System

Abstract

An object of the system according to the embodiment is to enable dual-income parents to efficiently manage their home schedules and tasks and address child-raising issues.SOLUTION: A system according to an embodiment includes an task management unit, a reminding unit, an advice providing unit, and a schedule optimizing unit. The task management unit grasps a schedule or a task of a home. The reminding unit provides reminders based on the schedule or task grasped by the task management unit. The advice providing unit understands problems or needs of childcare of a household and provides individual advice. The schedule optimizer analyzes the schedules of the dual-income parents and suggests an efficient schedule.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for dual-income parents to efficiently manage household schedules and tasks and address the challenges of raising children.

[0005] The system according to the embodiment aims to help dual-income parents efficiently manage their household schedules and tasks and address the challenges of raising children. [Means for solving the problem]

[0006] The system according to the embodiment includes a task management unit, a reminder unit, an advice providing unit, and a schedule optimization unit. The task management unit keeps track of household schedules or tasks. The reminder unit provides reminders based on the schedules or tasks kept by the task management unit. The advice providing unit understands the child-rearing challenges or needs of the household and provides individualized advice. The schedule optimization unit analyzes the schedules of dual-income parents and proposes efficient schedules. [Effects of the Invention]

[0007] The system according to the embodiment allows dual-income parents to efficiently manage their household schedules and tasks and address the challenges of raising children. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A web service according to an embodiment of the present invention is an innovative child-rearing concierge system to support dual-income parents. This child-rearing concierge system utilizes AI-related technologies to manage child-rearing tasks and schedules, provide advice, set reminders, and more. This helps parents reduce stress and help them enjoy raising their children more effectively.

[0029] A child-rearing concierge system according to an embodiment includes a task management unit, a reminder unit, an advice providing unit, and a schedule optimization unit. The task management unit keeps track of household schedules and tasks. For example, the task management unit acquires household calendar information and manages tasks such as children's school events, medical appointments, and household chores. The task management unit can also update tasks based on input information from household members. The reminder unit provides reminders based on the schedules and tasks kept by the task management unit. For example, the reminder unit may send a reminder such as, "Your child's school sports day is tomorrow. Are you ready?" The reminder unit can also send notifications the day before important events. The advice providing unit understands the challenges and needs of child-rearing in the household and provides individualized advice. For example, the advice providing unit may provide advice such as, "Your child is three years old. To provide a balanced diet, consider creating a menu that includes lots of vegetables." The advice providing unit can also provide educational advice according to the child's developmental stage. The schedule optimization unit analyzes the schedules of dual-income parents and proposes efficient schedules. For example, the schedule optimization unit may suggest, "Your child has a medical appointment next Wednesday, but you also have a work meeting on that day. We recommend that you reschedule the medical appointment for another day." The schedule optimization unit can also propose schedules that take into account the parents' work-life balance. In this way, the child-rearing concierge system according to the embodiment can support dual-income parents in raising their children and reduce their stress.

[0030] The reminder section can customize the content of reminders based on the user's past behavioral history and suggest more specific actions. For example, the reminder section uses a generation AI to analyze the user's past behavioral history and customize the content of reminders. For example, if a user has been late to a child's school event in the past, the reminder could suggest a specific action such as, "Tomorrow is your child's school sports day. Please make sure you have made the necessary preparations the day before." The reminder section can also learn the user's behavioral patterns and optimize the content of reminders. This allows the reminder section to customize the content of reminders based on the user's past behavioral history and suggest more specific actions.

[0031] The reminder unit can estimate the user's stress level and fatigue level and optimize the timing of sending reminders. For example, the reminder unit uses a generation AI to estimate the user's stress level and fatigue level and optimize the timing of sending reminders. For example, if the user is tired, the reminder unit can delay the timing of sending reminders or change it to the next day. The reminder unit can also analyze the user's biometric data and monitor the stress level and fatigue level in real time. This allows the reminder unit to estimate the user's stress level and fatigue level and optimize the timing of sending reminders.

[0032] The reminder unit can provide the content of the reminder as an audio message or a video message and communicate it to the user visually or audibly. For example, the reminder unit uses a generation AI to provide the content of the reminder as an audio message. For example, an audio message such as "Your child's school sports day is tomorrow. Are you ready?" can be sent, allowing the user to receive the reminder not only visually but also audibly. The reminder unit can also provide the content of the reminder as a video message. For example, the video message can visually show the content of the reminder, making it easier for the user to understand. This allows the content of the reminder to be provided as an audio message or a video message and communicated to the user visually or audibly.

[0033] The Reminders section can expand the recipients of reminders beyond parents to children and other family members, supporting task management throughout the household. For example, the generative AI can send reminders to all family members, so that everyone can make sure they are prepared for a child's school event. The Reminders section can also share tasks for all family members to support task management throughout the household. This allows reminders to be sent to all family members, so that everyone can make sure they are prepared.

[0034] The advice providing unit can continuously improve the advice it provides based on the user's past feedback and evaluations. For example, the generation AI analyzes the user's past feedback and evaluations, and the advice providing unit continuously improves the content of the advice. For example, if the user evaluates the advice as "helpful," the advice providing unit will continue to provide similar advice in the future. The advice providing unit can also optimize the content of the advice based on the user's feedback. This allows the advice to be continuously improved based on the user's past feedback and evaluations.

[0035] The advice providing unit can personalize the content of advice to match the user's lifestyle and values, and make more specific and actionable suggestions. For example, the advice providing unit personalizes the content of advice by using a generation AI to take into account the user's lifestyle and values. For example, a specific suggestion such as "Consider a menu that incorporates lots of vegetables to provide a balanced diet" is made to a health-conscious user. The advice providing unit can also learn the user's behavioral patterns and optimize the content of advice. This allows the advice to be personalized to match the user's lifestyle and values, and make more specific and actionable suggestions.

[0036] The advice providing unit can provide the content of the advice not only as text but also as infographics or videos to make it easier to understand visually. In the advice providing unit, for example, the generation AI provides the content of the advice as infographics. For example, an infographic that visually shows a menu of a balanced meal can be sent to make it easier for the user to understand the specific menu. The advice providing unit can also provide the content of the advice as a video. For example, the content of the advice can be explained in a video to make it easier for the user to understand visually. This allows the content of the advice to be provided not only as text but also as infographics or videos to make it easier to understand visually.

[0037] The schedule optimization unit analyzes the user's past schedule history and patterns to make more accurate suggestions. For example, the schedule optimization unit uses a generation AI to analyze the user's past schedule history and optimize the schedule. For example, if the user has had an inconsistent work-life balance in the past, the generation AI might make a suggestion such as, "Your child has a medical appointment next Wednesday, but you also have a work meeting on that day. I recommend rescheduling the medical appointment to another day." The schedule optimization unit can also learn the user's behavioral patterns and optimize the schedule suggestions. This allows the system to analyze the user's past schedule history and patterns to make more accurate suggestions.

[0038] The schedule optimization unit can provide schedule suggestions not only in text format but also in cooperation with calendar apps and task management apps, making them easier to understand visually. For example, the schedule optimization unit provides schedule suggestions using a generation AI in cooperation with a calendar app. For example, the schedule can be automatically added to Google Calendar or Outlook Calendar, allowing the user to visually check the schedule. The schedule optimization unit can also manage the progress of tasks in cooperation with task management apps. This allows schedule suggestions to be provided not only in text format but also in cooperation with calendar apps and task management apps, making them easier to understand visually.

[0039] The schedule optimization unit can expand the scope of schedule optimization beyond parents to include children and other family members, thereby supporting schedule management for the entire household. For example, the schedule optimization unit uses a generative AI to expand the scope of schedule optimization beyond parents to include children and other family members. For example, it can centrally manage the schedules of all family members so that everyone can check important events and tasks. The schedule optimization unit can also share the schedules of all family members to support schedule management for the entire household. This allows the scope of schedule optimization beyond parents to include children and other family members, thereby supporting schedule management for the entire household.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The child-rearing concierge system may further include a health management unit. The health management unit monitors the health status of household members and provides appropriate health advice. For example, the health management unit obtains vital data of household members and monitors their health status in real time. The health management unit may also send reminders for regular health checks and support the scheduling of health checkups. Furthermore, the health management unit may analyze the diet and exercise records of household members and provide advice to promote healthy lifestyle habits.

[0042] The child-rearing concierge system can further include a learning support unit. The learning support unit monitors a child's learning progress and provides appropriate learning advice. For example, the learning support unit keeps track of a child's school grades and homework progress and proposes a learning plan. The learning support unit can also provide appropriate learning resources based on a child's interests and areas of expertise. Furthermore, the learning support unit can advise parents on how to support their child's learning and help them create a learning environment at home.

[0043] The child-rearing concierge system may further include a communication support unit. The communication support unit provides support for facilitating communication within the family. For example, the communication support unit supports the exchange of messages between family members to share important information. The communication support unit may also send reminders for family members to express gratitude to each other. Furthermore, the communication support unit may provide advice for family members to set common goals and work together toward those goals.

[0044] The parenting concierge system may further include an entertainment provider. The entertainment provider provides entertainment content for household members to relax and enjoy. For example, the entertainment provider may recommend movies and music based on the preferences of household members. The entertainment provider may also suggest games and activities that household members can enjoy together. The entertainment provider may also provide meditation or yoga sessions for household members to relax.

[0045] The child-rearing concierge system can further include a household finances management section. The household finances management section manages the household's income and expenses and provides advice on effective household finances management. For example, the household finances management section records the household's income and expenses and sets a monthly budget. The household finances management section can also provide advice on saving money and help reduce wasteful spending. Furthermore, the household finances management section can suggest a savings plan to prepare for large expenses in the future.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The task management unit keeps track of household schedules and tasks. For example, it obtains household calendar information and manages tasks such as children's school events, medical appointments, and household chores. It can also update tasks based on input from household members. Step 2: The Reminders section provides reminders based on the schedule and tasks captured by the Task Management section. For example, it can send a reminder such as, "Tomorrow is your child's school sports day. Are you ready?" It can also send notifications the day before important events. Step 3: The advice department understands the challenges and needs of each family in raising children and provides individualized advice. For example, they might say, "Your child is now three years old. To provide a balanced diet, consider creating menus that incorporate lots of vegetables." They can also provide educational advice according to the child's developmental stage. Step 4: The schedule optimization component analyzes the schedules of dual-income parents and proposes efficient schedules. For example, it might say, "Your child has a medical appointment next Wednesday, but you also have a work meeting on that day. We recommend rescheduling the medical appointment to another day." It can also propose schedules that take into account the parents' work-life balance.

[0048] (Example 2) A web service according to an embodiment of the present invention is an innovative child-rearing concierge system to support dual-income parents. This child-rearing concierge system utilizes AI-related technologies to manage child-rearing tasks and schedules, provide advice, set reminders, and more. This helps parents reduce stress and help them enjoy raising their children more effectively.

[0049] A child-rearing concierge system according to an embodiment includes a task management unit, a reminder unit, an advice providing unit, and a schedule optimization unit. The task management unit keeps track of household schedules and tasks. For example, the task management unit acquires household calendar information and manages tasks such as children's school events, medical appointments, and household chores. The task management unit can also update tasks based on input information from household members. The reminder unit provides reminders based on the schedules and tasks kept by the task management unit. For example, the reminder unit may send a reminder such as, "Your child's school sports day is tomorrow. Are you ready?" The reminder unit can also send notifications the day before important events. The advice providing unit understands the challenges and needs of child-rearing in the household and provides individualized advice. For example, the advice providing unit may provide advice such as, "Your child is three years old. To provide a balanced diet, consider creating a menu that includes lots of vegetables." The advice providing unit can also provide educational advice according to the child's developmental stage. The schedule optimization unit analyzes the schedules of dual-income parents and proposes efficient schedules. For example, the schedule optimization unit may suggest, "Your child has a medical appointment next Wednesday, but you also have a work meeting on that day. We recommend that you reschedule the medical appointment for another day." The schedule optimization unit can also propose schedules that take into account the parents' work-life balance. In this way, the child-rearing concierge system according to the embodiment can support dual-income parents in raising their children and reduce their stress.

[0050] The reminder section can customize the content of reminders based on the user's past behavioral history and suggest more specific actions. For example, the reminder section uses a generation AI to analyze the user's past behavioral history and customize the content of reminders. For example, if a user has been late to a child's school event in the past, the reminder could suggest a specific action such as, "Tomorrow is your child's school sports day. Please make sure you have made the necessary preparations the day before." The reminder section can also learn the user's behavioral patterns and optimize the content of reminders. This allows the reminder section to customize the content of reminders based on the user's past behavioral history and suggest more specific actions.

[0051] The reminder unit can estimate the user's stress level and fatigue level and optimize the timing of sending reminders. For example, the reminder unit uses a generation AI to estimate the user's stress level and fatigue level and optimize the timing of sending reminders. For example, if the user is tired, the reminder unit can delay the timing of sending reminders or change it to the next day. The reminder unit can also analyze the user's biometric data and monitor the stress level and fatigue level in real time. This allows the reminder unit to estimate the user's stress level and fatigue level and optimize the timing of sending reminders.

[0052] The reminder unit uses an emotion estimation function to adjust the content of the reminder to match the user's emotional state, eliciting positive emotions. For example, the reminder unit uses a generative AI to estimate the user's emotional state and adjust the content of the reminder. For example, if the user is feeling stressed, the reminder could include a positive message such as "We recommend that you take time to relax today." The reminder unit can also analyze the user's facial expressions and voice to monitor the user's emotional state in real time. This allows the reminder content to be adjusted to match the user's emotional state, eliciting positive emotions.

[0053] The reminder unit can provide the content of the reminder as an audio message or a video message and communicate it to the user visually or audibly. For example, the reminder unit uses a generation AI to provide the content of the reminder as an audio message. For example, an audio message such as "Your child's school sports day is tomorrow. Are you ready?" can be sent, allowing the user to receive the reminder not only visually but also audibly. The reminder unit can also provide the content of the reminder as a video message. For example, the video message can visually show the content of the reminder, making it easier for the user to understand. This allows the content of the reminder to be provided as an audio message or a video message and communicated to the user visually or audibly.

[0054] The Reminders section can expand the recipients of reminders beyond parents to children and other family members, supporting task management throughout the household. For example, the generative AI can send reminders to all family members, so that everyone can make sure they are prepared for a child's school event. The Reminders section can also share tasks for all family members to support task management throughout the household. This allows reminders to be sent to all family members, so that everyone can make sure they are prepared.

[0055] The reminder unit uses an emotion estimation function to monitor the user's emotions in real time when a reminder is sent, and can change the content of the reminder as needed. For example, the reminder unit uses a generation AI to monitor the user's emotions in real time when a reminder is sent, and can change the content of the reminder as needed. For example, if the user is feeling stressed, the reminder content can be changed to a gentler tone. The reminder unit can also analyze the user's emotional state in real time and optimize the content of the reminder. This allows the user's emotions to be monitored in real time when a reminder is sent, and can change the content of the reminder as needed.

[0056] The advice providing unit can continuously improve the advice it provides based on the user's past feedback and evaluations. For example, the generation AI analyzes the user's past feedback and evaluations, and the advice providing unit continuously improves the content of the advice. For example, if the user evaluates the advice as "helpful," the advice providing unit will continue to provide similar advice in the future. The advice providing unit can also optimize the content of the advice based on the user's feedback. This allows the advice to be continuously improved based on the user's past feedback and evaluations.

[0057] The advice providing unit can personalize the content of advice to match the user's lifestyle and values, and make more specific and actionable suggestions. For example, the advice providing unit personalizes the content of advice by using a generation AI to take into account the user's lifestyle and values. For example, a specific suggestion such as "Consider a menu that incorporates lots of vegetables to provide a balanced diet" is made to a health-conscious user. The advice providing unit can also learn the user's behavioral patterns and optimize the content of advice. This allows the advice to be personalized to match the user's lifestyle and values, and make more specific and actionable suggestions.

[0058] The advice providing unit uses an emotion estimation function to adjust the content of advice to match the user's emotional state and elicit positive emotions. For example, the advice providing unit uses a generation AI to estimate the user's emotional state and adjust the content of the advice. For example, if the user is feeling stressed, the advice may include a positive message such as "I recommend you take some time to relax today." The advice providing unit can also analyze the user's emotional state in real time and optimize the content of the advice. This allows the advice content to be adjusted to match the user's emotional state and elicit positive emotions.

[0059] The advice providing unit can provide the content of the advice not only as text but also as infographics or videos to make it easier to understand visually. In the advice providing unit, for example, the generation AI provides the content of the advice as infographics. For example, an infographic that visually shows a menu of a balanced meal can be sent to make it easier for the user to understand the specific menu. The advice providing unit can also provide the content of the advice as a video. For example, the content of the advice can be explained in a video to make it easier for the user to understand visually. This allows the content of the advice to be provided not only as text but also as infographics or videos to make it easier to understand visually.

[0060] The advice providing unit uses the emotion estimation function to monitor the user's emotions in real time when providing advice, and can change the content of the advice as needed. For example, the advice providing unit monitors the user's emotions in real time when the generation AI provides advice, and can change the content of the advice as needed. For example, if the user is feeling stressed, the advice content can be changed to a gentler tone. The advice providing unit can also analyze the user's emotional state in real time and optimize the content of the advice. This makes it possible to monitor the user's emotions in real time when providing advice, and can change the content of the advice as needed.

[0061] The schedule optimization unit analyzes the user's past schedule history and patterns to make more accurate suggestions. For example, the schedule optimization unit uses a generation AI to analyze the user's past schedule history and optimize the schedule. For example, if the user has had an inconsistent work-life balance in the past, the generation AI might make a suggestion such as, "Your child has a medical appointment next Wednesday, but you also have a work meeting on that day. I recommend rescheduling the medical appointment to another day." The schedule optimization unit can also learn the user's behavioral patterns and optimize the schedule suggestions. This allows the system to analyze the user's past schedule history and patterns to make more accurate suggestions.

[0062] The schedule optimization unit uses an emotion estimation function to adjust the schedule suggestions to match the user's emotional state, thereby eliciting positive emotions. For example, the schedule optimization unit uses a generation AI to estimate the user's emotional state and adjust the schedule suggestions accordingly. For example, if the user is feeling stressed, the generation AI may include a positive message such as, "We recommend that you take time to relax today." The schedule optimization unit can also analyze the user's emotional state in real time and optimize the schedule suggestions accordingly. This allows the schedule suggestions to be adjusted to match the user's emotional state, thereby eliciting positive emotions.

[0063] The schedule optimization unit can provide schedule suggestions not only in text format but also in cooperation with calendar apps and task management apps, making them easier to understand visually. For example, the schedule optimization unit provides schedule suggestions using a generation AI in cooperation with a calendar app. For example, the schedule can be automatically added to Google Calendar or Outlook Calendar, allowing the user to visually check the schedule. The schedule optimization unit can also manage the progress of tasks in cooperation with task management apps. This allows schedule suggestions to be provided not only in text format but also in cooperation with calendar apps and task management apps, making them easier to understand visually.

[0064] The schedule optimization unit can expand the scope of schedule optimization beyond parents to include children and other family members, thereby supporting schedule management for the entire household. For example, the schedule optimization unit uses a generative AI to expand the scope of schedule optimization beyond parents to include children and other family members. For example, it can centrally manage the schedules of all family members so that everyone can check important events and tasks. The schedule optimization unit can also share the schedules of all family members to support schedule management for the entire household. This allows the scope of schedule optimization beyond parents to include children and other family members, thereby supporting schedule management for the entire household.

[0065] The schedule optimization unit uses an emotion estimation function to monitor the user's emotions in real time when proposing a schedule, and can change the content of the schedule as needed. For example, the schedule optimization unit monitors the user's emotions in real time when the generation AI proposes a schedule, and can change the content of the schedule as needed. For example, if the user is feeling stressed, the content of the schedule can be changed to a gentler tone. The schedule optimization unit can also analyze the user's emotional state in real time and optimize the content of the schedule. This makes it possible to monitor the user's emotions in real time when proposing a schedule, and can change the content of the schedule as needed.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The child-rearing concierge system may further include a health management unit. The health management unit monitors the health status of household members and provides appropriate health advice. For example, the health management unit obtains vital data of household members and monitors their health status in real time. The health management unit may also send reminders for regular health checks and support the scheduling of health checkups. Furthermore, the health management unit may analyze the diet and exercise records of household members and provide advice to promote healthy lifestyle habits.

[0068] The child-rearing concierge system can further include a learning support unit. The learning support unit monitors a child's learning progress and provides appropriate learning advice. For example, the learning support unit keeps track of a child's school grades and homework progress and proposes a learning plan. The learning support unit can also provide appropriate learning resources based on a child's interests and areas of expertise. Furthermore, the learning support unit can advise parents on how to support their child's learning and help them create a learning environment at home.

[0069] The child-rearing concierge system may further include a communication support unit. The communication support unit provides support for facilitating communication within the family. For example, the communication support unit supports the exchange of messages between family members to share important information. The communication support unit may also send reminders for family members to express gratitude to each other. Furthermore, the communication support unit may provide advice for family members to set common goals and work together toward those goals.

[0070] The parenting concierge system may further include an entertainment provider. The entertainment provider provides entertainment content for household members to relax and enjoy. For example, the entertainment provider may recommend movies and music based on the preferences of household members. The entertainment provider may also suggest games and activities that household members can enjoy together. The entertainment provider may also provide meditation or yoga sessions for household members to relax.

[0071] The child-rearing concierge system can further include a household finances management section. The household finances management section manages the household's income and expenses and provides advice on effective household finances management. For example, the household finances management section records the household's income and expenses and sets a monthly budget. The household finances management section can also provide advice on saving money and help reduce wasteful spending. Furthermore, the household finances management section can suggest a savings plan to prepare for large expenses in the future.

[0072] The parenting concierge system can also use emotion estimation to suggest entertainment content based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing music or movies. If the user is feeling happy, it can suggest games or activities the whole family can enjoy. It can also use emotion estimation to offer meditation or yoga sessions tailored to the user's emotional state.

[0073] The parenting concierge system can further use emotion estimation to tailor financial advice based on the user's emotional state. For example, if the user is stressed, the system can provide financial advice in a gentle tone. Alternatively, if the user is relaxed, the system can provide detailed financial advice. The emotion estimation function can also be used to suggest savings plans tailored to the user's emotional state.

[0074] The parenting concierge system can further use emotion estimation to tailor learning support advice based on the user's emotional state. For example, if the user is feeling stressed, the system can provide learning support advice in a gentle tone. Alternatively, if the user is relaxed, the system can suggest a detailed learning plan. Furthermore, the system can use emotion estimation to provide learning resources tailored to the user's emotional state.

[0075] The parenting concierge system can further use its emotion estimation function to tailor communication support advice based on the user's emotional state. For example, if the user is feeling stressed, the system can provide communication support advice in a gentle tone. Also, if the user is relaxed, the system can suggest detailed communication methods. Furthermore, the emotion estimation function can be used to suggest thank-you messages tailored to the user's emotional state.

[0076] The parenting concierge system can further use emotion inference to tailor health care advice based on the user's emotional state. For example, if the user is stressed, the system can provide health care advice in a gentle tone. Alternatively, if the user is relaxed, the system can provide detailed health care advice. The emotion inference function can also be used to send health check reminders tailored to the user's emotional state.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The task management unit keeps track of household schedules and tasks. For example, it obtains household calendar information and manages tasks such as children's school events, medical appointments, and household chores. It can also update tasks based on input from household members. Step 2: The Reminders section provides reminders based on the schedule and tasks captured by the Task Management section. For example, it can send a reminder such as, "Tomorrow is your child's school sports day. Are you ready?" It can also send notifications the day before important events. Step 3: The advice department understands the challenges and needs of each family in raising children and provides individualized advice. For example, they might say, "Your child is now three years old. To provide a balanced diet, consider creating menus that incorporate lots of vegetables." They can also provide educational advice according to the child's developmental stage. Step 4: The schedule optimization component analyzes the schedules of dual-income parents and proposes efficient schedules. For example, it might say, "Your child has a medical appointment next Wednesday, but you also have a work meeting on that day. We recommend rescheduling the medical appointment to another day." It can also propose schedules that take into account the parents' work-life balance.

[0079] 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.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] 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.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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. [Explanation of symbols]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a task management section for keeping track of household schedules or tasks; a reminder unit that provides reminders based on the schedule or tasks grasped by the task management unit; An advice department that understands the child-rearing challenges or needs of families and provides individual advice; A schedule optimization unit that analyzes the schedules of dual-income parents and proposes efficient schedules. A system characterized by:

2. The reminder unit Customize reminders based on users' past behavior and suggest more specific actions 2. The system of claim 1.

3. The advice providing unit Continually improve the advice we provide based on your past feedback and ratings 2. The system of claim 1.

4. The schedule optimization unit Analyze users' past schedule history and patterns to make more accurate suggestions 2. The system of claim 1.

5. The reminder unit Tailor reminders to suit the user's emotional state, eliciting positive emotions 2. The system of claim 1.

6. The advice providing unit Advice content is tailored to the user's emotional state, eliciting positive emotions 2. The system of claim 1.

7. The schedule optimization unit Monitor user sentiment in real time when proposing a schedule and adjust the schedule as needed 2. The system of claim 1.

8. The reminder unit Monitor user sentiment in real time when sending reminders and adjust reminder content as needed 2. The system of claim 1.

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