members. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a reminder unit, and an adjustment unit. The input unit inputs schedules and plans of family members. The analysis unit analyzes the schedules and plans input by the input unit. The reminder unit reminds family members of events and tasks based on the information analyzed by the analysis unit. The adjustment unit supports schedule adjustment for all family members based on the information reminded by the reminder unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage and coordinate the schedules of all family members. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In an embodiment of the present invention, a family communication system is a system in which all family members use an app and a generation AI coordinates the family's schedules and plans. In this family communication system, all family members enter their own schedules and plans into the app, and the generation AI analyzes these schedules and plans and automatically organizes the schedules of all family members. The generation AI reminds family members of important events and tasks, facilitating communication within the family. The generation AI also assists in coordinating the schedules of all family members. For example, all family members enter their own schedules and plans into the app. For example, they may enter information such as children's school events, parents' work schedules, and travel plans for the whole family. This information is then input into the generation AI. The generation AI then analyzes the input information and automatically organizes the schedules of all family members. The generation AI compares each member's schedule and checks for overlaps or inconsistencies. For example, if a child's school event overlaps with a parent's work schedule, the generation AI notifies the family member. The generation AI also reminds family members of important events and tasks. For example, the day before a family trip, the generation AI may remind the family, "Tomorrow is travel day." The Generative AI also provides each family member with the information they need to facilitate smooth communication within the family. For example, it notifies parents of details about their children's school events. Furthermore, the Generative AI assists in coordinating schedules for the entire family. For example, when planning a family trip, the Generative AI considers each member's schedule and suggests the optimal dates. In this way, the schedules of all family members are coordinated, ensuring smooth communication. This system allows all family members to share their schedules through the app, ensuring smooth communication without forgetting important events or tasks. Furthermore, the Generative AI's assistance in schedule coordination ensures efficient management of all family members' schedules. This allows the Family Communication System to efficiently manage the schedules of all family members, reminding them of important events and tasks, and assisting in schedule coordination.

[0029] The family communication system according to the embodiment includes an input unit, an analysis unit, a reminder unit, and an adjustment unit. The input unit inputs schedules and plans of all family members. The schedules and plans of all family members include, but are not limited to, school events, work schedules, and family events. The input unit allows, for example, all family members to input their own schedules and plans into the app. The input unit can also support multiple input methods, such as voice input and text input. For example, the input unit can convert schedules dictated by family members into text data using voice recognition technology. The analysis unit analyzes the schedules and plans input by the input unit. For example, the analysis unit compares the schedules of each family member to check for overlaps or inconsistencies. For example, the analysis unit notifies a parent if a child's school event overlaps with a parent's work schedule. The analysis unit can also analyze each family member's schedule to determine schedule priorities. For example, the analysis unit prioritizes important events and tasks. The reminder unit reminds the parent of important events and tasks based on the information analyzed by the analysis unit. For example, the reminding unit may remind each family member, for example, "Tomorrow is travel day," the day before the family travels together. The reminding unit may also provide necessary information to each family member to facilitate communication within the family. For example, the reminding unit may notify parents of details of their children's school events. The coordination unit may support schedule coordination for all family members based on the information reminded by the reminding unit. For example, when planning a family trip, the coordination unit may propose an optimal itinerary taking into account each family member's schedule. For example, the coordination unit may propose a travel itinerary taking into account each family member's free time. As a result, the family communication system according to the embodiment can efficiently manage the schedules of all family members, remind them of important events and tasks, and support schedule coordination.

[0030] The input unit can input the schedules and plans of all family members. The schedules and plans of all family members include, but are not limited to, school events, work schedules, and family events. For example, the input unit can allow all family members to input their own schedules and plans into the app. The input unit can also support multiple input methods, such as voice input and text input. For example, the input unit can use voice recognition technology to convert schedules dictated by family members into text data. This allows for centralized management of the schedules and plans of all family members. Some or all of the above-described processing in the input unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the input unit can input schedule data entered by family members into a generation AI, which can analyze the data and suggest the optimal input method.

[0031] The analysis unit can analyze the input schedules and plans and check for overlaps and inconsistencies. For example, the analysis unit compares each member's schedule to check for overlaps and inconsistencies. For example, the analysis unit notifies a parent if a child's school event overlaps with a parent's work schedule. The analysis unit can also analyze each member's schedule to determine schedule priorities. For example, the analysis unit prioritizes important events and tasks. This enables automatic detection and adjustment of schedule overlaps and inconsistencies. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the input schedule data into a generation AI, which then analyzes the data to detect overlaps and inconsistencies.

[0032] The reminder unit can remind users of important events and tasks. For example, the day before a family trip, the reminder unit may remind them that "tomorrow is travel day." The reminder unit can also provide necessary information to each family member to facilitate communication within the family. For example, the reminder unit may notify parents of details of their children's school events. This allows them to be reminded of important events and tasks without forgetting them. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reminder unit can remind users of important events and tasks based on information analyzed by a generation AI.

[0033] The reminder unit can provide information to facilitate communication within the family. For example, the reminder unit provides each member with necessary information to facilitate communication within the family. For example, the reminder unit notifies parents of details of their child's school events. The reminder unit can also provide memos and chat functions that can be shared by all family members. For example, the reminder unit creates shared memos that all family members can access and records important information. The reminder unit can also provide a chat function that allows all family members to communicate in real time. This facilitates communication within the family. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the reminder unit can provide information to facilitate communication within the family based on information analyzed by the generation AI.

[0034] The adjustment unit can adjust the schedules of all family members and propose itineraries. For example, when planning a trip for the entire family, the adjustment unit can propose an optimal itinerary taking into account the schedules of each member. For example, the adjustment unit can propose a travel itinerary taking into account the free time of each member. The adjustment unit can also use an algorithm for adjusting the schedules of all family members. For example, the adjustment unit can use a generation AI to analyze the schedules of each member and propose an optimal itinerary. This allows for efficient schedule adjustment for all family members. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can adjust the schedules of all family members and propose an optimal itinerary based on information analyzed by the generation AI.

[0035] The input unit can analyze past schedule input history and select an input method. For example, the input unit uses a generation AI to analyze input methods (such as voice input or text input) frequently used by family members in the past and propose an optimal input method. The input unit can also use a generation AI to analyze schedule patterns previously entered by family members and propose an efficient input method. Furthermore, the input unit can also analyze time periods previously entered by family members and propose optimal input timings. This makes it possible to propose an optimal input method based on past history. Some or all of the above-described processing in the input unit may be performed using, or without, the generation AI. For example, the input unit can input past schedule input history into the generation AI, which then analyzes the data and selects the optimal input method.

[0036] The input unit can filter the schedule based on the family member's current living situation and areas of interest when inputting the schedule. For example, the input unit allows the generation AI to propose relevant schedules based on the family member's current living situation (work, school, hobbies, etc.). The input unit can also allow the generation AI to propose relevant events and tasks based on the family member's areas of interest (sports, music, travel, etc.). Furthermore, the input unit can allow the generation AI to adjust the schedule priorities based on the family member's current living situation and areas of interest. This makes it possible to propose relevant schedules based on the family member's living situation and areas of interest. Some or all of the above-described processing in the input unit can be performed using, or without, the generation AI. For example, the input unit can input data on the family member's current living situation and areas of interest to the generation AI, and the generation AI can analyze the data and perform filtering.

[0037] When inputting a schedule, the input unit can prioritize inputting highly relevant schedules taking into account the geographical location information of family members. For example, the input unit can prioritize inputting events close to the current location of family members. The input unit can also prioritize inputting schedules related to places that family members often visit. Furthermore, the input unit can also prioritize inputting events that family members can participate in while traveling. This makes it possible to prioritize inputting highly relevant schedules based on geographical location information. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI, for example. For example, the input unit can input geographical location information of family members to the generation AI, which can analyze the data and prioritize inputting highly relevant schedules.

[0038] The input unit can analyze the social media activities of family members when inputting a schedule and input a related schedule. For example, the input unit inputs events that family members have shown interest in on social media into the schedule. The input unit can also input plans that family members have shared on social media into the schedule. Furthermore, the input unit can also input events of accounts that family members follow on social media into the schedule. This makes it possible to input a related schedule based on social media activities. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI, for example. For example, the input unit can input social media activity data of family members into the generation AI, which then analyzes the data and inputs a related schedule.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the schedule. For example, the analysis unit allows the generation AI to perform a detailed analysis of important events or tasks. The analysis unit can also allow the generation AI to perform a simplified analysis of low-priority schedules. Furthermore, the analysis unit can allow the generation AI to perform a detailed analysis of schedules that affect all family members. This makes it possible to adjust the level of detail of the analysis depending on the importance of the schedule. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input schedule importance data to the generation AI, which then analyzes the data and adjusts the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the schedule. For example, the analysis unit allows the generation AI to apply an efficient analysis algorithm to work-related schedules. The analysis unit can also allow the generation AI to apply an educational analysis algorithm to school-related schedules. Furthermore, the analysis unit can allow the generation AI to apply a communication-focused analysis algorithm to family events. This allows an appropriate analysis algorithm to be applied depending on the category of the schedule. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input schedule category data into the generation AI, which then analyzes the data and applies different analysis algorithms.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the schedule. For example, the analysis unit allows the generation AI to prioritize analysis of the most recent schedule. The analysis unit can also allow the generation AI to postpone analysis of long-term schedules. Furthermore, the analysis unit can also allow the generation AI to prioritize analysis of schedules whose submission deadline is approaching. This makes it possible to determine the priority of analysis based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input schedule submission time data into the generation AI, and the generation AI can analyze the data and determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of schedules. For example, the analysis unit allows the generation AI to prioritize analysis of highly relevant schedules. The analysis unit can also allow the generation AI to postpone analysis of less relevant schedules. Furthermore, the analysis unit can also allow the generation AI to prioritize analysis of schedules that affect all family members. This makes it possible to adjust the order of analysis based on the relevance of schedules. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the analysis unit can input schedule relevance data into the generation AI, which then analyzes the data and adjusts the order of analysis.

[0043] The reminding unit can adjust the level of detail of the reminder based on the importance of the event or task when reminding. For example, the generation AI of the reminding unit can provide a detailed reminder for important events or tasks. The reminding unit can also provide a simplified reminder for low-priority events or tasks. Furthermore, the reminding unit can also provide a detailed reminder for events or tasks that affect the entire family. This allows the level of detail of the reminder to be adjusted according to the importance of the event or task. Some or all of the above-mentioned processing in the reminding unit may be performed using, or without, the generation AI. For example, the reminding unit can input importance data of events or tasks into the generation AI, which can analyze the data and adjust the level of detail of the reminder.

[0044] The reminding unit can apply different reminding algorithms depending on the category of the event or task when reminding. For example, the generation AI of the reminding unit applies an efficient reminding algorithm to work-related events or tasks. The generation AI of the reminding unit can also apply an educational reminding algorithm to school-related events or tasks. Furthermore, the generation AI of the reminding unit can also apply a communication-focused reminding algorithm to family events. This allows an appropriate reminding algorithm to be applied depending on the category of the event or task. Some or all of the above-mentioned processing in the reminding unit may be performed using, or without, the generation AI. For example, the reminding unit can input event or task category data into the generation AI, which can analyze the data and apply different reminding algorithms.

[0045] The reminding unit can determine the priority of reminders based on the submission dates of events and tasks when reminding. For example, the reminding unit allows the generation AI to prioritize reminders for events and tasks whose submission deadlines are approaching. The reminding unit can also allow the generation AI to postpone reminders for long-term events and tasks. Furthermore, the reminding unit can also allow the generation AI to prioritize reminders for upcoming events and tasks. This makes it possible to determine the priority of reminders based on the submission dates. Some or all of the above-mentioned processing in the reminding unit may be performed using, or without, the generation AI. For example, the reminding unit can input submission date data for events and tasks into the generation AI, which can analyze the data and determine the priority of reminders.

[0046] The reminding unit can adjust the order of reminders based on the relevance of events and tasks when reminding. For example, the generation AI of the reminding unit prioritizes reminders for highly relevant events and tasks. The reminding unit can also have the generation AI postpone reminders for less relevant events and tasks. Furthermore, the reminding unit can also have the generation AI prioritize reminders for events and tasks that affect the entire family. This makes it possible to adjust the order of reminders based on the relevance of events and tasks. Some or all of the above-mentioned processing in the reminding unit may be performed using, or without, the generation AI. For example, the reminding unit can input relevance data for events and tasks into the generation AI, which can analyze the data and adjust the order of reminders.

[0047] When making adjustments, the adjustment unit can analyze the family member's past schedule history and select the optimal adjustment method. For example, the adjustment unit uses the generation AI to analyze schedule adjustment methods used by family members in the past and propose an optimal method. The adjustment unit can also use the generation AI to analyze schedule patterns adjusted by family members in the past and propose an efficient adjustment method. Furthermore, the adjustment unit can also analyze time periods in which family members have adjusted their schedules in the past and propose the optimal adjustment timing. This makes it possible to propose the optimal adjustment method based on the past schedule history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, the generation AI. For example, the adjustment unit can input the family member's past schedule history data into the generation AI, which then analyzes the data and selects the optimal adjustment method.

[0048] During adjustment, the adjustment unit can customize the adjustment means based on the family member's current living situation. For example, the adjustment unit allows the generation AI to suggest a relevant schedule adjustment method based on the family member's current living situation (work, school, hobbies, etc.). The adjustment unit can also allow the generation AI to adjust schedule priorities based on the family member's current living situation. Furthermore, the adjustment unit can also allow the generation AI to customize the schedule adjustment means based on the family member's current living situation. This allows the adjustment means to be customized based on the current living situation. Some or all of the above-described processing in the adjustment unit may be performed using, or without, the generation AI. For example, the adjustment unit can input data on the family member's current living situation into the generation AI, which can analyze the data and customize the adjustment means.

[0049] During adjustment, the adjustment unit can select an optimal adjustment method taking into consideration the geographical location information of family members. For example, the adjustment unit prioritizes adjusting events close to the current location of family members. The adjustment unit can also prioritize adjusting schedules related to places frequently visited by family members. Furthermore, the adjustment unit can also prioritize adjusting events that family members can participate in while traveling. This allows the optimal adjustment method to be selected based on the geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input geographical location information data of family members into the generation AI, which can analyze the data and select the optimal adjustment method.

[0050] During adjustment, the adjustment unit can analyze the social media activities of the family and suggest adjustment measures. For example, the adjustment unit adjusts events that family members have shown interest in on social media into the schedule. The adjustment unit can also adjust plans that family members have shared on social media into the schedule. Furthermore, the adjustment unit can also adjust events of accounts that family members follow on social media into the schedule. This makes it possible to suggest adjustment measures based on social media activities. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI, for example. For example, the adjustment unit can input social media activity data of family members into the generation AI, which then analyzes the data and suggests adjustment measures.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The family communication system can further include a health management unit. The health management unit collects health data of all family members and sends it to the analysis unit. For example, it can collect data such as each member's sleep time, amount of exercise, and dietary content. The analysis unit can evaluate each member's health status based on this data and provide necessary advice. For example, it can remind members who are not getting enough sleep to go to bed earlier. It can also send notifications encouraging members who are not getting enough exercise to exercise. This allows for efficient health management of all family members.

[0053] The family communication system may further include a learning support unit. The learning support unit manages a child's learning progress and sends it to the analysis unit. For example, it can collect data such as homework submission status and test scores. The analysis unit can evaluate a child's learning situation based on this data and provide necessary advice. For example, if homework submission is late, it can send a reminder. Also, if grades are declining, it can provide additional learning resources. This allows for efficient support of children's learning.

[0054] The family communication system can further include a household finance management unit. The household finance management unit collects income and expenditure data for all family members and sends it to the analysis unit. For example, it can collect data on the income and expenditure of each member. The analysis unit can evaluate the household finance situation based on this data and provide necessary advice. For example, if expenditure exceeds income, it can send advice on saving. Also, if savings targets have not been reached, it can send a notification encouraging savings. This allows for efficient household finance management.

[0055] The family communication system may further include a hobby sharing unit. The hobby sharing unit collects the hobbies and interests of all family members and sends the collected information to the analysis unit. For example, the hobby sharing unit may collect data on the hobbies and interests of each member. Based on this data, the analysis unit may suggest activities that can be enjoyed by all family members. For example, if all family members like movies, the analysis unit may suggest plans for a movie night. If all family members like the outdoors, the analysis unit may suggest plans for a camping trip. This allows all family members to enjoy their shared hobbies.

[0056] The family communication system may further include a pet management unit. The pet management unit collects health data of family pets and transmits it to the analysis unit. For example, it may collect data on pet diet, exercise, and health checkups. The analysis unit may evaluate the pet's health status based on this data and provide necessary advice. For example, if it is determined that the pet is not getting enough exercise, it may send a notification encouraging the pet to exercise. It may also suggest specific dietary recommendations based on the results of health checkups. This allows for efficient health management of pets.

[0057] The family communication system can further include a travel planning unit. The travel planning unit collects travel plans from all family members and sends them to the analysis unit. For example, it can collect data such as each member's desired travel destinations, budget, and schedule. The analysis unit can propose optimal travel plans based on this data. For example, it can propose optimal travel destinations taking into account everyone's desired destinations. It can also suggest accommodations and activities according to budget. This allows for efficient travel planning that satisfies all family members.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The input unit inputs the schedules and plans of all family members. These schedules and plans include school events, work schedules, and family events. The input unit allows all family members to input their own schedules and plans into the app, and supports multiple input methods, such as voice input and text input. For example, voice recognition technology can be used to convert schedules dictated by family members into text data. Step 2: The analysis unit analyzes the schedules and appointments entered by the input unit. The analysis unit compares each member's schedule to check for overlaps and inconsistencies and analyzes to determine schedule priorities. For example, if a child's school event overlaps with a parent's work schedule, the analysis unit notifies the user. It also prioritizes important events and tasks. Step 3: The reminder unit reminds the family of important events and tasks based on the information analyzed by the analysis unit. For example, the reminder unit may remind the family that "tomorrow is travel day" the day before the family goes on a trip. It also provides necessary information to each family member to facilitate communication within the family. For example, it may notify parents of details of their children's school events. Step 4: The coordination unit assists in adjusting schedules for all family members based on the information reminded by the reminding unit. For example, when planning a trip for the whole family, the coordination unit takes into account each member's schedule and proposes the best itinerary. It can propose travel dates taking into account each member's free time.

[0060] (Example 2) In an embodiment of the present invention, a family communication system is a system in which all family members use an app and a generation AI coordinates the family's schedules and plans. In this family communication system, all family members enter their own schedules and plans into the app, and the generation AI analyzes these schedules and plans and automatically organizes the schedules of all family members. The generation AI reminds family members of important events and tasks, facilitating communication within the family. The generation AI also assists in coordinating the schedules of all family members. For example, all family members enter their own schedules and plans into the app. For example, they may enter information such as children's school events, parents' work schedules, and travel plans for the whole family. This information is then input into the generation AI. The generation AI then analyzes the input information and automatically organizes the schedules of all family members. The generation AI compares each member's schedule and checks for overlaps or inconsistencies. For example, if a child's school event overlaps with a parent's work schedule, the generation AI notifies the family member. The generation AI also reminds family members of important events and tasks. For example, the day before a family trip, the generation AI may remind the family, "Tomorrow is travel day." The Generative AI also provides each family member with the information they need to facilitate smooth communication within the family. For example, it notifies parents of details about their children's school events. Furthermore, the Generative AI assists in coordinating schedules for the entire family. For example, when planning a family trip, the Generative AI considers each member's schedule and suggests the optimal dates. In this way, the schedules of all family members are coordinated, ensuring smooth communication. This system allows all family members to share their schedules through the app, ensuring smooth communication without forgetting important events or tasks. Furthermore, the Generative AI's assistance in schedule coordination ensures efficient management of all family members' schedules. This allows the Family Communication System to efficiently manage the schedules of all family members, reminding them of important events and tasks, and assisting in schedule coordination.

[0061] The family communication system according to the embodiment includes an input unit, an analysis unit, a reminder unit, and an adjustment unit. The input unit inputs schedules and plans of all family members. The schedules and plans of all family members include, but are not limited to, school events, work schedules, and family events. The input unit allows, for example, all family members to input their own schedules and plans into the app. The input unit can also support multiple input methods, such as voice input and text input. For example, the input unit can convert schedules dictated by family members into text data using voice recognition technology. The analysis unit analyzes the schedules and plans input by the input unit. For example, the analysis unit compares the schedules of each family member to check for overlaps or inconsistencies. For example, the analysis unit notifies a parent if a child's school event overlaps with a parent's work schedule. The analysis unit can also analyze each family member's schedule to determine schedule priorities. For example, the analysis unit prioritizes important events and tasks. The reminder unit reminds the parent of important events and tasks based on the information analyzed by the analysis unit. For example, the reminding unit may remind each family member, for example, "Tomorrow is travel day," the day before the family travels together. The reminding unit may also provide necessary information to each family member to facilitate communication within the family. For example, the reminding unit may notify parents of details of their children's school events. The coordination unit may support schedule coordination for all family members based on the information reminded by the reminding unit. For example, when planning a family trip, the coordination unit may propose an optimal itinerary taking into account each family member's schedule. For example, the coordination unit may propose a travel itinerary taking into account each family member's free time. As a result, the family communication system according to the embodiment can efficiently manage the schedules of all family members, remind them of important events and tasks, and support schedule coordination.

[0062] The input unit can input the schedules and plans of all family members. The schedules and plans of all family members include, but are not limited to, school events, work schedules, and family events. For example, the input unit can allow all family members to input their own schedules and plans into the app. The input unit can also support multiple input methods, such as voice input and text input. For example, the input unit can use voice recognition technology to convert schedules dictated by family members into text data. This allows for centralized management of the schedules and plans of all family members. Some or all of the above-described processing in the input unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the input unit can input schedule data entered by family members into a generation AI, which can analyze the data and suggest the optimal input method.

[0063] The analysis unit can analyze the input schedules and plans and check for overlaps and inconsistencies. For example, the analysis unit compares each member's schedule to check for overlaps and inconsistencies. For example, the analysis unit notifies a parent if a child's school event overlaps with a parent's work schedule. The analysis unit can also analyze each member's schedule to determine schedule priorities. For example, the analysis unit prioritizes important events and tasks. This enables automatic detection and adjustment of schedule overlaps and inconsistencies. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the input schedule data into a generation AI, which then analyzes the data to detect overlaps and inconsistencies.

[0064] The reminder unit can remind users of important events and tasks. For example, the day before a family trip, the reminder unit may remind them that "tomorrow is travel day." The reminder unit can also provide necessary information to each family member to facilitate communication within the family. For example, the reminder unit may notify parents of details of their children's school events. This allows them to be reminded of important events and tasks without forgetting them. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reminder unit can remind users of important events and tasks based on information analyzed by a generation AI.

[0065] The reminder unit can provide information to facilitate communication within the family. For example, the reminder unit provides each member with necessary information to facilitate communication within the family. For example, the reminder unit notifies parents of details of their child's school events. The reminder unit can also provide memos and chat functions that can be shared by all family members. For example, the reminder unit creates shared memos that all family members can access and records important information. The reminder unit can also provide a chat function that allows all family members to communicate in real time. This facilitates communication within the family. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the reminder unit can provide information to facilitate communication within the family based on information analyzed by the generation AI.

[0066] The adjustment unit can adjust the schedules of all family members and propose itineraries. For example, when planning a trip for the entire family, the adjustment unit can propose an optimal itinerary taking into account the schedules of each member. For example, the adjustment unit can propose a travel itinerary taking into account the free time of each member. The adjustment unit can also use an algorithm for adjusting the schedules of all family members. For example, the adjustment unit can use a generation AI to analyze the schedules of each member and propose an optimal itinerary. This allows for efficient schedule adjustment for all family members. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can adjust the schedules of all family members and propose an optimal itinerary based on information analyzed by the generation AI.

[0067] The input unit can estimate the emotions of family members and adjust the timing of schedule input based on the estimated emotions. For example, if a family member is feeling stressed, the input unit can cause the generation AI to delay the timing of schedule input so that the family member can input in a relaxed state. Furthermore, if a family member is busy, the input unit can also adjust the timing of schedule input so that the family member can input in a shorter time. Furthermore, if a family member is relaxed, the input unit can also advance the timing of schedule input so that the family member can input more detailed information. This allows the timing of schedule input to be adjusted according to the family member's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the input unit can be performed using, for example, the generation AI. For example, the input unit can input emotional data of family members into the generation AI, and the generation AI can analyze the emotions and adjust the timing of schedule input.

[0068] The input unit can analyze past schedule input history and select an input method. For example, the input unit uses a generation AI to analyze input methods (such as voice input or text input) frequently used by family members in the past and propose an optimal input method. The input unit can also use a generation AI to analyze schedule patterns previously entered by family members and propose an efficient input method. Furthermore, the input unit can also analyze time periods previously entered by family members and propose optimal input timings. This makes it possible to propose an optimal input method based on past history. Some or all of the above-described processing in the input unit may be performed using, or without, the generation AI. For example, the input unit can input past schedule input history into the generation AI, which then analyzes the data and selects the optimal input method.

[0069] The input unit can filter the schedule based on the family member's current living situation and areas of interest when inputting the schedule. For example, the input unit allows the generation AI to propose relevant schedules based on the family member's current living situation (work, school, hobbies, etc.). The input unit can also allow the generation AI to propose relevant events and tasks based on the family member's areas of interest (sports, music, travel, etc.). Furthermore, the input unit can allow the generation AI to adjust the schedule priorities based on the family member's current living situation and areas of interest. This makes it possible to propose relevant schedules based on the family member's living situation and areas of interest. Some or all of the above-described processing in the input unit can be performed using, or without, the generation AI. For example, the input unit can input data on the family member's current living situation and areas of interest to the generation AI, and the generation AI can analyze the data and perform filtering.

[0070] The input unit can estimate the emotions of family members and determine the priorities of schedules to be input based on the estimated emotions. For example, if a family member is feeling stressed, the input unit can cause the generation AI to prioritize events that will help them relax. Furthermore, if a family member is busy, the input unit can also cause the generation AI to prioritize input of important tasks. Furthermore, if a family member is relaxed, the input unit can cause the generation AI to prioritize input of long-term plans. This allows the schedule priorities to be determined according to the family members' emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the input unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the input unit can input emotional data of family members to the generation AI, which can then analyze the emotions and determine the schedule priorities.

[0071] When inputting a schedule, the input unit can prioritize inputting highly relevant schedules taking into account the geographical location information of family members. For example, the input unit can prioritize inputting events close to the current location of family members. The input unit can also prioritize inputting schedules related to places that family members often visit. Furthermore, the input unit can also prioritize inputting events that family members can participate in while traveling. This makes it possible to prioritize inputting highly relevant schedules based on geographical location information. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI, for example. For example, the input unit can input geographical location information of family members to the generation AI, which can analyze the data and prioritize inputting highly relevant schedules.

[0072] The input unit can analyze the social media activities of family members when inputting a schedule and input a related schedule. For example, the input unit inputs events that family members have shown interest in on social media into the schedule. The input unit can also input plans that family members have shared on social media into the schedule. Furthermore, the input unit can also input events of accounts that family members follow on social media into the schedule. This makes it possible to input a related schedule based on social media activities. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI, for example. For example, the input unit can input social media activity data of family members into the generation AI, which then analyzes the data and inputs a related schedule.

[0073] The analysis unit can estimate the emotions of family members and adjust the way the analysis is presented based on the estimated emotions. For example, if a family member is feeling stressed, the generation AI can provide a simple analysis result. Furthermore, if a family member is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if a family member is busy, the generation AI can provide a summary analysis result. This allows the way the analysis is presented to be adjusted according to the emotions of the family members. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input emotional data of family members into the generation AI, which can analyze the emotions and adjust the way the analysis is presented.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the schedule. For example, the analysis unit allows the generation AI to perform a detailed analysis of important events or tasks. The analysis unit can also allow the generation AI to perform a simplified analysis of low-priority schedules. Furthermore, the analysis unit can allow the generation AI to perform a detailed analysis of schedules that affect all family members. This makes it possible to adjust the level of detail of the analysis depending on the importance of the schedule. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input schedule importance data to the generation AI, which then analyzes the data and adjusts the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the schedule. For example, the analysis unit allows the generation AI to apply an efficient analysis algorithm to work-related schedules. The analysis unit can also allow the generation AI to apply an educational analysis algorithm to school-related schedules. Furthermore, the analysis unit can allow the generation AI to apply a communication-focused analysis algorithm to family events. This allows an appropriate analysis algorithm to be applied depending on the category of the schedule. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input schedule category data into the generation AI, which then analyzes the data and applies different analysis algorithms.

[0076] The analysis unit can estimate the emotions of family members and adjust the length of the analysis based on the estimated emotions. For example, if a family member is stressed, the generation AI can provide a short analysis result. Furthermore, if a family member is relaxed, the analysis unit can provide a long analysis result. Furthermore, if a family member is busy, the generation AI can provide a short analysis result that focuses on the main points. This allows the length of the analysis to be adjusted according to the emotions of the family members. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input emotional data of family members into the generation AI, which can analyze the emotions and adjust the length of the analysis.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the schedule. For example, the analysis unit allows the generation AI to prioritize analysis of the most recent schedule. The analysis unit can also allow the generation AI to postpone analysis of long-term schedules. Furthermore, the analysis unit can also allow the generation AI to prioritize analysis of schedules whose submission deadline is approaching. This makes it possible to determine the priority of analysis based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input schedule submission time data into the generation AI, and the generation AI can analyze the data and determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of schedules. For example, the analysis unit allows the generation AI to prioritize analysis of highly relevant schedules. The analysis unit can also allow the generation AI to postpone analysis of less relevant schedules. Furthermore, the analysis unit can also allow the generation AI to prioritize analysis of schedules that affect all family members. This makes it possible to adjust the order of analysis based on the relevance of schedules. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the analysis unit can input schedule relevance data into the generation AI, which then analyzes the data and adjusts the order of analysis.

[0079] The reminder unit can estimate the emotions of family members and adjust the reminder method based on the estimated emotions. For example, if a family member is stressed, the generation AI can provide a gentle reminder method. Furthermore, if a family member is relaxed, the generation AI can provide a detailed reminder method. Furthermore, if a family member is busy, the generation AI can provide a reminder method that focuses on the main points. This allows the reminder method to be adjusted according to the emotions of the family members. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reminder unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reminder unit can input emotional data of family members into the generation AI, which can analyze the emotions and adjust the reminder method.

[0080] The reminding unit can adjust the level of detail of the reminder based on the importance of the event or task when reminding. For example, the generation AI of the reminding unit can provide a detailed reminder for important events or tasks. The reminding unit can also provide a simplified reminder for low-priority events or tasks. Furthermore, the reminding unit can also provide a detailed reminder for events or tasks that affect the entire family. This allows the level of detail of the reminder to be adjusted according to the importance of the event or task. Some or all of the above-mentioned processing in the reminding unit may be performed using, or without, the generation AI. For example, the reminding unit can input importance data of events or tasks into the generation AI, which can analyze the data and adjust the level of detail of the reminder.

[0081] The reminding unit can apply different reminding algorithms depending on the category of the event or task when reminding. For example, the generation AI of the reminding unit applies an efficient reminding algorithm to work-related events or tasks. The generation AI of the reminding unit can also apply an educational reminding algorithm to school-related events or tasks. Furthermore, the generation AI of the reminding unit can also apply a communication-focused reminding algorithm to family events. This allows an appropriate reminding algorithm to be applied depending on the category of the event or task. Some or all of the above-mentioned processing in the reminding unit may be performed using, or without, the generation AI. For example, the reminding unit can input event or task category data into the generation AI, which can analyze the data and apply different reminding algorithms.

[0082] The reminder unit can estimate the emotions of family members and adjust the timing of reminders based on the estimated emotions. For example, if a family member is feeling stressed, the generation AI of the reminder unit can delay the timing of reminders. Furthermore, if a family member is relaxed, the generation AI of the reminder unit can also advance the timing of reminders. Furthermore, if a family member is busy, the generation AI of the reminder unit can adjust the timing of reminders and send reminders in a shorter time. This allows the timing of reminders to be adjusted according to the emotions of family members. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reminder unit can be performed using, for example, the generation AI. For example, the reminder unit can input emotional data of family members into the generation AI, which can analyze the emotions and adjust the timing of reminders.

[0083] The reminding unit can determine the priority of reminders based on the submission dates of events and tasks when reminding. For example, the reminding unit allows the generation AI to prioritize reminders for events and tasks whose submission deadlines are approaching. The reminding unit can also allow the generation AI to postpone reminders for long-term events and tasks. Furthermore, the reminding unit can also allow the generation AI to prioritize reminders for upcoming events and tasks. This makes it possible to determine the priority of reminders based on the submission dates. Some or all of the above-mentioned processing in the reminding unit may be performed using, or without, the generation AI. For example, the reminding unit can input submission date data for events and tasks into the generation AI, which can analyze the data and determine the priority of reminders.

[0084] The reminding unit can adjust the order of reminders based on the relevance of events and tasks when reminding. For example, the generation AI of the reminding unit prioritizes reminders for highly relevant events and tasks. The reminding unit can also have the generation AI postpone reminders for less relevant events and tasks. Furthermore, the reminding unit can also have the generation AI prioritize reminders for events and tasks that affect the entire family. This makes it possible to adjust the order of reminders based on the relevance of events and tasks. Some or all of the above-mentioned processing in the reminding unit may be performed using, or without, the generation AI. For example, the reminding unit can input relevance data for events and tasks into the generation AI, which can analyze the data and adjust the order of reminders.

[0085] The adjustment unit can estimate the emotions of family members and adjust the schedule adjustment method based on the estimated emotions. For example, if a family member is feeling stressed, the adjustment unit can have the generation AI suggest a schedule adjustment method that allows the family member to relax. Furthermore, if a family member is relaxed, the adjustment unit can have the generation AI suggest a detailed schedule adjustment method. Furthermore, if a family member is busy, the adjustment unit can have the generation AI suggest an efficient schedule adjustment method. This allows the schedule adjustment method to be adjusted according to the family member's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the adjustment unit can be performed using, for example, the generation AI, or without the generation AI. For example, the adjustment unit can input emotional data of family members into the generation AI, which can analyze the emotions and adjust the schedule adjustment method.

[0086] When making adjustments, the adjustment unit can analyze the family member's past schedule history and select the optimal adjustment method. For example, the adjustment unit uses the generation AI to analyze schedule adjustment methods used by family members in the past and propose an optimal method. The adjustment unit can also use the generation AI to analyze schedule patterns adjusted by family members in the past and propose an efficient adjustment method. Furthermore, the adjustment unit can also analyze time periods in which family members have adjusted their schedules in the past and propose the optimal adjustment timing. This makes it possible to propose the optimal adjustment method based on the past schedule history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, the generation AI. For example, the adjustment unit can input the family member's past schedule history data into the generation AI, which then analyzes the data and selects the optimal adjustment method.

[0087] During adjustment, the adjustment unit can customize the adjustment means based on the family member's current living situation. For example, the adjustment unit allows the generation AI to suggest a relevant schedule adjustment method based on the family member's current living situation (work, school, hobbies, etc.). The adjustment unit can also allow the generation AI to adjust schedule priorities based on the family member's current living situation. Furthermore, the adjustment unit can also allow the generation AI to customize the schedule adjustment means based on the family member's current living situation. This allows the adjustment means to be customized based on the current living situation. Some or all of the above-described processing in the adjustment unit may be performed using, or without, the generation AI. For example, the adjustment unit can input data on the family member's current living situation into the generation AI, which can analyze the data and customize the adjustment means.

[0088] The adjustment unit can estimate the emotions of family members and determine priorities for schedule adjustment based on the estimated emotions. For example, if a family member is feeling stressed, the adjustment unit can cause the generation AI to prioritize relaxing events. Furthermore, if a family member is busy, the adjustment unit can cause the generation AI to prioritize important tasks. Furthermore, if a family member is relaxed, the adjustment unit can cause the generation AI to prioritize long-term plans. This allows the priorities for schedule adjustment to be determined according to the emotions of the family members. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the adjustment unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the adjustment unit can input emotional data of family members into the generation AI, which can analyze the emotions and determine the priorities for schedule adjustment.

[0089] During adjustment, the adjustment unit can select an optimal adjustment method taking into consideration the geographical location information of family members. For example, the adjustment unit prioritizes adjusting events close to the current location of family members. The adjustment unit can also prioritize adjusting schedules related to places frequently visited by family members. Furthermore, the adjustment unit can also prioritize adjusting events that family members can participate in while traveling. This allows the optimal adjustment method to be selected based on the geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input geographical location information data of family members into the generation AI, which can analyze the data and select the optimal adjustment method.

[0090] During adjustment, the adjustment unit can analyze the social media activities of the family and suggest adjustment measures. For example, the adjustment unit adjusts events that family members have shown interest in on social media into the schedule. The adjustment unit can also adjust plans that family members have shared on social media into the schedule. Furthermore, the adjustment unit can also adjust events of accounts that family members follow on social media into the schedule. This makes it possible to suggest adjustment measures based on social media activities. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI, for example. For example, the adjustment unit can input social media activity data of family members into the generation AI, which then analyzes the data and suggests adjustment measures. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, reminding unit, and adjustment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input schedules and plans for all family members using the reception device 38 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input schedules and plans. The reminding unit reminds of important events and tasks using, for example, the output device 40 of the smart device 14. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports schedule adjustment for all family members. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, remind unit, and adjustment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input schedules and plans for all family members using the microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input schedules and plans. The remind unit reminds of important events and tasks using, for example, the speaker 240 of the smart glasses 214. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports schedule adjustment for all family members. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, reminder unit, and adjustment unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit can input schedules and plans for all family members using the microphone 238 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input schedules and plans. The reminder unit reminds of important events and tasks using, for example, the speaker 240 of the headset terminal 314. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports schedule adjustment for all family members. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, remind unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input schedules and plans of all family members using the microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input schedules and plans. The remind unit reminds of important events and tasks using, for example, the speaker 240 of the robot 414. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports schedule adjustment of all family members.

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

[0092] The family communication system can further include a health management unit. The health management unit collects health data of all family members and sends it to the analysis unit. For example, it can collect data such as each member's sleep time, amount of exercise, and dietary content. The analysis unit can evaluate each member's health status based on this data and provide necessary advice. For example, it can remind members who are not getting enough sleep to go to bed earlier. It can also send notifications encouraging members who are not getting enough exercise to exercise. This allows for efficient health management of all family members.

[0093] The family communication system may further include a learning support unit. The learning support unit manages a child's learning progress and sends it to the analysis unit. For example, it can collect data such as homework submission status and test scores. The analysis unit can evaluate a child's learning situation based on this data and provide necessary advice. For example, if homework submission is late, it can send a reminder. Also, if grades are declining, it can provide additional learning resources. This allows for efficient support of children's learning.

[0094] The family communication system can further include a household finance management unit. The household finance management unit collects income and expenditure data for all family members and sends it to the analysis unit. For example, it can collect data on the income and expenditure of each member. The analysis unit can evaluate the household finance situation based on this data and provide necessary advice. For example, if expenditure exceeds income, it can send advice on saving. Also, if savings targets have not been reached, it can send a notification encouraging savings. This allows for efficient household finance management.

[0095] The family communication system may further include a hobby sharing unit. The hobby sharing unit collects the hobbies and interests of all family members and sends the collected information to the analysis unit. For example, the hobby sharing unit may collect data on the hobbies and interests of each member. Based on this data, the analysis unit may suggest activities that can be enjoyed by all family members. For example, if all family members like movies, the analysis unit may suggest plans for a movie night. If all family members like the outdoors, the analysis unit may suggest plans for a camping trip. This allows all family members to enjoy their shared hobbies.

[0096] The family communication system may further include an emotion sharing unit. The emotion sharing unit collects emotional data from all family members and transmits it to the analysis unit. For example, it can collect the emotional state of each member. The analysis unit can evaluate the emotional state of all family members based on this data and provide necessary advice. For example, it can suggest relaxation activities to a member who is feeling stressed. It can also provide counseling resources to a member who is emotionally unstable. This allows for efficient emotional management for all family members.

[0097] The family communication system may further include a pet management unit. The pet management unit collects health data of family pets and transmits it to the analysis unit. For example, it may collect data on pet diet, exercise, and health checkups. The analysis unit may evaluate the pet's health status based on this data and provide necessary advice. For example, if it is determined that the pet is not getting enough exercise, it may send a notification encouraging the pet to exercise. It may also suggest specific dietary recommendations based on the results of health checkups. This allows for efficient health management of pets.

[0098] The family communication system can further include a travel planning unit. The travel planning unit collects travel plans from all family members and sends them to the analysis unit. For example, it can collect data such as each member's desired travel destinations, budget, and schedule. The analysis unit can propose optimal travel plans based on this data. For example, it can propose optimal travel destinations taking into account everyone's desired destinations. It can also suggest accommodations and activities according to budget. This allows for efficient travel planning that satisfies all family members.

[0099] The family communication system can also use the emotion estimation function to adjust schedule priorities based on the emotional state of each family member. For example, it can prioritize relaxing events for stressed family members, and prioritize important tasks for emotionally stable family members. Furthermore, it can provide counseling and relaxation resources to emotionally unstable family members. This allows for efficient schedule adjustments based on the emotional state of each family member.

[0100] The family communication system can further use the emotion estimation function to adjust the reminder method based on the emotional state of each family member. For example, a gentle reminder method can be provided to a member who is feeling stressed. A detailed reminder method can be provided to a member who is emotionally stable. Furthermore, a reminder method that focuses on the main points can be provided to a member who is emotionally unstable. This allows for efficient reminders that suit the emotional state of each family member.

[0101] The family communication system can also use the emotion estimation function to adjust the schedule input method based on the emotional state of each family member. For example, a simple input method can be provided for a stressed member, while a more detailed input method can be provided for an emotionally stable member. Furthermore, a relaxing input method such as voice input can be provided for an emotionally unstable member. This allows for efficient schedule input according to the emotional state of each family member.

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

[0103] Step 1: The input unit inputs the schedules and plans of all family members. These schedules and plans include school events, work schedules, and family events. The input unit allows all family members to input their own schedules and plans into the app, and supports multiple input methods, such as voice input and text input. For example, voice recognition technology can be used to convert schedules dictated by family members into text data. Step 2: The analysis unit analyzes the schedules and appointments entered by the input unit. The analysis unit compares each member's schedule to check for overlaps and inconsistencies and analyzes to determine schedule priorities. For example, if a child's school event overlaps with a parent's work schedule, the analysis unit notifies the user. It also prioritizes important events and tasks. Step 3: The reminder unit reminds the family of important events and tasks based on the information analyzed by the analysis unit. For example, the reminder unit may remind the family that "tomorrow is travel day" the day before the family goes on a trip. It also provides necessary information to each family member to facilitate communication within the family. For example, it may notify parents of details of their children's school events. Step 4: The coordination unit assists in adjusting schedules for all family members based on the information reminded by the reminding unit. For example, when planning a trip for the whole family, the coordination unit takes into account each member's schedule and proposes the best itinerary. It can propose travel dates taking into account each member's free time.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

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

Claims

1. An input section for entering family schedules and plans; an analysis unit that analyzes the schedule or plan input by the input unit; a reminding unit that reminds the user of an event or task based on the information analyzed by the analyzing unit; and an adjustment unit that assists in adjusting the schedules of all family members based on the information reminded by the reminding unit. A system characterized by:

2. The input unit Enter schedules and events for the whole family 2. The system of claim 1.

3. The analysis unit Analyze entered schedules and appointments to check for overlaps and inconsistencies 2. The system of claim 1.

4. The reminding unit Remind yourself of important events and tasks 2. The system of claim 1.

5. The reminding unit Providing information to facilitate communication within the family 2. The system of claim 1.

6. The adjustment unit Coordinate schedules for all family members and suggest dates 2. The system of claim 1.

7. The input unit Estimate the emotions of family members and adjust the timing of schedule entries based on the estimated emotions 2. The system of claim 1.

8. The input unit Analyze past schedule entry history and select the entry method 2. The system of claim 1.

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