system

Abstract

The system according to this embodiment aims to suggest an optimal household chore schedule and tips for efficiency based on the user's lifestyle and schedule. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, and a provision unit. The analysis unit analyzes the user's lifestyle and schedule. The proposal unit proposes an optimal household chore schedule and tips for efficiency based on the data analyzed by the analysis unit. The provision unit provides specific procedures and tips for specific household chores based on the household chore schedule proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to propose an optimal housework schedule based on the user's living habits and schedule, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal housework schedule and hints for efficiency improvement based on the user's living habits and schedule.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, and a provision unit. The analysis unit analyzes the user's lifestyle and schedule. The proposal unit proposes an optimal household chore schedule and tips for efficiency based on the data analyzed by the analysis unit. The provision unit provides specific procedures and tips for particular household chores based on the household chore schedule proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal household chore schedule and tips for efficiency based on the user's lifestyle and schedule. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The household chore management system according to an embodiment of the present invention is a household chore management application that utilizes generative AI to support the daily lives of busy modern people. This household chore management system analyzes the user's lifestyle and schedule and proposes an optimal household chore schedule and tips for efficiency. It also provides specific procedures and tips for particular household chores, reducing the burden within the household. For example, the household chore management system analyzes the user's lifestyle and schedule. In this process, it collects detailed data such as the user's wake-up time, commute time, meal times, and bedtime. For example, if a user wakes up at 7 am every morning and leaves for work at 8 am, it proposes an optimal household chore schedule based on that schedule. Next, the household chore management system proposes an optimal household chore schedule and tips for efficiency based on the analyzed data. For example, it can suggest chores that can be done in a short time in the morning, or incorporate chores that should be done all at once on weekends into the schedule. This allows the user to complete household chores efficiently. Furthermore, the household chore management system provides specific procedures and tips for particular household chores. For example, it provides cleaning procedures, laundry tips, and cooking recipes. This reduces the burden of household chores and allows the user to perform household chores efficiently. This system makes it easier for users to manage their daily lives and reduces the burden on the household. For example, in dual-income households and households with young children, household chores can be prioritized and completed more efficiently. Similarly, in households with single individuals or those supporting elderly people, the burden of household chores is reduced, improving the quality of life. Thus, this household chore management system, utilizing generative AI, is a groundbreaking app that reduces the burden on the household and simplifies daily life management by analyzing the user's lifestyle and schedule, suggesting an optimal chore schedule and tips for efficiency, and providing specific procedures and tips.

[0029] The household chore management system according to this embodiment comprises an analysis unit, a proposal unit, and a provision unit. The analysis unit analyzes the user's lifestyle and schedule. The analysis unit collects detailed data such as the user's wake-up time, commute time, meal times, and bedtime. For example, if the user wakes up at 7:00 AM and leaves for work at 8:00 AM, the analysis unit proposes an optimal household chore schedule based on that schedule. The analysis unit can analyze the user's lifestyle and schedule using a generation AI. For example, the analysis unit inputs the user's lifestyle data into the generation AI, which analyzes the data and proposes an optimal household chore schedule. The proposal unit proposes an optimal household chore schedule and tips for efficiency based on the data analyzed by the analysis unit. For example, the proposal unit proposes household chores that can be done in a short amount of time during the morning. The proposal unit can propose an optimal household chore schedule based on the user's lifestyle and schedule using a generation AI. For example, the proposal unit inputs the user's lifestyle data into the generation AI, which analyzes the data and proposes an optimal household chore schedule. The proposal unit can also incorporate household chores that should be done all at once on weekends into the schedule. For example, the suggestion unit inputs the user's lifestyle data into the generation AI, which analyzes the data and suggests household chores that should be done all at once on the weekend. The provision unit provides specific procedures and tips for specific household chores based on the household chore schedule suggested by the suggestion unit. The provision unit provides, for example, cleaning procedures, laundry tips, and cooking recipes. The provision unit can use the generation AI to provide specific procedures and tips for specific household chores based on the user's lifestyle and schedule. For example, the provision unit inputs the user's lifestyle data into the generation AI, which analyzes the data and provides specific procedures and tips for specific household chores. As a result, the household chore management system according to this embodiment can reduce the burden on the household by analyzing the user's lifestyle and schedule, suggesting an optimal household chore schedule and tips for efficiency, and providing specific procedures and tips.

[0030] The analysis unit analyzes the user's lifestyle and schedule. For example, the analysis unit collects detailed data such as the user's wake-up time, commute time, meal times, and bedtime. Specifically, it utilizes data acquired from the user's smartphone or wearable device to understand the user's daily behavioral patterns in detail. This includes what activities the user engages in at what times of day, where they are, and how frequently they repeat certain actions. For example, if a user wakes up at 7 am every morning and leaves for work at 8 am, the analysis unit will suggest an optimal household chore schedule based on that schedule. The analysis unit can also analyze the user's lifestyle and schedule using generative AI. The generative AI receives the user's lifestyle data as input, learns the data patterns, and generates an optimal household chore schedule. For example, based on the user's past behavioral data, the generative AI predicts which household chores are most efficient to perform at specific times of day. This allows the analysis unit to provide a household chore schedule that is best suited to the user's lifestyle. Furthermore, the analysis unit can also adapt to changes in the user's lifestyle. For example, if a user starts a new job or moves, the household chore schedule is readjusted accordingly. This allows the analysis unit to always provide an optimal household chore schedule based on the latest information, supporting the user's life.

[0031] The suggestion department proposes optimal household chore schedules and tips for efficiency based on data analyzed by the analysis department. For example, the suggestion department suggests chores that can be done quickly in the morning. Specifically, it lists and prioritizes tasks that can be completed quickly so that users can efficiently do chores during their busy morning hours. The suggestion department can use generative AI to propose optimal household chore schedules based on the user's lifestyle and schedule. The generative AI receives user lifestyle data as input, learns data patterns, and generates an optimal household chore schedule. For example, the suggestion department inputs user lifestyle data into the generative AI, which analyzes the data and proposes an optimal household chore schedule. The suggestion department can also incorporate chores that should be done all at once on weekends into the schedule. For example, the suggestion department inputs user lifestyle data into the generative AI, which analyzes the data and proposes chores that should be done all at once on weekends. This allows users to avoid busy weekday hours and efficiently do chores on weekends. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, after a user completes a suggested household chore schedule, the results and feedback are collected, and the generating AI learns from this data and incorporates it into future suggestions. This allows the suggestion system to provide household chore schedules that are better suited to the user's needs, thereby reducing the burden on the household.

[0032] The service provider provides specific procedures and tips for particular household chores based on the household schedule proposed by the suggestion provider. For example, the service provider provides cleaning procedures, laundry tips, and cooking recipes. Specifically, it clearly explains detailed procedures and tips to enable users to efficiently perform the suggested chores. The service provider can use generative AI to provide specific procedures and tips for particular household chores based on the user's lifestyle and schedule. The generative AI receives user lifestyle data as input, learns data patterns, and generates optimal procedures and tips. For example, the service provider inputs user lifestyle data into the generative AI, which analyzes the data and provides specific procedures and tips for specific household chores. This allows users to efficiently perform the suggested chores. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its offerings. For example, after a user performs the provided procedures and tips, the service provider collects the results and feedback, and the generative AI learns from this data and incorporates it into future offerings. This allows the service provider to provide specific procedures and tips that are better suited to the user's needs, reducing the burden on the household. Furthermore, the service provider can customize procedures and tips according to the user's skill level and preferences. For example, when providing cooking recipes, they can offer simple recipes for beginners and complex recipes for advanced cooks. This allows the service provider to offer optimal procedures and tips tailored to the user's skill level and preferences, thereby reducing the burden on households.

[0033] The analysis unit can collect detailed data such as the user's wake-up time, commute time, meal times, and bedtime. For example, the analysis unit can record the user's wake-up time and save it to a database. The analysis unit can also record the user's commute time and save it to a database. The analysis unit can also record the user's meal times and save them to a database. The analysis unit can also record the user's bedtime and save it to a database. This allows for more accurate analysis by collecting detailed lifestyle data of the user. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's lifestyle data into a generative AI, which can analyze the data and propose an optimal household schedule.

[0034] The suggestion unit can suggest household chores that can be done in a short amount of time during the morning hours. For example, the suggestion unit can suggest cleaning that can be done in 5 minutes or less during the morning hours. The suggestion unit can also suggest simple cooking that can be done in a short amount of time during the morning hours. The suggestion unit can also suggest laundry that can be done in a short amount of time during the morning hours. In this way, by suggesting household chores that can be done in a short amount of time during the morning hours, it is possible to support the user in efficiently performing household chores. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the suggestion unit can input the user's lifestyle data into a generative AI, and the generative AI can analyze the data and suggest household chores that can be done in a short amount of time during the morning hours.

[0035] The suggestion unit can incorporate household chores that should be done all at once on weekends into the schedule. For example, the suggestion unit may suggest doing a thorough cleaning on the weekend. It may also suggest doing laundry on the weekend. It may also suggest preparing meals in advance on the weekend. By incorporating household chores that should be done all at once on weekends into the schedule, it can help the user manage their time. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's lifestyle data into a generative AI, which can then analyze the data and suggest household chores that should be done all at once on weekends.

[0036] The service provider can provide cleaning procedures, laundry tips, cooking recipes, and so on. For example, the service provider can provide cleaning procedures. The service provider can also provide laundry tips. The service provider can also provide cooking recipes. By providing specific procedures and tips, it can support the user in performing household chores. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's lifestyle data into a generative AI, which can then analyze the data to provide cleaning procedures, laundry tips, cooking recipes, and so on.

[0037] The analysis unit can analyze the user's past lifestyle data and select the optimal data collection method. For example, the analysis unit can select the optimal method based on the data collection methods the user has used in the past. The analysis unit can also analyze the user's past data collection history and select an efficient method. The analysis unit can also propose the optimal data collection method based on the user's lifestyle. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's past lifestyle data into a generative AI, and the generative AI can analyze the data and select the optimal data collection method.

[0038] The analysis unit can filter lifestyle data based on the user's current health status and stress level. For example, if the user's health status is poor, the analysis unit may refrain from collecting data. The analysis unit may also refrain from collecting data if the user's stress level is high. If the user's health status is good, the analysis unit may collect detailed data. This allows for more appropriate data collection by adjusting data collection according to the user's health status and stress level. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the user's health status and stress level into a generative AI, which can then analyze and filter the data.

[0039] The analysis unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting lifestyle data. For example, if the user is at home, the analysis unit will prioritize the collection of data related to lifestyle habits at home. If the user is at work, the analysis unit can also prioritize the collection of data related to lifestyle habits at work. If the user is out, the analysis unit can also prioritize the collection of data related to lifestyle habits at their destination. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0040] The analysis unit can analyze a user's social media activity and collect relevant data when collecting lifestyle data. For example, the analysis unit can collect lifestyle data based on information shared by the user on social media. The analysis unit can also collect data based on the user's interests from their social media activity. The analysis unit can also analyze the content of a user's social media posts and collect relevant data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity data into a generative AI, which can then analyze the data and collect relevant data.

[0041] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the household chores. For example, it may suggest detailed procedures for high-importance chores, and simplified procedures for low-importance chores. The suggestion unit can also adjust the level of detail in its suggestions according to importance. By adjusting the level of detail in suggestions according to the importance of the chores, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input chore importance data into a generative AI, which can then analyze the data and adjust the level of detail in its suggestions.

[0042] The suggestion unit can apply different suggestion algorithms depending on the category of household chore when making suggestions. For example, the suggestion unit can apply a cleaning-specific algorithm to suggestions related to cleaning. The suggestion unit can also apply a laundry-specific algorithm to suggestions related to laundry. The suggestion unit can also apply a cooking-specific algorithm to suggestions related to cooking. By applying different suggestion algorithms depending on the category of household chore, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input household chore category data into a generative AI, which can then analyze the data and apply different suggestion algorithms.

[0043] The proposal unit can determine the priority of proposals based on the timing of household chores. For example, the proposal unit will prioritize proposals for chores to be done in the near future. The proposal unit can also postpone proposals for chores to be done over the long term. The proposal unit can also adjust the priority of proposals according to the timing of the chores. This allows for more appropriate proposals by determining the priority of proposals based on the timing of the chores. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input data on the timing of household chores into a generative AI, and the generative AI can analyze the data to determine the priority of proposals.

[0044] The suggestion unit can adjust the order of suggestions based on the relevance of household chores when making suggestions. For example, the suggestion unit may suggest highly relevant chores consecutively. The suggestion unit may also postpone less relevant chores. The suggestion unit can also adjust the order of suggestions according to the relevance of household chores. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of household chores. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input household chore relevance data into a generative AI, and the generative AI can analyze the data and adjust the order of suggestions.

[0045] The service provider can adjust the level of detail in the instructions and tips based on the difficulty of the household chore when providing the information. For example, the service provider can provide detailed instructions for difficult household chores. For easy household chores, the service provider can also provide simplified instructions. The service provider can adjust the level of detail in the instructions and tips according to the difficulty level. This allows for the provision of more appropriate instructions and tips by adjusting the level of detail in the instructions and tips according to the difficulty level of the household chore. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input household chore difficulty data into a generative AI, and the generative AI can analyze the data to adjust the level of detail in the instructions and tips.

[0046] The service provider can apply different service provision algorithms depending on the category of household chore at the time of provision. For example, the service provider can apply a cleaning-specific algorithm to cleaning procedures. The service provider can also apply a laundry-specific algorithm to laundry procedures. The service provider can also apply a cooking-specific algorithm to cooking procedures. By applying different service provision algorithms depending on the category of household chore, more appropriate procedures and tips can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input household chore category data into a generative AI, which can then analyze the data and apply different service provision algorithms.

[0047] The service provider can adjust the order of procedures and tips based on when the household chores are performed. For example, the service provider can prioritize providing procedures for household chores to be performed in the near future. The service provider can also postpone household chores to be performed in the long term. The service provider can also adjust the order of procedures and tips according to when they are performed. By adjusting the order of procedures and tips based on when the household chores are performed, more appropriate procedures and tips can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input data on when the household chores are performed into a generating AI, and the generating AI can analyze the data and adjust the order of procedures and tips.

[0048] The service provider can improve the accuracy of procedures and tips by referring to relevant literature on household chores when providing them. For example, the service provider can provide procedures by referring to the latest research on household chores. The service provider can also provide procedures by referring to specialized books on household chores. The service provider can also provide procedures by referring to online resources on household chores. This allows for improved accuracy of procedures and tips by referring to relevant literature on household chores. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input household chore-related literature data into a generative AI, which can then analyze the data to improve the accuracy of procedures and tips.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can recognize the user's voice commands and collect data based on voice input when collecting user lifestyle data. For example, if the user gives the voice command "I'm going to start cleaning now," the analysis unit can record that time and save it in the database as the cleaning start time. Similarly, if the user gives the voice command "I'm going to start preparing dinner," the analysis unit can record that time and save it in the database as the dinner preparation time. This allows for more accurate data collection by collecting lifestyle data based on the user's voice commands. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's voice command data into a generative AI, and the generative AI can analyze the data and collect lifestyle data.

[0051] The analysis unit can utilize the user's location information to collect user lifestyle data. For example, if the user is at home, the analysis unit will prioritize collecting lifestyle data at home. If the user is at work, the analysis unit can also prioritize collecting lifestyle data at work. If the user is out, the analysis unit can also prioritize collecting lifestyle data at their location. This allows for the collection of more relevant data by utilizing the user's location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's location data into a generative AI, which can then analyze the data and collect lifestyle data.

[0052] The suggestion unit can analyze the user's past household chore history and propose an optimal household chore schedule. For example, based on the user's past household chore history, the suggestion unit can propose an optimal household chore schedule. It can also analyze the frequency and time spent on household chores performed by the user in the past and propose an efficient household chore schedule. It can also analyze the results of household chores performed by the user in the past and propose areas for improvement. In this way, by analyzing past household chore history, a more appropriate household chore schedule can be proposed. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past household chore history data into a generative AI, which can then analyze the data and propose an optimal household chore schedule.

[0053] The analysis unit can collect user lifestyle data while taking the user's health data into consideration. For example, if the user's health is poor, the analysis unit will refrain from collecting data. If the user's health is good, the analysis unit can collect detailed data. Based on the user's health data, the analysis unit can also select the optimal data collection method. This allows for more appropriate data collection by considering the user's health data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's health data into a generative AI, which can then analyze the data and collect lifestyle data.

[0054] The suggestion unit can propose priorities for household chores based on the user's lifestyle data. For example, the suggestion unit analyzes the user's lifestyle data and proposes the most important chores as a priority. Based on the user's lifestyle data, the suggestion unit can also propose an efficient order of chores. Based on the user's lifestyle data, the suggestion unit can also adjust the priorities of chores. This makes it possible to perform household chores more efficiently by proposing priorities based on the user's lifestyle data. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's lifestyle data into a generative AI, which can then analyze the data and propose priorities for household chores.

[0055] The analysis unit can collect relevant data by analyzing the user's social media activity when collecting user lifestyle data. For example, the analysis unit can collect lifestyle data based on information shared by the user on social media. The analysis unit can also collect data based on the user's interests from the user's social media activity. The analysis unit can also collect relevant data by analyzing the content of the user's social media posts. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity data into a generative AI, and the generative AI can analyze the data and collect relevant data.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The analysis unit analyzes the user's lifestyle and schedule. For example, it collects detailed data such as the user's wake-up time, commute time, meal times, and bedtime, and analyzes this data using generating AI. Step 2: The suggestion unit proposes an optimal household chore schedule and tips for efficiency based on the data analyzed by the analysis unit. For example, it suggests chores that can be done quickly in the morning or chores that should be done all at once on the weekend. The suggestion unit also uses generation AI to propose an optimal household chore schedule based on the user's lifestyle and schedule. Step 3: The provisioning department provides specific procedures and tips for particular household chores based on the household schedule proposed by the suggestion department. For example, it provides cleaning procedures, laundry tips, cooking recipes, etc. The provisioning department also uses generational AI to provide specific procedures and tips for particular household chores based on the user's lifestyle and schedule.

[0058] (Example of form 2) The household chore management system according to an embodiment of the present invention is a household chore management application that utilizes generative AI to support the daily lives of busy modern people. This household chore management system analyzes the user's lifestyle and schedule and proposes an optimal household chore schedule and tips for efficiency. It also provides specific procedures and tips for particular household chores, reducing the burden within the household. For example, the household chore management system analyzes the user's lifestyle and schedule. In this process, it collects detailed data such as the user's wake-up time, commute time, meal times, and bedtime. For example, if a user wakes up at 7 am every morning and leaves for work at 8 am, it proposes an optimal household chore schedule based on that schedule. Next, the household chore management system proposes an optimal household chore schedule and tips for efficiency based on the analyzed data. For example, it can suggest chores that can be done in a short time in the morning, or incorporate chores that should be done all at once on weekends into the schedule. This allows the user to complete household chores efficiently. Furthermore, the household chore management system provides specific procedures and tips for particular household chores. For example, it provides cleaning procedures, laundry tips, and cooking recipes. This reduces the burden of household chores and allows the user to perform household chores efficiently. This system makes it easier for users to manage their daily lives and reduces the burden on the household. For example, in dual-income households and households with young children, household chores can be prioritized and completed more efficiently. Similarly, in households with single individuals or those supporting elderly people, the burden of household chores is reduced, improving the quality of life. Thus, this household chore management system, utilizing generative AI, is a groundbreaking app that reduces the burden on the household and simplifies daily life management by analyzing the user's lifestyle and schedule, suggesting an optimal chore schedule and tips for efficiency, and providing specific procedures and tips.

[0059] The household chore management system according to this embodiment comprises an analysis unit, a proposal unit, and a provision unit. The analysis unit analyzes the user's lifestyle and schedule. The analysis unit collects detailed data such as the user's wake-up time, commute time, meal times, and bedtime. For example, if the user wakes up at 7:00 AM and leaves for work at 8:00 AM, the analysis unit proposes an optimal household chore schedule based on that schedule. The analysis unit can analyze the user's lifestyle and schedule using a generation AI. For example, the analysis unit inputs the user's lifestyle data into the generation AI, which analyzes the data and proposes an optimal household chore schedule. The proposal unit proposes an optimal household chore schedule and tips for efficiency based on the data analyzed by the analysis unit. For example, the proposal unit proposes household chores that can be done in a short amount of time during the morning. The proposal unit can propose an optimal household chore schedule based on the user's lifestyle and schedule using a generation AI. For example, the proposal unit inputs the user's lifestyle data into the generation AI, which analyzes the data and proposes an optimal household chore schedule. The proposal unit can also incorporate household chores that should be done all at once on weekends into the schedule. For example, the suggestion unit inputs the user's lifestyle data into the generation AI, which analyzes the data and suggests household chores that should be done all at once on the weekend. The provision unit provides specific procedures and tips for specific household chores based on the household chore schedule suggested by the suggestion unit. The provision unit provides, for example, cleaning procedures, laundry tips, and cooking recipes. The provision unit can use the generation AI to provide specific procedures and tips for specific household chores based on the user's lifestyle and schedule. For example, the provision unit inputs the user's lifestyle data into the generation AI, which analyzes the data and provides specific procedures and tips for specific household chores. As a result, the household chore management system according to this embodiment can reduce the burden on the household by analyzing the user's lifestyle and schedule, suggesting an optimal household chore schedule and tips for efficiency, and providing specific procedures and tips.

[0060] The analysis unit analyzes the user's lifestyle and schedule. For example, the analysis unit collects detailed data such as the user's wake-up time, commute time, meal times, and bedtime. Specifically, it utilizes data acquired from the user's smartphone or wearable device to understand the user's daily behavioral patterns in detail. This includes what activities the user engages in at what times of day, where they are, and how frequently they repeat certain actions. For example, if a user wakes up at 7 am every morning and leaves for work at 8 am, the analysis unit will suggest an optimal household chore schedule based on that schedule. The analysis unit can also analyze the user's lifestyle and schedule using generative AI. The generative AI receives the user's lifestyle data as input, learns the data patterns, and generates an optimal household chore schedule. For example, based on the user's past behavioral data, the generative AI predicts which household chores are most efficient to perform at specific times of day. This allows the analysis unit to provide a household chore schedule that is best suited to the user's lifestyle. Furthermore, the analysis unit can also adapt to changes in the user's lifestyle. For example, if a user starts a new job or moves, the household chore schedule is readjusted accordingly. This allows the analysis unit to always provide an optimal household chore schedule based on the latest information, supporting the user's life.

[0061] The suggestion department proposes optimal household chore schedules and tips for efficiency based on data analyzed by the analysis department. For example, the suggestion department suggests chores that can be done quickly in the morning. Specifically, it lists and prioritizes tasks that can be completed quickly so that users can efficiently do chores during their busy morning hours. The suggestion department can use generative AI to propose optimal household chore schedules based on the user's lifestyle and schedule. The generative AI receives user lifestyle data as input, learns data patterns, and generates an optimal household chore schedule. For example, the suggestion department inputs user lifestyle data into the generative AI, which analyzes the data and proposes an optimal household chore schedule. The suggestion department can also incorporate chores that should be done all at once on weekends into the schedule. For example, the suggestion department inputs user lifestyle data into the generative AI, which analyzes the data and proposes chores that should be done all at once on weekends. This allows users to avoid busy weekday hours and efficiently do chores on weekends. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, after a user completes a suggested household chore schedule, the results and feedback are collected, and the generating AI learns from this data and incorporates it into future suggestions. This allows the suggestion system to provide household chore schedules that are better suited to the user's needs, thereby reducing the burden on the household.

[0062] The service provider provides specific procedures and tips for particular household chores based on the household schedule proposed by the suggestion provider. For example, the service provider provides cleaning procedures, laundry tips, and cooking recipes. Specifically, it clearly explains detailed procedures and tips to enable users to efficiently perform the suggested chores. The service provider can use generative AI to provide specific procedures and tips for particular household chores based on the user's lifestyle and schedule. The generative AI receives user lifestyle data as input, learns data patterns, and generates optimal procedures and tips. For example, the service provider inputs user lifestyle data into the generative AI, which analyzes the data and provides specific procedures and tips for specific household chores. This allows users to efficiently perform the suggested chores. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its offerings. For example, after a user performs the provided procedures and tips, the service provider collects the results and feedback, and the generative AI learns from this data and incorporates it into future offerings. This allows the service provider to provide specific procedures and tips that are better suited to the user's needs, reducing the burden on the household. Furthermore, the service provider can customize procedures and tips according to the user's skill level and preferences. For example, when providing cooking recipes, they can offer simple recipes for beginners and complex recipes for advanced cooks. This allows the service provider to offer optimal procedures and tips tailored to the user's skill level and preferences, thereby reducing the burden on households.

[0063] The analysis unit can collect detailed data such as the user's wake-up time, commute time, meal times, and bedtime. For example, the analysis unit can record the user's wake-up time and save it to a database. The analysis unit can also record the user's commute time and save it to a database. The analysis unit can also record the user's meal times and save them to a database. The analysis unit can also record the user's bedtime and save it to a database. This allows for more accurate analysis by collecting detailed lifestyle data of the user. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's lifestyle data into a generative AI, which can analyze the data and propose an optimal household schedule.

[0064] The suggestion unit can suggest household chores that can be done in a short amount of time during the morning hours. For example, the suggestion unit can suggest cleaning that can be done in 5 minutes or less during the morning hours. The suggestion unit can also suggest simple cooking that can be done in a short amount of time during the morning hours. The suggestion unit can also suggest laundry that can be done in a short amount of time during the morning hours. In this way, by suggesting household chores that can be done in a short amount of time during the morning hours, it is possible to support the user in efficiently performing household chores. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the suggestion unit can input the user's lifestyle data into a generative AI, and the generative AI can analyze the data and suggest household chores that can be done in a short amount of time during the morning hours.

[0065] The suggestion unit can incorporate household chores that should be done all at once on weekends into the schedule. For example, the suggestion unit may suggest doing a thorough cleaning on the weekend. It may also suggest doing laundry on the weekend. It may also suggest preparing meals in advance on the weekend. By incorporating household chores that should be done all at once on weekends into the schedule, it can help the user manage their time. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's lifestyle data into a generative AI, which can then analyze the data and suggest household chores that should be done all at once on weekends.

[0066] The service provider can provide cleaning procedures, laundry tips, cooking recipes, and so on. For example, the service provider can provide cleaning procedures. The service provider can also provide laundry tips. The service provider can also provide cooking recipes. By providing specific procedures and tips, it can support the user in performing household chores. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's lifestyle data into a generative AI, which can then analyze the data to provide cleaning procedures, laundry tips, cooking recipes, and so on.

[0067] The analysis unit can estimate the user's emotions and adjust the timing of lifestyle data collection based on the estimated emotions. For example, if the user is stressed, the analysis unit will collect data during times when the user is relaxed. If the user is busy, the analysis unit can also collect data in a short amount of time. If the user is relaxed, the analysis unit can also collect detailed data. This allows for more appropriate data collection by adjusting the data collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI, which can analyze the data and adjust the timing of lifestyle data collection.

[0068] The analysis unit can analyze the user's past lifestyle data and select the optimal data collection method. For example, the analysis unit can select the optimal method based on the data collection methods the user has used in the past. The analysis unit can also analyze the user's past data collection history and select an efficient method. The analysis unit can also propose the optimal data collection method based on the user's lifestyle. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's past lifestyle data into a generative AI, and the generative AI can analyze the data and select the optimal data collection method.

[0069] The analysis unit can filter lifestyle data based on the user's current health status and stress level. For example, if the user's health status is poor, the analysis unit may refrain from collecting data. The analysis unit may also refrain from collecting data if the user's stress level is high. If the user's health status is good, the analysis unit may collect detailed data. This allows for more appropriate data collection by adjusting data collection according to the user's health status and stress level. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the user's health status and stress level into a generative AI, which can then analyze and filter the data.

[0070] The analysis unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the analysis unit may prioritize collecting data related to stress reduction. If the user is relaxed, the analysis unit may also prioritize collecting data related to relaxation. If the user is busy, the analysis unit may also prioritize collecting data related to efficiency. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can analyze the data and determine the priority of data to collect.

[0071] The analysis unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting lifestyle data. For example, if the user is at home, the analysis unit will prioritize the collection of data related to lifestyle habits at home. If the user is at work, the analysis unit can also prioritize the collection of data related to lifestyle habits at work. If the user is out, the analysis unit can also prioritize the collection of data related to lifestyle habits at their destination. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0072] The analysis unit can analyze a user's social media activity and collect relevant data when collecting lifestyle data. For example, the analysis unit can collect lifestyle data based on information shared by the user on social media. The analysis unit can also collect data based on the user's interests from their social media activity. The analysis unit can also analyze the content of a user's social media posts and collect relevant data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity data into a generative AI, which can then analyze the data and collect relevant data.

[0073] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will offer simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can offer more detailed suggestions. If the user is busy, the suggestion unit can offer suggestions that can be completed quickly. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then analyze the data and adjust the way suggestions are presented.

[0074] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the household chores. For example, it may suggest detailed procedures for high-importance chores, and simplified procedures for low-importance chores. The suggestion unit can also adjust the level of detail in its suggestions according to importance. By adjusting the level of detail in suggestions according to the importance of the chores, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input chore importance data into a generative AI, which can then analyze the data and adjust the level of detail in its suggestions.

[0075] The suggestion unit can apply different suggestion algorithms depending on the category of household chore when making suggestions. For example, the suggestion unit can apply a cleaning-specific algorithm to suggestions related to cleaning. The suggestion unit can also apply a laundry-specific algorithm to suggestions related to laundry. The suggestion unit can also apply a cooking-specific algorithm to suggestions related to cooking. By applying different suggestion algorithms depending on the category of household chore, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input household chore category data into a generative AI, which can then analyze the data and apply different suggestion algorithms.

[0076] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion unit can make a short suggestion. If the user is relaxed, the suggestion unit can also make a longer suggestion. If the user is busy, the suggestion unit can also make a suggestion that can be completed in a short time. By adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the length of the suggestion.

[0077] The proposal unit can determine the priority of proposals based on the timing of household chores. For example, the proposal unit will prioritize proposals for chores to be done in the near future. The proposal unit can also postpone proposals for chores to be done over the long term. The proposal unit can also adjust the priority of proposals according to the timing of the chores. This allows for more appropriate proposals by determining the priority of proposals based on the timing of the chores. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input data on the timing of household chores into a generative AI, and the generative AI can analyze the data to determine the priority of proposals.

[0078] The suggestion unit can adjust the order of suggestions based on the relevance of household chores when making suggestions. For example, the suggestion unit may suggest highly relevant chores consecutively. The suggestion unit may also postpone less relevant chores. The suggestion unit can also adjust the order of suggestions according to the relevance of household chores. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of household chores. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input household chore relevance data into a generative AI, and the generative AI can analyze the data and adjust the order of suggestions.

[0079] The service provider can estimate the user's emotions and adjust the way the procedures and tips are presented based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand procedures. If the user is relaxed, the service provider can also provide detailed procedures. If the user is busy, the service provider can also provide procedures that can be completed in a short time. By adjusting the way the procedures and tips are presented according to the user's emotions, more appropriate procedures and tips 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. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can analyze the data and adjust the way the procedures and tips are presented.

[0080] The service provider can adjust the level of detail in the instructions and tips based on the difficulty of the household chore when providing the information. For example, the service provider can provide detailed instructions for difficult household chores. For easy household chores, the service provider can also provide simplified instructions. The service provider can adjust the level of detail in the instructions and tips according to the difficulty level. This allows for the provision of more appropriate instructions and tips by adjusting the level of detail in the instructions and tips according to the difficulty level of the household chore. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input household chore difficulty data into a generative AI, and the generative AI can analyze the data to adjust the level of detail in the instructions and tips.

[0081] The service provider can apply different service provision algorithms depending on the category of household chore at the time of provision. For example, the service provider can apply a cleaning-specific algorithm to cleaning procedures. The service provider can also apply a laundry-specific algorithm to laundry procedures. The service provider can also apply a cooking-specific algorithm to cooking procedures. By applying different service provision algorithms depending on the category of household chore, more appropriate procedures and tips can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input household chore category data into a generative AI, which can then analyze the data and apply different service provision algorithms.

[0082] The service provider can estimate the user's emotions and prioritize the procedures and tips to be provided based on the estimated emotions. For example, if the user is feeling stressed, the service provider will prioritize providing stress-reducing procedures. If the user is relaxed, the service provider may also prioritize providing relaxation procedures. If the user is busy, the service provider may also prioritize providing efficiency procedures. This allows for the provision of more appropriate procedures and tips by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can then analyze the data to determine the priority of procedures and tips.

[0083] The service provider can adjust the order of procedures and tips based on when the household chores are performed. For example, the service provider can prioritize providing procedures for household chores to be performed in the near future. The service provider can also postpone household chores to be performed in the long term. The service provider can also adjust the order of procedures and tips according to when they are performed. By adjusting the order of procedures and tips based on when the household chores are performed, more appropriate procedures and tips can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input data on when the household chores are performed into a generating AI, and the generating AI can analyze the data and adjust the order of procedures and tips.

[0084] The service provider can improve the accuracy of procedures and tips by referring to relevant literature on household chores when providing them. For example, the service provider can provide procedures by referring to the latest research on household chores. The service provider can also provide procedures by referring to specialized books on household chores. The service provider can also provide procedures by referring to online resources on household chores. This allows for improved accuracy of procedures and tips by referring to relevant literature on household chores. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input household chore-related literature data into a generative AI, which can then analyze the data to improve the accuracy of procedures and tips.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The analysis unit can recognize the user's voice commands and collect data based on voice input when collecting user lifestyle data. For example, if the user gives the voice command "I'm going to start cleaning now," the analysis unit can record that time and save it in the database as the cleaning start time. Similarly, if the user gives the voice command "I'm going to start preparing dinner," the analysis unit can record that time and save it in the database as the dinner preparation time. This allows for more accurate data collection by collecting lifestyle data based on the user's voice commands. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's voice command data into a generative AI, and the generative AI can analyze the data and collect lifestyle data.

[0087] The suggestion unit can estimate the user's emotions and adjust the suggested household chores based on those emotions. For example, if the user is stressed, the suggestion unit can suggest relaxing chores. If the user is relaxed, the suggestion unit can also suggest chores that can be done efficiently. If the user is busy, the suggestion unit can also suggest chores that can be completed in a short time. By adjusting the suggested chores according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then analyze the data and adjust the suggested household chores.

[0088] The service provider can estimate the user's emotions and adjust the way it provides household chore instructions and tips based on those estimated emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand instructions. If the user is relaxed, the service provider can also provide detailed instructions. If the user is busy, the service provider can also provide instructions that can be completed in a short time. By adjusting the way instructions and tips are provided according to the user's emotions, more appropriate instructions and tips 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. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can analyze the data and adjust the way instructions and tips are provided.

[0089] The analysis unit can utilize the user's location information to collect user lifestyle data. For example, if the user is at home, the analysis unit will prioritize collecting lifestyle data at home. If the user is at work, the analysis unit can also prioritize collecting lifestyle data at work. If the user is out, the analysis unit can also prioritize collecting lifestyle data at their location. This allows for the collection of more relevant data by utilizing the user's location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's location data into a generative AI, which can then analyze the data and collect lifestyle data.

[0090] The suggestion unit can analyze the user's past household chore history and propose an optimal household chore schedule. For example, based on the user's past household chore history, the suggestion unit can propose an optimal household chore schedule. It can also analyze the frequency and time spent on household chores performed by the user in the past and propose an efficient household chore schedule. It can also analyze the results of household chores performed by the user in the past and propose areas for improvement. In this way, by analyzing past household chore history, a more appropriate household chore schedule can be proposed. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past household chore history data into a generative AI, which can then analyze the data and propose an optimal household chore schedule.

[0091] The service provider can estimate the user's emotions and adjust the order in which it provides household chore procedures and tips based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing stress-reducing procedures. If the user is relaxed, the service provider may also prioritize providing relaxation procedures. If the user is busy, the service provider may also prioritize providing efficiency procedures. By adjusting the order in which procedures and tips are provided according to the user's emotions, more appropriate procedures and tips 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. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can analyze the data and adjust the order in which procedures and tips are provided.

[0092] The analysis unit can collect user lifestyle data while taking the user's health data into consideration. For example, if the user's health is poor, the analysis unit will refrain from collecting data. If the user's health is good, the analysis unit can collect detailed data. Based on the user's health data, the analysis unit can also select the optimal data collection method. This allows for more appropriate data collection by considering the user's health data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's health data into a generative AI, which can then analyze the data and collect lifestyle data.

[0093] The suggestion unit can propose priorities for household chores based on the user's lifestyle data. For example, the suggestion unit analyzes the user's lifestyle data and proposes the most important chores as a priority. Based on the user's lifestyle data, the suggestion unit can also propose an efficient order of chores. Based on the user's lifestyle data, the suggestion unit can also adjust the priorities of chores. This makes it possible to perform household chores more efficiently by proposing priorities based on the user's lifestyle data. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's lifestyle data into a generative AI, which can then analyze the data and propose priorities for household chores.

[0094] The service provider can estimate the user's emotions and adjust the frequency of providing household chore instructions and tips based on the estimated emotions. For example, if the user is stressed, the service provider will frequently provide stress-reducing instructions. If the user is relaxed, the service provider may reduce the frequency of providing relaxation instructions. If the user is busy, the service provider may frequently provide efficiency-related instructions. By adjusting the frequency of instructions and tips according to the user's emotions, more appropriate instructions and tips 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. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can analyze the data and adjust the frequency of providing instructions and tips.

[0095] The analysis unit can collect relevant data by analyzing the user's social media activity when collecting user lifestyle data. For example, the analysis unit can collect lifestyle data based on information shared by the user on social media. The analysis unit can also collect data based on the user's interests from the user's social media activity. The analysis unit can also collect relevant data by analyzing the content of the user's social media posts. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity data into a generative AI, and the generative AI can analyze the data and collect relevant data.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The analysis unit analyzes the user's lifestyle and schedule. For example, it collects detailed data such as the user's wake-up time, commute time, meal times, and bedtime, and analyzes this data using generating AI. Step 2: The suggestion unit proposes an optimal household chore schedule and tips for efficiency based on the data analyzed by the analysis unit. For example, it suggests chores that can be done quickly in the morning or chores that should be done all at once on the weekend. The suggestion unit also uses generation AI to propose an optimal household chore schedule based on the user's lifestyle and schedule. Step 3: The provisioning department provides specific procedures and tips for particular household chores based on the household schedule proposed by the suggestion department. For example, it provides cleaning procedures, laundry tips, cooking recipes, etc. The provisioning department also uses generational AI to provide specific procedures and tips for particular household chores based on the user's lifestyle and schedule.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's lifestyle and schedule. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes an optimal household chore schedule and tips for efficiency based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart device 14, and provides specific procedures and tips for specific household chores. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's lifestyle and schedule. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes an optimal household chore schedule and tips for efficiency based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart glasses 214, and provides specific procedures and tips for specific household chores. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the analysis unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's lifestyle and schedule. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes an optimal household chore schedule and tips for efficiency based on the analyzed data. The provision unit is implemented by the control unit 46A of the headset terminal 314, and provides specific procedures and tips for specific household chores. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the analysis unit, proposal unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's lifestyle and schedule. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes an optimal household chore schedule and tips for efficiency based on the analyzed data. The provision unit is implemented by the control unit 46A of the robot 414, and provides specific procedures and tips for specific household chores. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) An analysis unit that analyzes the user's lifestyle and schedule, Based on the data analyzed by the aforementioned analysis unit, the proposal unit suggests an optimal household chore schedule and tips for improving efficiency. The system includes a provisioning unit that provides specific procedures and tips for particular household chores based on the household chore schedule proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Collect detailed data on users, such as their wake-up time, commute time, meal times, and bedtime. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, I suggest household chores that can be done in a short amount of time during the morning hours. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Schedule household chores that should be done all at once on the weekend. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide cleaning procedures, laundry tips, cooking recipes, and more. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the timing of lifestyle data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We analyze users' past lifestyle data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When collecting lifestyle data, filtering is performed based on the user's current health status and stress level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When collecting lifestyle data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When collecting lifestyle data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the household chore. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of household chore. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned proposal section is, When making proposals, prioritize them based on when household chores are performed. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of household chores. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the instructions and tips provided are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the information, we adjust the level of detail in the instructions and tips based on the difficulty level of the household chore. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing services, different service provision algorithms are applied depending on the category of household chore. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the steps and tips to provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, adjust the order of the steps and tips based on when the household chores are performed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing this service, we refer to relevant literature on household chores to improve the accuracy of procedures and tips. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 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. An analysis unit that analyzes the user's lifestyle and schedule, Based on the data analyzed by the aforementioned analysis unit, the proposal unit suggests an optimal household chore schedule and tips for improving efficiency. The system includes a provisioning unit that provides specific procedures and tips for particular household chores based on the household chore schedule proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit, Collect detailed data on users, such as their wake-up time, commute time, meal times, and bedtime. The system according to feature 1.

3. The aforementioned proposal section is, I suggest household chores that can be done in a short amount of time during the morning hours. The system according to feature 1.

4. The aforementioned proposal section is, Schedule household chores that should be done all at once on the weekend. The system according to feature 1.

5. The aforementioned supply unit is, We provide cleaning procedures, laundry tips, cooking recipes, and more. The system according to feature 1.

6. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the timing of lifestyle data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned analysis unit, We analyze users' past lifestyle data and select the optimal data collection method. The system according to feature 1.

8. The aforementioned analysis unit, When collecting lifestyle data, filtering is performed based on the user's current health status and stress level. The system according to feature 1.

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