system

Abstract

The system according to this embodiment aims to propose a division of household chores and childcare between spouses based on objective criteria. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives information such as the working hours and holidays of the couple, and the distance from their workplace to the daycare center. The analysis unit analyzes the information entered by the reception unit and statistically determines the division of household chores and childcare. The proposal unit proposes the division of household chores and childcare based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that in families where the division of housework and childcare between spouses is felt to be unfair, means for proposing a division based on objective criteria are not sufficiently provided.

[0005] The system according to the embodiment aims to propose a division of housework and childcare between spouses based on objective criteria.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives information such as the working hours and holidays of the couple, and the distance from their workplace to the daycare center. The analysis unit analyzes the information entered by the reception unit and statistically determines the division of household chores and childcare. The proposal unit proposes the division of household chores and childcare based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose a division of household chores and childcare between spouses based on objective criteria. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The household chore and childcare sharing support system according to an embodiment of the present invention is a system for ensuring fairness in the division of household chores and childcare between spouses. This household chore and childcare sharing support system requires each spouse to input detailed information such as their working hours, days off, and the distance from their workplaces to the daycare center into an app. Next, an AI analyzes this data and, based on big data, statistically determines and recommends the division of household chores and childcare. This mechanism can eliminate unfairness in household chores and childcare and reduce dissatisfaction between spouses. For example, each spouse inputs their working hours. For example, if the husband works 5 days a week from 10am to 6pm and the wife works 4 days a week from 9am to 3pm, these working hours are entered into the app. Next, each spouse inputs their days off. For example, if the husband has Saturdays and Sundays off and the wife has Mondays, Wednesdays, and Saturdays off, these days off are entered into the app. The distance from each spouse's workplace to the daycare center is also input. For example, if the distance from the husband's workplace to the daycare center is 1km and the distance from the wife's workplace to the daycare center is 5km, these distances are entered into the app. Next, the AI ​​analyzes this data. The AI ​​statistically determines the division of household chores and childcare based on data such as each spouse's working hours, days off, and distance from their workplace to the daycare center. For example, if the husband works five days a week from 10 am to 6 pm and the wife works four days a week from 9 am to 3 pm, the AI ​​will determine that the husband has less time to dedicate to household chores and childcare and will assign more of them to the wife. Also, if the distance from the husband's workplace to the daycare center is 1 km and the distance from the wife's workplace to the daycare center is 5 km, the AI ​​will determine that it is more efficient for the husband to be in charge of dropping off and picking up the child from daycare. Furthermore, the AI ​​recommends the division of household chores and childcare based on big data. For example, based on data on the division of household chores and childcare in similar families or families in similar circumstances, it will suggest the optimal division of household chores and childcare for each spouse. This can eliminate unfairness in household chores and childcare and reduce dissatisfaction between spouses. This means that the household chore and childcare sharing support system can eliminate unfairness in household chores and childcare and reduce dissatisfaction between spouses by statistically determining and proposing the division of household chores and childcare based on information such as the working hours and holidays of the couple and the distance from the workplace to the daycare center.

[0029] The household chore and childcare sharing support system according to this embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit inputs information such as the working hours and holidays of the couple, and the distance from their workplaces to the daycare center. The working hours of the couple include, but are not limited to, full-time, part-time, and shift work. For example, if the husband works 5 days a week from 10:00 to 18:00 and the wife works 4 days a week from 9:00 to 15:00, the reception unit inputs these working hours. The reception unit also inputs the holidays of each spouse. For example, if the husband has Saturdays and Sundays off and the wife has Mondays, Wednesdays, and Saturdays off, these holidays are input. Furthermore, the reception unit inputs the distance from each person's workplace to the daycare center. For example, if the distance from the husband's workplace to the daycare center is 1 km and the distance from the wife's workplace to the daycare center is 5 km, these distances are input. The analysis unit analyzes the information input by the reception unit and statistically determines the division of household chores and childcare. The analysis unit determines the division of household chores and childcare based on data such as each spouse's working hours, days off, and distance from their workplace to the daycare center. For example, if the husband works five days a week from 10 am to 6 pm and the wife works four days a week from 9 am to 3 pm, the analysis unit will determine that the husband has less time to dedicate to household chores and childcare and will assign more of them to the wife. Also, if the distance from the husband's workplace to the daycare center is 1 km and the distance from the wife's workplace to the daycare center is 5 km, the analysis unit will determine that it is more efficient for the husband to be responsible for dropping off and picking up the child from daycare. The proposal unit proposes the division of household chores and childcare based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the optimal division of household chores and childcare for each spouse based on data on the division of household chores and childcare in similar families or families with similar circumstances. As a result, the household chore and childcare sharing support system according to this embodiment can eliminate unfairness in household chores and childcare and reduce dissatisfaction between spouses by statistically determining and proposing the division of household chores and childcare based on information such as the working hours and holidays of the couple and the distance from the workplace to the nursery school.

[0030] The reception desk inputs information such as the couple's working hours, days off, and the distance from their workplaces to the daycare center. Working hours include, but are not limited to, full-time, part-time, or shift work. For example, if the husband works 5 days a week from 10am to 6pm and the wife works 4 days a week from 9am to 3pm, the reception desk would input these working hours. The reception desk also inputs each spouse's days off. For example, if the husband has Saturdays and Sundays off and the wife has Mondays, Wednesdays, and Saturdays off, these days off would be entered. Furthermore, the reception desk inputs the distance from each workplace to the daycare center. For example, if the distance from the husband's workplace to the daycare center is 1km and the distance from the wife's workplace to the daycare center is 5km, these distances would be entered. To efficiently collect this information, the reception desk provides a user-friendly interface. For example, the input form is designed to be intuitive and easy to understand, allowing users to easily enter information. The entered data is encrypted to ensure security and protect privacy. Additionally, the reception desk has a function to save the information entered by the user, allowing for later editing and updating. This allows users to update their information in accordance with changes in their lifestyle, ensuring they always have the latest data. The reception unit plays a role in quickly and accurately transmitting the information entered by users to the analysis unit, thereby improving the overall efficiency of the system.

[0031] The analysis department analyzes the information entered by the reception department and statistically determines the division of household chores and childcare. For example, the analysis department determines the division of household chores and childcare based on data such as each spouse's working hours, holidays, and distance from their workplaces to the daycare center. For example, if the husband works 5 days a week from 10am to 6pm and the wife works 4 days a week from 9am to 3pm, the analysis department will determine that the husband has less time to dedicate to household chores and childcare and will assign more of them to the wife. Also, if the distance from the husband's workplace to the daycare center is 1km and the distance from the wife's workplace to the daycare center is 5km, the analysis department will determine that it is more efficient for the husband to be in charge of dropping off and picking up the child from daycare. The analysis department uses AI to analyze the data and derive the optimal division of household chores and childcare. The AI ​​learns from past data and data from similar families and proposes the optimal division method using statistical methods. For example, the AI ​​analyzes the working hours and holiday patterns of the couple and determines which person should be in charge of household chores and childcare at which time of day. Furthermore, the AI ​​considers the distance to the daycare center and traffic conditions to suggest the optimal method of dropping off and picking up children. In addition, the analysis department collects user feedback and continuously improves the analysis algorithm. This allows the analysis department to always provide the most optimal division of household chores and childcare using the latest information and technology.

[0032] The Proposal Department proposes a division of household chores and childcare based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes the optimal division of household chores and childcare for each spouse based on data on the division of household chores and childcare in similar families or families with similar circumstances. The Proposal Department creates a specific schedule for the division of household chores and childcare based on the data provided by the Analysis Department. For example, it proposes a schedule where the husband is responsible for taking the child to daycare in the morning and the wife is responsible for picking them up in the evening. The Proposal Department also makes specific suggestions regarding the division of household chores. For example, it proposes a specific task division, such as the husband being responsible for cleaning on weekends and the wife being responsible for cooking on weekdays. The Proposal Department provides a visually easy-to-understand interface so that users can easily check the proposed schedule and tasks. For example, it displays the schedule in a calendar format and displays the person responsible for each task and the time slot in different colors. The Proposal Department also provides a function that allows users to edit the proposed schedule and tasks. This allows users to flexibly adjust the schedule to suit their lifestyle. Furthermore, the Proposal Department collects user feedback and continuously improves the accuracy and effectiveness of the suggestions. This allows the proposal department to consistently provide the optimal division of household chores and childcare responsibilities, thereby reducing dissatisfaction between spouses.

[0033] The proposal department can propose the division of household chores and childcare based on big data. For example, the proposal department can propose the optimal division of household chores and childcare for each spouse based on data on the division of household chores and childcare in similar families or families in similar circumstances. For example, the proposal department can analyze information such as the working hours, holidays, and distance from the workplace to the daycare center of each spouse based on big data and propose the division of household chores and childcare. By proposing the division of household chores and childcare based on big data, a more objective and equitable division can be achieved.

[0034] The proposal department can propose a division of household chores and childcare based on data on how household chores and childcare are divided in similar or nearby households. For example, the proposal department can propose the optimal division of household chores and childcare for each spouse based on data on how household chores and childcare are divided in similar or nearby households. For example, the proposal department can analyze information such as the spouses' working hours, holidays, and distance from the workplace to the daycare center, based on data on how household chores and childcare are divided in similar or nearby households, and propose a division of household chores and childcare. By making proposals based on data on how household chores and childcare are divided in similar or nearby households, a more realistic and practical division of labor can be achieved.

[0035] The reception desk can analyze the user's past input history and suggest the optimal method for entering work hours. For example, the reception desk can automatically display work hours that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest work hours to be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal method for entering work hours and streamline the input process. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI.

[0036] The reception system can simplify the input process by automatically acquiring the user's current location information when they enter their work hours. For example, when a user opens the app, the reception system can automatically acquire their current location and simplify the input of their work hours. The reception system can also suggest optimal locations considering the distance from the user's current location when they enter their work hours. Furthermore, if a user uses the app while on the move, the reception system can update their current location in real time and simplify the input of their work hours. By automatically acquiring the user's current location information, the input of work hours can be simplified and the input process can be made more efficient. Some or all of the above processes in the reception system may be performed using AI, for example, or without using AI.

[0037] The reception desk can automatically suggest potential destinations when a user enters their holiday schedule, referencing their past travel history. For example, the reception desk can automatically display places the user has frequently visited in the past as potential destinations. The reception desk can also predict places the user will visit on specific days of the week or at specific times and suggest them as potential destinations. Furthermore, the reception desk can analyze the user's past travel patterns and suggest the most suitable destinations. This streamlines the holiday entry process and automatically suggests potential destinations by referencing the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0038] The reception desk can refer to the user's calendar information when they enter holidays and make suggestions based on their schedule. For example, the reception desk can refer to the appointments registered in the user's calendar and automatically set the holiday entry. The reception desk can also suggest locations related to specific events as candidate locations based on the user's calendar information. Furthermore, the reception desk can suggest the most suitable holiday entry based on the user's schedule, based on the user's calendar information. In this way, by referring to the user's calendar information, the system can streamline the holiday entry process based on the schedule and make suggestions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0039] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of the input information during the analysis. For example, the analysis unit can analyze the division of household chores and childcare by considering the interrelationship between working hours and holidays. It can also analyze efficient division of chores by considering the interrelationship between the distance from the workplace to the daycare center and working hours. Furthermore, the analysis unit can analyze the optimal division of household chores and childcare by considering the balance between the working hours and holidays of both spouses. In this way, the accuracy of the analysis can be improved by considering the interrelationships of the input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0040] The analysis unit can perform analysis while considering the attribute information of the inputter. For example, the analysis unit can analyze the division of household chores and childcare by considering the age and health status of the couple. The analysis unit can also analyze the optimal division of chores by considering the occupation and work style of the couple. Furthermore, the analysis unit can analyze the division of household chores and childcare by considering the lifestyle and hobbies of the couple. By considering the attribute information of the inputter, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0041] The analysis unit can perform analysis while considering the geographical distribution of information. For example, the analysis unit can analyze the efficient division of household chores and childcare by considering the geographical distribution of the couple's workplaces and daycare centers. The analysis unit can also analyze the optimal division of chores by considering the geographical distribution of the couple's residences and workplaces. Furthermore, the analysis unit can analyze the division of household chores and childcare by considering the geographical distribution within the couple's living area. By considering the geographical distribution of information, it is possible to analyze a more efficient division of household chores and childcare. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0042] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research literature on the division of household chores and childcare. It can also improve the accuracy of its analysis by referring to case studies of the division of household chores and childcare in similar families. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to statistical data on the division of household chores and childcare. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0043] The proposal unit can adjust the level of detail of its proposals based on the importance of the analysis results. For example, the proposal unit can provide detailed proposals based on important analysis results. It can also provide simplified proposals based on less important analysis results. Furthermore, the proposal unit can adjust the level of detail of its proposals in stages according to the importance of the analysis results. This allows the proposal unit to provide users with proposals that are suitable for them by adjusting the level of detail of its proposals based on the importance of the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0044] The proposal unit can apply different proposal algorithms depending on the category of the analysis results when making a proposal. For example, the proposal unit can apply a specific proposal algorithm based on the analysis results regarding the division of household chores. It can also apply a different proposal algorithm based on the analysis results regarding the division of childcare. Furthermore, the proposal unit can select and apply the most appropriate proposal algorithm depending on the category of the analysis results. By applying different proposal algorithms depending on the category of the analysis results, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0045] The proposal department can determine the priority of proposals based on the timing of the submission of analysis results. For example, the proposal department will prioritize proposals with the most recent analysis results. Conversely, the proposal department may lower the priority of proposals with older analysis results. The proposal department can also adjust the priority of proposals in stages according to the timing of the submission of analysis results. This allows for the provision of the latest information preferentially by determining the priority of proposals based on the timing of the submission of analysis results. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI.

[0046] The proposal unit can adjust the order of proposals based on the relevance of the analysis results. For example, if the analysis results are highly relevant, the proposal unit will prioritize making that proposal. Conversely, if the analysis results are less relevant, the proposal unit can postpone that proposal. The proposal unit can also adjust the order of proposals in stages according to the relevance of the analysis results. By adjusting the order of proposals based on the relevance of the analysis results, the proposal unit can prioritize providing information that is important to the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The reception desk can analyze the user's past input history and suggest the optimal method for entering work hours. For example, the reception desk can automatically display work hours that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest work hours to be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal method for entering work hours and streamline the input process. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI.

[0049] The reception system can simplify the input process by automatically acquiring the user's current location information when they enter their work hours. For example, when a user opens the app, the reception system can automatically acquire their current location and simplify the input of their work hours. The reception system can also suggest optimal locations considering the distance from the user's current location when they enter their work hours. Furthermore, if a user uses the app while on the move, the reception system can update their current location in real time and simplify the input of their work hours. By automatically acquiring the user's current location information, the input of work hours can be simplified and the input process can be made more efficient. Some or all of the above processes in the reception system may be performed using AI, for example, or without using AI.

[0050] The reception desk can automatically suggest potential destinations when a user enters their holiday schedule, referencing their past travel history. For example, the reception desk can automatically display places the user has frequently visited in the past as potential destinations. The reception desk can also predict places the user will visit on specific days of the week or at specific times and suggest them as potential destinations. Furthermore, the reception desk can analyze the user's past travel patterns and suggest the most suitable destinations. This streamlines the holiday entry process and automatically suggests potential destinations by referencing the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0051] The reception desk can refer to the user's calendar information when they enter holidays and make suggestions based on their schedule. For example, the reception desk can refer to the appointments registered in the user's calendar and automatically set the holiday entry. The reception desk can also suggest locations related to specific events as candidate locations based on the user's calendar information. Furthermore, the reception desk can suggest the most suitable holiday entry based on the user's schedule, based on the user's calendar information. In this way, by referring to the user's calendar information, the system can streamline the holiday entry process based on the schedule and make suggestions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0052] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of the input information during the analysis. For example, the analysis unit can analyze the division of household chores and childcare by considering the interrelationship between working hours and holidays. It can also analyze efficient division of chores by considering the interrelationship between the distance from the workplace to the daycare center and working hours. Furthermore, the analysis unit can analyze the optimal division of household chores and childcare by considering the balance between the working hours and holidays of both spouses. In this way, the accuracy of the analysis can be improved by considering the interrelationships of the input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The reception desk will input information such as the couple's working hours, days off, and distance from their workplaces to the daycare center. For example, if the husband works 5 days a week from 10am to 6pm and the wife works 4 days a week from 9am to 3pm, these working hours will be entered. The couple's days off will also be entered; if the husband has Saturdays and Sundays off and the wife has Mondays, Wednesdays, and Saturdays off, these days off will be entered. Furthermore, if the distance from the husband's workplace to the daycare center is 1km and the distance from the wife's workplace to the daycare center is 5km, these distances will be entered. Step 2: The analysis unit analyzes the information entered by the reception unit and statistically determines the division of household chores and childcare. For example, if the husband works 5 days a week from 10am to 6pm and the wife works 4 days a week from 9am to 3pm, the analysis unit will determine that the husband has less time to dedicate to household chores and childcare and will assign more of them to the wife. Also, if the distance from the husband's workplace to the daycare center is 1km and the distance from the wife's workplace to the daycare center is 5km, the analysis unit will determine that it is more efficient for the husband to be in charge of dropping off and picking up the children from daycare. Step 3: The proposal unit proposes a division of household chores and childcare based on the analysis results obtained by the analysis unit. For example, it proposes the optimal division of household chores and childcare for each spouse based on data on the division of household chores and childcare in similar families or families with similar circumstances.

[0055] (Example of form 2) The household chore and childcare sharing support system according to an embodiment of the present invention is a system for ensuring fairness in the division of household chores and childcare between spouses. This household chore and childcare sharing support system requires each spouse to input detailed information such as their working hours, days off, and the distance from their workplaces to the daycare center into an app. Next, an AI analyzes this data and, based on big data, statistically determines and recommends the division of household chores and childcare. This mechanism can eliminate unfairness in household chores and childcare and reduce dissatisfaction between spouses. For example, each spouse inputs their working hours. For example, if the husband works 5 days a week from 10am to 6pm and the wife works 4 days a week from 9am to 3pm, these working hours are entered into the app. Next, each spouse inputs their days off. For example, if the husband has Saturdays and Sundays off and the wife has Mondays, Wednesdays, and Saturdays off, these days off are entered into the app. The distance from each spouse's workplace to the daycare center is also input. For example, if the distance from the husband's workplace to the daycare center is 1km and the distance from the wife's workplace to the daycare center is 5km, these distances are entered into the app. Next, the AI ​​analyzes this data. The AI ​​statistically determines the division of household chores and childcare based on data such as each spouse's working hours, days off, and distance from their workplace to the daycare center. For example, if the husband works five days a week from 10 am to 6 pm and the wife works four days a week from 9 am to 3 pm, the AI ​​will determine that the husband has less time to dedicate to household chores and childcare and will assign more of them to the wife. Also, if the distance from the husband's workplace to the daycare center is 1 km and the distance from the wife's workplace to the daycare center is 5 km, the AI ​​will determine that it is more efficient for the husband to be in charge of dropping off and picking up the child from daycare. Furthermore, the AI ​​recommends the division of household chores and childcare based on big data. For example, based on data on the division of household chores and childcare in similar families or families in similar circumstances, it will suggest the optimal division of household chores and childcare for each spouse. This can eliminate unfairness in household chores and childcare and reduce dissatisfaction between spouses. This means that the household chore and childcare sharing support system can eliminate unfairness in household chores and childcare and reduce dissatisfaction between spouses by statistically determining and proposing the division of household chores and childcare based on information such as the working hours and holidays of the couple and the distance from the workplace to the daycare center.

[0056] The household chore and childcare sharing support system according to this embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit inputs information such as the working hours and holidays of the couple, and the distance from their workplaces to the daycare center. The working hours of the couple include, but are not limited to, full-time, part-time, and shift work. For example, if the husband works 5 days a week from 10:00 to 18:00 and the wife works 4 days a week from 9:00 to 15:00, the reception unit inputs these working hours. The reception unit also inputs the holidays of each spouse. For example, if the husband has Saturdays and Sundays off and the wife has Mondays, Wednesdays, and Saturdays off, these holidays are input. Furthermore, the reception unit inputs the distance from each person's workplace to the daycare center. For example, if the distance from the husband's workplace to the daycare center is 1 km and the distance from the wife's workplace to the daycare center is 5 km, these distances are input. The analysis unit analyzes the information input by the reception unit and statistically determines the division of household chores and childcare. The analysis unit determines the division of household chores and childcare based on data such as each spouse's working hours, days off, and distance from their workplace to the daycare center. For example, if the husband works five days a week from 10 am to 6 pm and the wife works four days a week from 9 am to 3 pm, the analysis unit will determine that the husband has less time to dedicate to household chores and childcare and will assign more of them to the wife. Also, if the distance from the husband's workplace to the daycare center is 1 km and the distance from the wife's workplace to the daycare center is 5 km, the analysis unit will determine that it is more efficient for the husband to be responsible for dropping off and picking up the child from daycare. The proposal unit proposes the division of household chores and childcare based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the optimal division of household chores and childcare for each spouse based on data on the division of household chores and childcare in similar families or families with similar circumstances. As a result, the household chore and childcare sharing support system according to this embodiment can eliminate unfairness in household chores and childcare and reduce dissatisfaction between spouses by statistically determining and proposing the division of household chores and childcare based on information such as the working hours and holidays of the couple and the distance from the workplace to the nursery school.

[0057] The reception desk inputs information such as the couple's working hours, days off, and the distance from their workplaces to the daycare center. Working hours include, but are not limited to, full-time, part-time, or shift work. For example, if the husband works 5 days a week from 10am to 6pm and the wife works 4 days a week from 9am to 3pm, the reception desk would input these working hours. The reception desk also inputs each spouse's days off. For example, if the husband has Saturdays and Sundays off and the wife has Mondays, Wednesdays, and Saturdays off, these days off would be entered. Furthermore, the reception desk inputs the distance from each workplace to the daycare center. For example, if the distance from the husband's workplace to the daycare center is 1km and the distance from the wife's workplace to the daycare center is 5km, these distances would be entered. To efficiently collect this information, the reception desk provides a user-friendly interface. For example, the input form is designed to be intuitive and easy to understand, allowing users to easily enter information. The entered data is encrypted to ensure security and protect privacy. Additionally, the reception desk has a function to save the information entered by the user, allowing for later editing and updating. This allows users to update their information in accordance with changes in their lifestyle, ensuring they always have the latest data. The reception unit plays a role in quickly and accurately transmitting the information entered by users to the analysis unit, thereby improving the overall efficiency of the system.

[0058] The analysis department analyzes the information entered by the reception department and statistically determines the division of household chores and childcare. For example, the analysis department determines the division of household chores and childcare based on data such as each spouse's working hours, holidays, and distance from their workplaces to the daycare center. For example, if the husband works 5 days a week from 10am to 6pm and the wife works 4 days a week from 9am to 3pm, the analysis department will determine that the husband has less time to dedicate to household chores and childcare and will assign more of them to the wife. Also, if the distance from the husband's workplace to the daycare center is 1km and the distance from the wife's workplace to the daycare center is 5km, the analysis department will determine that it is more efficient for the husband to be in charge of dropping off and picking up the child from daycare. The analysis department uses AI to analyze the data and derive the optimal division of household chores and childcare. The AI ​​learns from past data and data from similar families and proposes the optimal division method using statistical methods. For example, the AI ​​analyzes the working hours and holiday patterns of the couple and determines which person should be in charge of household chores and childcare at which time of day. Furthermore, the AI ​​considers the distance to the daycare center and traffic conditions to suggest the optimal method of dropping off and picking up children. In addition, the analysis department collects user feedback and continuously improves the analysis algorithm. This allows the analysis department to always provide the most optimal division of household chores and childcare using the latest information and technology.

[0059] The Proposal Department proposes a division of household chores and childcare based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes the optimal division of household chores and childcare for each spouse based on data on the division of household chores and childcare in similar families or families with similar circumstances. The Proposal Department creates a specific schedule for the division of household chores and childcare based on the data provided by the Analysis Department. For example, it proposes a schedule where the husband is responsible for taking the child to daycare in the morning and the wife is responsible for picking them up in the evening. The Proposal Department also makes specific suggestions regarding the division of household chores. For example, it proposes a specific task division, such as the husband being responsible for cleaning on weekends and the wife being responsible for cooking on weekdays. The Proposal Department provides a visually easy-to-understand interface so that users can easily check the proposed schedule and tasks. For example, it displays the schedule in a calendar format and displays the person responsible for each task and the time slot in different colors. The Proposal Department also provides a function that allows users to edit the proposed schedule and tasks. This allows users to flexibly adjust the schedule to suit their lifestyle. Furthermore, the Proposal Department collects user feedback and continuously improves the accuracy and effectiveness of the suggestions. This allows the proposal department to consistently provide the optimal division of household chores and childcare responsibilities, thereby reducing dissatisfaction between spouses.

[0060] The proposal department can propose the division of household chores and childcare based on big data. For example, the proposal department can propose the optimal division of household chores and childcare for each spouse based on data on the division of household chores and childcare in similar families or families in similar circumstances. For example, the proposal department can analyze information such as the working hours, holidays, and distance from the workplace to the daycare center of each spouse based on big data and propose the division of household chores and childcare. By proposing the division of household chores and childcare based on big data, a more objective and equitable division can be achieved.

[0061] The proposal department can propose a division of household chores and childcare based on data on how household chores and childcare are divided in similar or nearby households. For example, the proposal department can propose the optimal division of household chores and childcare for each spouse based on data on how household chores and childcare are divided in similar or nearby households. For example, the proposal department can analyze information such as the spouses' working hours, holidays, and distance from the workplace to the daycare center, based on data on how household chores and childcare are divided in similar or nearby households, and propose a division of household chores and childcare. By making proposals based on data on how household chores and childcare are divided in similar or nearby households, a more realistic and practical division of labor can be achieved.

[0062] The reception desk can estimate the user's emotions and adjust the work time input method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the work time input procedure. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable work time input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick work time input. This reduces user stress and streamlines the input process by adjusting the work time input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0063] The reception desk can analyze the user's past input history and suggest the optimal method for entering work hours. For example, the reception desk can automatically display work hours that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest work hours to be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal method for entering work hours and streamline the input process. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI.

[0064] The reception system can simplify the input process by automatically acquiring the user's current location information when they enter their work hours. For example, when a user opens the app, the reception system can automatically acquire their current location and simplify the input of their work hours. The reception system can also suggest optimal locations considering the distance from the user's current location when they enter their work hours. Furthermore, if a user uses the app while on the move, the reception system can update their current location in real time and simplify the input of their work hours. By automatically acquiring the user's current location information, the input of work hours can be simplified and the input process can be made more efficient. Some or all of the above processes in the reception system may be performed using AI, for example, or without using AI.

[0065] The reception desk can estimate the user's emotions and adjust the design of the holiday input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide an interface with calming colors to reduce visual stress. If the user is having fun, the reception desk can provide an interface with bright colors to make holiday input work more enjoyable. If the user is tired, the reception desk can provide a simple and highly visible interface to make holiday input work easier. In this way, by adjusting the design of the holiday input interface according to the user's emotions, user stress can be reduced and input work can be made more efficient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0066] The reception desk can automatically suggest potential destinations when a user enters their holiday schedule, referencing their past travel history. For example, the reception desk can automatically display places the user has frequently visited in the past as potential destinations. The reception desk can also predict places the user will visit on specific days of the week or at specific times and suggest them as potential destinations. Furthermore, the reception desk can analyze the user's past travel patterns and suggest the most suitable destinations. This streamlines the holiday entry process and automatically suggests potential destinations by referencing the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0067] The reception desk can refer to the user's calendar information when they enter holidays and make suggestions based on their schedule. For example, the reception desk can refer to the appointments registered in the user's calendar and automatically set the holiday entry. The reception desk can also suggest locations related to specific events as candidate locations based on the user's calendar information. Furthermore, the reception desk can suggest the most suitable holiday entry based on the user's schedule, based on the user's calendar information. In this way, by referring to the user's calendar information, the system can streamline the holiday entry process based on the schedule and make suggestions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0068] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. If the user is in a hurry, the analysis unit can perform a simplified analysis and provide results quickly. If the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. In this way, by adjusting the analysis criteria according to the user's emotions, it is possible to provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of the input information during the analysis. For example, the analysis unit can analyze the division of household chores and childcare by considering the interrelationship between working hours and holidays. It can also analyze efficient division of chores by considering the interrelationship between the distance from the workplace to the daycare center and working hours. Furthermore, the analysis unit can analyze the optimal division of household chores and childcare by considering the balance between the working hours and holidays of both spouses. In this way, the accuracy of the analysis can be improved by considering the interrelationships of the input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0070] The analysis unit can perform analysis while considering the attribute information of the inputter. For example, the analysis unit can analyze the division of household chores and childcare by considering the age and health status of the couple. The analysis unit can also analyze the optimal division of chores by considering the occupation and work style of the couple. Furthermore, the analysis unit can analyze the division of household chores and childcare by considering the lifestyle and hobbies of the couple. By considering the attribute information of the inputter, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0071] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can display important information first to reduce visual stress. If the user is relaxed, the analysis unit can sequentially display analysis results including detailed information. If the user is in a hurry, the analysis unit can display concise analysis results first. In this way, by adjusting the display order of analysis results according to the user's emotions, information suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The analysis unit can perform analysis while considering the geographical distribution of information. For example, the analysis unit can analyze the efficient division of household chores and childcare by considering the geographical distribution of the couple's workplaces and daycare centers. The analysis unit can also analyze the optimal division of chores by considering the geographical distribution of the couple's residences and workplaces. Furthermore, the analysis unit can analyze the division of household chores and childcare by considering the geographical distribution within the couple's living area. By considering the geographical distribution of information, it is possible to analyze a more efficient division of household chores and childcare. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0073] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research literature on the division of household chores and childcare. It can also improve the accuracy of its analysis by referring to case studies of the division of household chores and childcare in similar families. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to statistical data on the division of household chores and childcare. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0074] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion function can provide a simple and highly visible suggestion. If the user is relaxed, the suggestion function can also provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion function can provide a suggestion that gets straight to the point. In this way, by adjusting the way suggestions are presented according to the user's emotions, it is possible to provide suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The proposal unit can adjust the level of detail of its proposals based on the importance of the analysis results. For example, the proposal unit can provide detailed proposals based on important analysis results. It can also provide simplified proposals based on less important analysis results. Furthermore, the proposal unit can adjust the level of detail of its proposals in stages according to the importance of the analysis results. This allows the proposal unit to provide users with proposals that are suitable for them by adjusting the level of detail of its proposals based on the importance of the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0076] The proposal unit can apply different proposal algorithms depending on the category of the analysis results when making a proposal. For example, the proposal unit can apply a specific proposal algorithm based on the analysis results regarding the division of household chores. It can also apply a different proposal algorithm based on the analysis results regarding the division of childcare. Furthermore, the proposal unit can select and apply the most appropriate proposal algorithm depending on the category of the analysis results. By applying different proposal algorithms depending on the category of the analysis results, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0077] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is nervous, the suggestion function will provide a short, to-the-point suggestion. If the user is relaxed, the suggestion function can provide a longer suggestion with more detailed explanations. If the user is in a hurry, the suggestion function can provide a quick and concise suggestion. In this way, by adjusting the length of the suggestion according to the user's emotions, it is possible to provide suggestions that are appropriate for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The proposal department can determine the priority of proposals based on the timing of the submission of analysis results. For example, the proposal department will prioritize proposals with the most recent analysis results. Conversely, the proposal department may lower the priority of proposals with older analysis results. The proposal department can also adjust the priority of proposals in stages according to the timing of the submission of analysis results. This allows for the provision of the latest information preferentially by determining the priority of proposals based on the timing of the submission of analysis results. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI.

[0079] The proposal unit can adjust the order of proposals based on the relevance of the analysis results. For example, if the analysis results are highly relevant, the proposal unit will prioritize making that proposal. Conversely, if the analysis results are less relevant, the proposal unit can postpone that proposal. The proposal unit can also adjust the order of proposals in stages according to the relevance of the analysis results. By adjusting the order of proposals based on the relevance of the analysis results, the proposal unit can prioritize providing information that is important to the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. If the user is in a hurry, the analysis unit can perform a simplified analysis and provide results quickly. If the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. In this way, by adjusting the analysis criteria according to the user's emotions, it is possible to provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion function can provide a simple and highly visible suggestion. If the user is relaxed, the suggestion function can also provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion function can provide a suggestion that gets straight to the point. In this way, by adjusting the way suggestions are presented according to the user's emotions, it is possible to provide suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is nervous, the suggestion function will provide a short, to-the-point suggestion. If the user is relaxed, the suggestion function can provide a longer suggestion with more detailed explanations. If the user is in a hurry, the suggestion function can provide a quick and concise suggestion. In this way, by adjusting the length of the suggestion according to the user's emotions, it is possible to provide suggestions that are appropriate for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception desk can estimate the user's emotions and adjust the work time input method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the work time input procedure. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable work time input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick work time input. This reduces user stress and streamlines the input process by adjusting the work time input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The reception desk can estimate the user's emotions and adjust the design of the holiday input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide an interface with calming colors to reduce visual stress. If the user is having fun, the reception desk can provide an interface with bright colors to make holiday input work more enjoyable. If the user is tired, the reception desk can provide a simple and highly visible interface to make holiday input work easier. In this way, by adjusting the design of the holiday input interface according to the user's emotions, user stress can be reduced and input work can be made more efficient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The reception desk can analyze the user's past input history and suggest the optimal method for entering work hours. For example, the reception desk can automatically display work hours that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest work hours to be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal method for entering work hours and streamline the input process. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI.

[0087] The reception system can simplify the input process by automatically acquiring the user's current location information when they enter their work hours. For example, when a user opens the app, the reception system can automatically acquire their current location and simplify the input of their work hours. The reception system can also suggest optimal locations considering the distance from the user's current location when they enter their work hours. Furthermore, if a user uses the app while on the move, the reception system can update their current location in real time and simplify the input of their work hours. By automatically acquiring the user's current location information, the input of work hours can be simplified and the input process can be made more efficient. Some or all of the above processes in the reception system may be performed using AI, for example, or without using AI.

[0088] The reception desk can automatically suggest potential destinations when a user enters their holiday schedule, referencing their past travel history. For example, the reception desk can automatically display places the user has frequently visited in the past as potential destinations. The reception desk can also predict places the user will visit on specific days of the week or at specific times and suggest them as potential destinations. Furthermore, the reception desk can analyze the user's past travel patterns and suggest the most suitable destinations. This streamlines the holiday entry process and automatically suggests potential destinations by referencing the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0089] The reception desk can refer to the user's calendar information when they enter holidays and make suggestions based on their schedule. For example, the reception desk can refer to the appointments registered in the user's calendar and automatically set the holiday entry. The reception desk can also suggest locations related to specific events as candidate locations based on the user's calendar information. Furthermore, the reception desk can suggest the most suitable holiday entry based on the user's schedule, based on the user's calendar information. In this way, by referring to the user's calendar information, the system can streamline the holiday entry process based on the schedule and make suggestions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0090] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of the input information during the analysis. For example, the analysis unit can analyze the division of household chores and childcare by considering the interrelationship between working hours and holidays. It can also analyze efficient division of chores by considering the interrelationship between the distance from the workplace to the daycare center and working hours. Furthermore, the analysis unit can analyze the optimal division of household chores and childcare by considering the balance between the working hours and holidays of both spouses. In this way, the accuracy of the analysis can be improved by considering the interrelationships of the input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The reception desk will input information such as the couple's working hours, days off, and distance from their workplaces to the daycare center. For example, if the husband works 5 days a week from 10am to 6pm and the wife works 4 days a week from 9am to 3pm, these working hours will be entered. The couple's days off will also be entered; if the husband has Saturdays and Sundays off and the wife has Mondays, Wednesdays, and Saturdays off, these days off will be entered. Furthermore, if the distance from the husband's workplace to the daycare center is 1km and the distance from the wife's workplace to the daycare center is 5km, these distances will be entered. Step 2: The analysis unit analyzes the information entered by the reception unit and statistically determines the division of household chores and childcare. For example, if the husband works 5 days a week from 10am to 6pm and the wife works 4 days a week from 9am to 3pm, the analysis unit will determine that the husband has less time to dedicate to household chores and childcare and will assign more of them to the wife. Also, if the distance from the husband's workplace to the daycare center is 1km and the distance from the wife's workplace to the daycare center is 5km, the analysis unit will determine that it is more efficient for the husband to be in charge of dropping off and picking up the children from daycare. Step 3: The proposal unit proposes a division of household chores and childcare based on the analysis results obtained by the analysis unit. For example, it proposes the optimal division of household chores and childcare for each spouse based on data on the division of household chores and childcare in similar families or families with similar circumstances.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0095] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs information such as the working hours and holidays of the couple and the distance from the workplace to the daycare center. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information input by the reception unit to statistically determine the division of household chores and childcare. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the division of household chores and childcare based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0103] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0104] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0105] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0106] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0107] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information such as the working hours and holidays of the couple and the distance from the workplace to the daycare center. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information entered by the reception unit to statistically determine the division of household chores and childcare. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the division of household chores and childcare based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0117] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information such as the working hours and holidays of the couple and the distance from the workplace to the daycare center. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information entered by the reception unit to statistically determine the division of household chores and childcare. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the division of household chores and childcare based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0137] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and inputs information such as the working hours and holidays of the couple and the distance from the workplace to the daycare center. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information input by the reception unit to statistically determine the division of household chores and childcare. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the division of household chores and childcare based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0148] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0149] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0150] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0154] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0155] 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.

[0156] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0157] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0158] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0159] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0161] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0162] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0163] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0164] (Note 1) The reception desk is where you input information such as the couple's working hours, holidays, and the distance from their workplace to the daycare center. The analysis unit analyzes the information entered by the reception unit and statistically determines the division of household chores and childcare responsibilities. The system includes a proposal unit that proposes a division of household chores and childcare based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Proposing a division of household chores and childcare based on big data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on data on the division of household chores and childcare in similar or closely related families, we propose a division of household chores and childcare. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of inputting work hours based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We analyze the user's past input history and suggest the optimal method for entering work hours. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When entering working hours, the system automatically retrieves the user's current location information to simplify the input process. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the design of the holiday input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter their holiday schedule, the system automatically suggests potential destinations based on their past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter holidays, the system references their calendar information to provide suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the interrelationships of the input information are taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the attribute information of the inputter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the geographical distribution of the information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting a proposal, the priority of the proposal will be determined based on the timing of the submission of analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk is where you input information such as the couple's working hours, holidays, and the distance from their workplace to the daycare center. The analysis unit analyzes the information entered by the reception unit and statistically determines the division of household chores and childcare responsibilities. The system includes a proposal unit that proposes a division of household chores and childcare based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, Proposing a division of household chores and childcare based on big data. The system according to feature 1.

3. The aforementioned proposal section is, Based on data on the division of household chores and childcare in similar or closely related families, we propose a division of household chores and childcare. The system according to feature 1.

4. The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of inputting work hours based on those estimated emotions. The system according to feature 1.

5. The aforementioned reception unit is We analyze the user's past input history and suggest the optimal method for entering work hours. The system according to feature 1.

6. The aforementioned reception unit is When entering working hours, the system automatically retrieves the user's current location information to simplify the input process. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the design of the holiday input interface based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When users enter their holiday schedule, the system automatically suggests potential destinations based on their past travel history. The system according to feature 1.

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