system

Abstract

The system according to the embodiment aims to detect resident slacking or lethargy and improve their motivation. [Solution] A system according to an embodiment includes a collection unit, a determination unit, and a provision unit. The collection unit collects the resident's smartphone usage history or the operation status of home appliances. The determination unit analyzes the data collected by the collection unit and determines whether the resident is "slacking" or "lethargic." The provision unit provides words to improve motivation based on the determination result obtained by the determination unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of making it difficult to effectively detect resident slacking or lethargy and take appropriate measures.

[0005] The system according to the embodiment aims to detect resident slacking or lethargy and improve their motivation. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a determination unit, and a provision unit. The collection unit collects the resident's smartphone usage history or the operation status of home appliances. The determination unit analyzes the data collected by the collection unit and determines whether the resident is "slacking" or "lethargic." The provision unit provides words to improve motivation based on the determination result obtained by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect resident slacking or lethargy and improve their motivation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A housework promotion system according to an embodiment of the present invention uses AI to analyze a resident's smartphone usage history and the operation status of home appliances, and detects whether the resident is slacking or lacking motivation. This housework promotion system encourages housework by using motivational words to encourage the resident when the resident has been using their smartphone for a long time or when the home appliances have not been used for a certain period of time. For example, if a resident has been using their smartphone for a long time, the AI ​​displays a message such as "Why don't you take a break and start doing some housework?". Furthermore, if the home appliances have not been used for a certain period of time, the AI ​​sends a reminder such as "It's time to vacuum." This system allows the resident to smartly perform housework and maintain the cleanliness of their room. For example, if a resident has been using their smartphone for a long time, the AI ​​displays a message such as "Why don't you take a break and start doing some housework?". Furthermore, if the home appliances have not been used for a certain period of time, the AI ​​sends a reminder such as "It's time to vacuum." This allows the resident to smartly perform housework and maintain the cleanliness of their room. This allows the housework promotion system to analyze residents' smartphone usage history and the operation status of home appliances, detect when residents are slacking off or feeling lethargic, and provide words of motivation to help residents complete their housework smarter.

[0029] A housework promotion system according to an embodiment includes a collection unit, a determination unit, and a provision unit. The collection unit collects data on a resident's smartphone usage history or the operation status of home appliances. The collection unit collects data such as smartphone usage time, frequency of use of specific apps, and operation time and frequency of use of home appliances. For example, the collection unit can collect smartphone usage time as total usage time per day or usage time during specific time periods. The collection unit can also collect frequency of use of specific apps as usage frequency of social networking apps, game apps, business apps, etc. The collection unit can also collect operation time of home appliances as operation time of air conditioners or television viewing time. The determination unit analyzes the collected data and determines whether the resident is "slacking" or "lethargic." For example, the determination unit determines that the resident is "slacking" or "lethargic" if the resident spends a long time using their smartphone or if the home appliances have not been used for a certain period of time. For example, the determination unit may determine that a resident is slacking or listless if the resident has used their smartphone for more than one hour or if the home appliances have not been used for more than one day. The provision unit may provide motivational words based on the determination result. The provision unit may send messages to the resident, such as, "Why don't you take a short break and start doing some housework?" or "It's time to vacuum." For example, the provision unit may send the message "Why don't you take a short break and start doing some housework?" after a long period of inactivity or at a specific time of day. The provision unit may also send the message "It's time to vacuum" if the vacuum cleaner has not been used for a certain period of time. As a result, the housework promotion system according to the embodiment analyzes the resident's smartphone usage history and the operation status of the home appliances, detects the resident's slacking or listlessness, and provides motivational words to help the resident do their housework smartly. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the determination unit can input the collected data and make a determination using an AI model that determines whether a resident is "slacking" or "lethargic." Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI.For example, the providing unit can provide a message using an AI model that takes the judgment result as input and generates words that will increase motivation.

[0030] The collection unit can collect data on smartphone usage time or usage frequency of specific apps, and operating time or usage frequency of home appliances. For example, the collection unit can collect smartphone usage time as total usage time in a day or usage time during a specific time period. The collection unit can also collect usage frequency of specific apps as usage frequency of social networking apps, game apps, business apps, etc. For example, the collection unit can collect usage frequency of social networking apps as the number of times used per day or the number of times used per week. The collection unit can also collect usage frequency of game apps as usage time per day or usage time during a specific time period. The collection unit can also collect usage frequency of business apps as the number of times used per day or the number of times used after a specific event. The collection unit can collect operating time of home appliances as operating time of air conditioners or television viewing time. For example, the collection unit can collect operating time of air conditioners as operating time per day or operating time during a specific time period. The collection unit can also collect television viewing time as viewing time per day or viewing time for specific programs. As a result, the collection unit can understand the behavior of residents in detail by collecting data such as smartphone usage time, frequency of use of specific apps, and operating time and frequency of use of home appliances. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that collects data using smartphone usage time and frequency of use of specific apps as input.

[0031] The determination unit analyzes the collected data and can determine that the resident is "slacking" or "lethargic" if the smartphone is used for a long period of time or if the home appliances have not been operated for a certain period of time. For example, the determination unit can determine that the resident is "slacking" or "lethargic" if the smartphone is used for more than one hour or if the home appliances have not been operated for more than one day. For example, the determination unit can determine that the resident is "slacking" or "lethargic" if the smartphone is used for more than three hours or if the home appliances have not been operated for more than three days. Furthermore, the determination unit can determine that the resident is "slacking" or "lethargic" if the smartphone is used for more than five hours or if the home appliances have not been operated for more than one week. This allows the determination unit to analyze the collected data and accurately determine whether the resident is "slacking" or "lethargic." Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the judgment unit can input collected data and make a judgment using an AI model that determines whether a resident is "slacking" or "lethargic."

[0032] The providing unit can send a message to the resident, such as "Why don't you take a short break and start doing some housework?" or "It's time to vacuum," based on the determination result. For example, the providing unit can send the message "Why don't you take a short break and start doing some housework?" after a long period of inactivity or at a specific time of day. The providing unit can also send the message "It's time to vacuum" if the vacuum cleaner has not been used for a certain period of time. For example, the providing unit can send the message "Why don't you take a short break and start doing some housework?" if the resident has been using their smartphone for a long period of time. The providing unit can also send the message "It's time to vacuum" if the home appliance has not been used for a certain period of time. In this way, the providing unit can send an appropriate message to the resident based on the determination result, thereby increasing the resident's motivation and encouraging them to do their housework. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can provide the message using an AI model that uses the determination result as input and generates motivational words.

[0033] The collection unit can analyze the resident's past smartphone usage history and the operation status of home appliances and select an appropriate collection method. For example, the collection unit can prioritize collecting data on apps that the resident used frequently in the past. The collection unit can also focus on collecting operation data on home appliances that the resident used for long periods of time in the past. The collection unit can also analyze the resident's past usage patterns and select the most efficient collection method. For example, the collection unit can analyze the resident's usage history for the past week and select the optimal collection method. The collection unit can also analyze the resident's usage history for the past month and select the optimal collection method. In this way, the collection unit can select the optimal collection method by analyzing the resident's past usage history and efficiently collect data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can collect data using an AI model that inputs the resident's past smartphone usage history and the operation status of home appliances and selects the optimal collection method.

[0034] When collecting data, the collection unit can filter the data based on the resident's current living situation and areas of interest. For example, the collection unit can prioritize collecting data on areas in which the resident is currently interested. The collection unit can also collect only necessary data depending on the resident's living situation. Furthermore, the collection unit can filter and collect highly relevant data based on the resident's areas of interest. For example, the collection unit can prioritize collecting data related to hobbies in which the resident is currently interested. The collection unit can also collect only necessary data depending on the resident's living situation, such as whether the resident has a job or not and their family situation. Furthermore, the collection unit can filter and collect highly relevant data based on the resident's areas of interest. In this way, the collection unit can collect highly relevant data by filtering data based on the resident's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that inputs the resident's current living situation and areas of interest and filters the data.

[0035] When collecting data, the collection unit can prioritize collecting highly relevant data based on the resident's geographical location information. For example, when the resident is at home, the collection unit can prioritize collecting the operation status of home appliances in the home. Furthermore, when the resident is out, the collection unit can prioritize collecting smartphone usage history. Furthermore, when the resident is in a specific location, the collection unit can prioritize collecting data related to the location. For example, when the resident is at home, the collection unit can prioritize collecting the number of times the refrigerator is opened and closed and the number of times the washing machine is used. Furthermore, when the resident is out, the collection unit can prioritize collecting smartphone browsing history and call history. Furthermore, when the resident is in a specific location, the collection unit can prioritize collecting event information related to the location and information about nearby facilities. In this way, the collection unit can prioritize collecting highly relevant data by taking the resident's geographical location information into consideration. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can collect data using an AI model that takes residents' geographic location information as input and prioritizes the collection of highly relevant data.

[0036] The collection unit may analyze the resident's social media activity during data collection to collect relevant data. For example, the collection unit may collect relevant data based on the content frequently posted by the resident on social media. The collection unit may also adjust the timing of data collection based on the resident's social media activity time. The collection unit may also collect relevant data based on the resident's social media interests. For example, the collection unit may collect data related to the resident's hobbies and topics of interest based on the content frequently posted by the resident on social media. The collection unit may also adjust the timing of data collection based on the resident's social media activity time. The collection unit may also collect relevant data based on the resident's social media interest time. In this way, the collection unit can efficiently collect relevant data by analyzing the resident's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect data using an AI model that uses the resident's social media activity as an input and collects relevant data.

[0037] The determination unit can improve the accuracy of the determination based on the correlation of collected data during the determination. The determination unit can, for example, analyze the correlation between smartphone usage time and the operating status of home appliances to make a highly accurate determination. The determination unit can also analyze the correlation between the frequency of use of a specific app and the operating status of home appliances to make a highly accurate determination. The determination unit can also analyze the correlation between a resident's lifestyle pattern and data to make a highly accurate determination. For example, the determination unit can analyze the correlation between smartphone usage time and the operating status of home appliances to accurately determine whether the resident is "slacking" or "lethargy." The determination unit can also analyze the correlation between the frequency of use of a specific app and the operating status of home appliances to accurately determine whether the resident is "slacking" or "lethargy." The determination unit can also analyze the correlation between a resident's lifestyle pattern and data to accurately determine whether the resident is "slacking" or "lethargy." As a result, the determination unit can improve the accuracy of the determination by taking into account the correlation of collected data. Some or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or without AI. For example, the judgment unit can make a judgment using an AI model that takes the correlations between collected data as input and improves the accuracy of the judgment.

[0038] The determination unit can determine whether a resident is "slacking" or "lethargic" based on the resident's attribute information during the determination. The determination unit can adjust the determination criteria based on, for example, the resident's age and gender. The determination unit can also adjust the determination criteria based on the resident's occupation and lifestyle. The determination unit can also adjust the determination criteria based on the resident's health condition and past behavioral history. For example, the determination unit can determine whether a resident is "slacking" or "lethargic" based on the resident's age and gender. The determination unit can also determine whether a resident is "slacking" or "lethargic" based on the resident's occupation and lifestyle. The determination unit can also determine whether a resident is "slacking" or "lethargic" based on the resident's health condition and past behavioral history. This allows the determination unit to make a more appropriate determination by taking the resident's attribute information into consideration. Some or all of the above-described processing in the determination unit may be performed, for example, using AI or without AI. For example, the judgment unit can input the resident's attribute information and make a judgment using an AI model that determines whether the resident is "slacking" or "lethargic."

[0039] At the time of the determination, the determination unit can determine whether the resident is "slacking" or "lethargy" based on the geographical distribution of the resident. For example, the determination unit can determine whether the resident is slacking off on housework when the resident is at home. Furthermore, the determination unit can determine whether the resident is lethargic when the resident is out. Furthermore, the determination unit can make a determination based on behavior related to a specific location when the resident is in that location. For example, the determination unit can determine whether the resident is slacking off on housework based on the operation status of home appliances when the resident is at home. Furthermore, the determination unit can determine whether the resident is lethargic based on the smartphone usage history when the resident is out. Furthermore, the determination unit can determine whether the resident is "slacking off" or "lethargy" based on behavior related to that location when the resident is in a specific location. This allows the determination unit to make a more appropriate determination by taking the geographical distribution of the resident into consideration. Some or all of the above-described processing by the determination unit may be performed, for example, using AI or without AI. For example, the judgment unit can input the geographical distribution of residents and make a judgment using an AI model that determines whether a person is "slacking off" or "lethargic."

[0040] The determination unit can improve the accuracy of the determination based on related literature when making the determination. The determination unit can, for example, refer to the latest research papers and update the determination criteria. The determination unit can also improve the determination algorithm based on related academic literature. The determination unit can also improve the accuracy of the determination by referring to specialized books. For example, the determination unit can update the criteria for determining whether a resident is "slacking" or "lethargy" by referring to the latest research papers. The determination unit can also improve the algorithm for determining whether a resident is "slacking" or "lethargy" by referring to related academic literature. The determination unit can also improve the accuracy of determining whether a resident is "slacking" or "lethargy" by referring to specialized books. As a result, the determination unit improves the accuracy of the determination by referring to related literature. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can make a determination using an AI model that uses related literature as input and improves the accuracy of the determination.

[0041] When providing a message, the providing unit can select an appropriate message by analyzing the resident's past response history. For example, the providing unit can prioritize providing messages to which the resident responded favorably in the past. The providing unit can also select the most effective message based on the resident's past response history. Furthermore, the providing unit can analyze the resident's response pattern and provide the optimal message. For example, the providing unit can prioritize providing messages to which the resident responded favorably in the past. The providing unit can also select the most effective message based on the resident's past response history. Furthermore, the providing unit can analyze the resident's response pattern and provide the optimal message. In this way, the providing unit can select the optimal message and provide it effectively by analyzing the resident's past response history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide a message using an AI model that inputs the resident's past response history and selects the optimal message.

[0042] The providing unit can customize the content of the message based on the resident's current living situation when providing the message. For example, if the resident is busy, the providing unit can provide a short and to-the-point message. Furthermore, if the resident is relaxed, the providing unit can provide a message including detailed explanations. Furthermore, if the resident is tired, the providing unit can provide a message including words of encouragement. In this way, the providing unit can provide a more effective message by customizing the content of the message based on the resident's current living situation. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide a message using an AI model that inputs the resident's current living situation and customizes the content of the message.

[0043] The providing unit can provide an appropriate message based on the resident's geographical location information at the time of providing. For example, if the resident is at home, the providing unit can provide a message regarding housework to be done at home. Furthermore, if the resident is out, the providing unit can provide a message regarding housework to be done after returning home. Furthermore, if the resident is in a specific location, the providing unit can provide a message related to the location. For example, if the resident is at home, the providing unit can provide a message regarding the time to vacuum or do laundry. Furthermore, if the resident is out, the providing unit can provide a reminder regarding housework to be done after returning home. Furthermore, if the resident is in a specific location, the providing unit can provide a message related to the location. In this way, the providing unit can provide an optimal message by taking the resident's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide a message using an AI model that inputs the resident's geographical location information and provides an appropriate message.

[0044] The providing unit may adjust the content of the message based on the resident's social media activity when providing the message. For example, the providing unit may adjust the message based on the content frequently posted by the resident on social media. The providing unit may also adjust the timing of the message based on the resident's social media activity time. Furthermore, the providing unit may adjust the content of the message based on the resident's social media interests. For example, the providing unit may provide a message related to a hobby or topic of interest based on the content frequently posted by the resident on social media. The providing unit may also adjust the timing of the message based on the resident's social media activity time. Furthermore, the providing unit may provide a relevant message based on the resident's social media interests. In this way, the providing unit can efficiently provide relevant messages by analyzing the resident's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide a message using an AI model that uses the resident's social media activity as input and adjusts the content of the message.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The housework promotion system can also collect schedule data from residents and suggest the timing of housework based on the schedule. For example, the collection unit can collect schedule data from the resident's calendar app or planner, and the determination unit can analyze this data to determine the optimal timing for housework. The provision unit can send messages such as "It would be efficient to clean at this time" during times when the resident is free. Also, if the resident is busy, it can send messages such as "Do the housework at the next available time." This makes it possible to suggest efficient housework based on the resident's schedule.

[0047] The housework promotion system can further suggest how to divide up housework based on the resident's family structure. For example, the collection unit can collect family structure data about the resident, and the determination unit can analyze this data to suggest how to divide up housework. The provision unit can suggest housework that all family members can participate in and send a message such as "Let's clean up together as a family." It can also suggest housework that is suitable for a specific family member and send a message such as "Why not ask your child to help out?" This allows all family members to work together to do housework.

[0048] The housework promotion system can further analyze the resident's past housework history and suggest efficient housework methods. For example, the collection unit collects the resident's past housework history data, and the determination unit analyzes this data to suggest efficient housework methods. The provision unit can send a message such as "Cleaning in this way is efficient" based on the housework methods that the resident has used successfully in the past. It can also send a message such as "Avoid this method" to encourage the resident to avoid housework methods that have failed in the past. This allows the resident to perform housework efficiently.

[0049] The housework promotion system can further suggest housework tasks based on the resident's geographical location information. For example, the collection unit can collect the resident's geographical location information, and the determination unit can analyze this data to suggest housework tasks based on the resident's current location. The provision unit can send a message such as "Let's clean up now" when the resident is at home, or a message such as "Let's do the laundry after we get home" when the resident is out. This makes it possible to suggest appropriate housework tasks based on the resident's current location.

[0050] The housework promotion system can further analyze the social media activities of residents and suggest housework based on their responses on social media. For example, the collection unit collects social media activity data of residents, and the determination unit analyzes this data to suggest housework that the residents are interested in on social media. If the resident posts on social media that "cleaning is fun," the provision unit can send a message such as "Let's clean." Or, if the resident posts that "I like cooking," the provision unit can send a message such as "Let's try a new recipe." This makes it possible to suggest housework based on the residents' social media activities.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The collection unit collects the resident's smartphone usage history or the operation status of home appliances. For example, it collects data such as smartphone usage time, frequency of use of specific apps, and operation time and frequency of use of home appliances. Specifically, it collects smartphone usage time as total usage time per day or usage time during specific time periods, and collects frequency of use of specific apps as frequency of use of social networking apps, game apps, business apps, etc. It also collects operation time of home appliances as air conditioner operation time and television viewing time. Step 2: The judgment unit analyzes the collected data and determines whether the resident is "slacking" or "lethargic." For example, if a resident uses their smartphone for a long period of time or if their home appliances have not been used for a certain period of time, it will determine that the resident is "slacking" or "lethargic." Specifically, if a resident uses their smartphone for more than an hour or if their home appliances have not been used for more than a day, it can determine that the resident is "slacking" or "lethargic." The processing in the judgment unit can sometimes be performed using AI, and an AI model can be used that uses the collected data as input and determines whether the resident is "slacking" or "lethargic." Step 3: The provider provides motivational words based on the assessment results. For example, it sends messages such as "Why don't you take a short break and start doing some housework?" or "It's time to vacuum" to the resident. Specifically, it can send the message "Why don't you take a short break and start doing some housework?" after a long period of inactivity or at a specific time of day, and send the message "It's time to vacuum" if the vacuum cleaner has not been used for a certain period of time. The processing in the provider can be performed using AI, and an AI model can be used that uses the assessment results as input and generates motivational words.

[0053] (Example 2) A housework promotion system according to an embodiment of the present invention uses AI to analyze a resident's smartphone usage history and the operation status of home appliances, and detects whether the resident is slacking or lacking motivation. This housework promotion system encourages housework by using motivational words to encourage the resident when the resident has been using their smartphone for a long time or when the home appliances have not been used for a certain period of time. For example, if a resident has been using their smartphone for a long time, the AI ​​displays a message such as "Why don't you take a break and start doing some housework?". Furthermore, if the home appliances have not been used for a certain period of time, the AI ​​sends a reminder such as "It's time to vacuum." This system allows the resident to smartly perform housework and maintain the cleanliness of their room. For example, if a resident has been using their smartphone for a long time, the AI ​​displays a message such as "Why don't you take a break and start doing some housework?". Furthermore, if the home appliances have not been used for a certain period of time, the AI ​​sends a reminder such as "It's time to vacuum." This allows the resident to smartly perform housework and maintain the cleanliness of their room. This allows the housework promotion system to analyze residents' smartphone usage history and the operation status of home appliances, detect when residents are slacking off or feeling lethargic, and provide words of motivation to help residents complete their housework smarter.

[0054] A housework promotion system according to an embodiment includes a collection unit, a determination unit, and a provision unit. The collection unit collects data on a resident's smartphone usage history or the operation status of home appliances. The collection unit collects data such as smartphone usage time, frequency of use of specific apps, and operation time and frequency of use of home appliances. For example, the collection unit can collect smartphone usage time as total usage time per day or usage time during specific time periods. The collection unit can also collect frequency of use of specific apps as usage frequency of social networking apps, game apps, business apps, etc. The collection unit can also collect operation time of home appliances as operation time of air conditioners or television viewing time. The determination unit analyzes the collected data and determines whether the resident is "slacking" or "lethargic." For example, the determination unit determines that the resident is "slacking" or "lethargic" if the resident spends a long time using their smartphone or if the home appliances have not been used for a certain period of time. For example, the determination unit may determine that a resident is slacking or listless if the resident has used their smartphone for more than one hour or if the home appliances have not been used for more than one day. The provision unit may provide motivational words based on the determination result. The provision unit may send messages to the resident, such as, "Why don't you take a short break and start doing some housework?" or "It's time to vacuum." For example, the provision unit may send the message "Why don't you take a short break and start doing some housework?" after a long period of inactivity or at a specific time of day. The provision unit may also send the message "It's time to vacuum" if the vacuum cleaner has not been used for a certain period of time. As a result, the housework promotion system according to the embodiment analyzes the resident's smartphone usage history and the operation status of the home appliances, detects the resident's slacking or listlessness, and provides motivational words to help the resident do their housework smartly. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the determination unit can input the collected data and make a determination using an AI model that determines whether a resident is "slacking" or "lethargic." Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI.For example, the providing unit can provide a message using an AI model that takes the judgment result as input and generates words that will increase motivation.

[0055] The collection unit can collect data on smartphone usage time or usage frequency of specific apps, and operating time or usage frequency of home appliances. For example, the collection unit can collect smartphone usage time as total usage time in a day or usage time during a specific time period. The collection unit can also collect usage frequency of specific apps as usage frequency of social networking apps, game apps, business apps, etc. For example, the collection unit can collect usage frequency of social networking apps as the number of times used per day or the number of times used per week. The collection unit can also collect usage frequency of game apps as usage time per day or usage time during a specific time period. The collection unit can also collect usage frequency of business apps as the number of times used per day or the number of times used after a specific event. The collection unit can collect operating time of home appliances as operating time of air conditioners or television viewing time. For example, the collection unit can collect operating time of air conditioners as operating time per day or operating time during a specific time period. The collection unit can also collect television viewing time as viewing time per day or viewing time for specific programs. As a result, the collection unit can understand the behavior of residents in detail by collecting data such as smartphone usage time, frequency of use of specific apps, and operating time and frequency of use of home appliances. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that collects data using smartphone usage time and frequency of use of specific apps as input.

[0056] The determination unit analyzes the collected data and can determine that the resident is "slacking" or "lethargic" if the smartphone is used for a long period of time or if the home appliances have not been operated for a certain period of time. For example, the determination unit can determine that the resident is "slacking" or "lethargic" if the smartphone is used for more than one hour or if the home appliances have not been operated for more than one day. For example, the determination unit can determine that the resident is "slacking" or "lethargic" if the smartphone is used for more than three hours or if the home appliances have not been operated for more than three days. Furthermore, the determination unit can determine that the resident is "slacking" or "lethargic" if the smartphone is used for more than five hours or if the home appliances have not been operated for more than one week. This allows the determination unit to analyze the collected data and accurately determine whether the resident is "slacking" or "lethargic." Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the judgment unit can input collected data and make a judgment using an AI model that determines whether a resident is "slacking" or "lethargic."

[0057] The providing unit can send a message to the resident, such as "Why don't you take a short break and start doing some housework?" or "It's time to vacuum," based on the determination result. For example, the providing unit can send the message "Why don't you take a short break and start doing some housework?" after a long period of inactivity or at a specific time of day. The providing unit can also send the message "It's time to vacuum" if the vacuum cleaner has not been used for a certain period of time. For example, the providing unit can send the message "Why don't you take a short break and start doing some housework?" if the resident has been using their smartphone for a long period of time. The providing unit can also send the message "It's time to vacuum" if the home appliance has not been used for a certain period of time. In this way, the providing unit can send an appropriate message to the resident based on the determination result, thereby increasing the resident's motivation and encouraging them to do their housework. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can provide the message using an AI model that uses the determination result as input and generates motivational words.

[0058] The collection unit can estimate the resident's emotions and adjust the timing of data collection based on the estimated resident's emotions. For example, if the resident is stressed, the collection unit can reduce data collection and collect data when the resident is relaxed. Furthermore, if the resident is relaxed, the collection unit can actively collect data and collect detailed data. Furthermore, if the resident is busy, the collection unit can minimize data collection and collect data when the resident has time. This allows the collection unit to adjust the timing of data collection according to the resident's emotions, thereby collecting data at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input data for estimating the resident's emotions and adjust the timing of data collection using an AI model for emotion estimation.

[0059] The collection unit can analyze the resident's past smartphone usage history and the operation status of home appliances and select an appropriate collection method. For example, the collection unit can prioritize collecting data on apps that the resident used frequently in the past. The collection unit can also focus on collecting operation data on home appliances that the resident used for long periods of time in the past. The collection unit can also analyze the resident's past usage patterns and select the most efficient collection method. For example, the collection unit can analyze the resident's usage history for the past week and select the optimal collection method. The collection unit can also analyze the resident's usage history for the past month and select the optimal collection method. In this way, the collection unit can select the optimal collection method by analyzing the resident's past usage history and efficiently collect data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can collect data using an AI model that inputs the resident's past smartphone usage history and the operation status of home appliances and selects the optimal collection method.

[0060] When collecting data, the collection unit can filter the data based on the resident's current living situation and areas of interest. For example, the collection unit can prioritize collecting data on areas in which the resident is currently interested. The collection unit can also collect only necessary data depending on the resident's living situation. Furthermore, the collection unit can filter and collect highly relevant data based on the resident's areas of interest. For example, the collection unit can prioritize collecting data related to hobbies in which the resident is currently interested. The collection unit can also collect only necessary data depending on the resident's living situation, such as whether the resident has a job or not and their family situation. Furthermore, the collection unit can filter and collect highly relevant data based on the resident's areas of interest. In this way, the collection unit can collect highly relevant data by filtering data based on the resident's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that inputs the resident's current living situation and areas of interest and filters the data.

[0061] The collection unit can estimate the resident's emotions and determine the priority of data to be collected based on the estimated resident's emotions. For example, if the resident is feeling stressed, the collection unit can prioritize collecting data related to relaxation. Furthermore, if the resident is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, if the resident is busy, the collection unit can prioritize collecting only important data. In this way, the collection unit can prioritize collecting important data by determining the priority of data to be collected according to the resident's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data for estimating the resident's emotions and determine the priority of data to be collected using an AI model for emotion estimation.

[0062] When collecting data, the collection unit can prioritize collecting highly relevant data based on the resident's geographical location information. For example, when the resident is at home, the collection unit can prioritize collecting the operation status of home appliances in the home. Furthermore, when the resident is out, the collection unit can prioritize collecting smartphone usage history. Furthermore, when the resident is in a specific location, the collection unit can prioritize collecting data related to the location. For example, when the resident is at home, the collection unit can prioritize collecting the number of times the refrigerator is opened and closed and the number of times the washing machine is used. Furthermore, when the resident is out, the collection unit can prioritize collecting smartphone browsing history and call history. Furthermore, when the resident is in a specific location, the collection unit can prioritize collecting event information related to the location and information about nearby facilities. In this way, the collection unit can prioritize collecting highly relevant data by taking the resident's geographical location information into consideration. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can collect data using an AI model that takes residents' geographic location information as input and prioritizes the collection of highly relevant data.

[0063] The collection unit may analyze the resident's social media activity during data collection to collect relevant data. For example, the collection unit may collect relevant data based on the content frequently posted by the resident on social media. The collection unit may also adjust the timing of data collection based on the resident's social media activity time. The collection unit may also collect relevant data based on the resident's social media interests. For example, the collection unit may collect data related to the resident's hobbies and topics of interest based on the content frequently posted by the resident on social media. The collection unit may also adjust the timing of data collection based on the resident's social media activity time. The collection unit may also collect relevant data based on the resident's social media interest time. In this way, the collection unit can efficiently collect relevant data by analyzing the resident's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect data using an AI model that uses the resident's social media activity as an input and collects relevant data.

[0064] The determination unit can estimate the resident's emotions and adjust the criteria for determining whether the resident is "slacking" or "lethargic" based on the estimated resident's emotions. For example, if the resident is feeling stressed, the determination unit can relax the criteria, making it less likely to determine the resident as lethargic. Furthermore, if the resident is relaxed, the determination unit can tighten the criteria, making it more likely to determine the resident as slacking. Furthermore, if the resident is busy, the determination unit can adjust the criteria and make a determination at an appropriate time. This allows the determination unit to adjust the criteria according to the resident's emotions, enabling more appropriate determination. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or without AI. For example, the determination unit can input data for estimating the resident's emotions and adjust the criteria using an AI model for emotion estimation.

[0065] The determination unit can improve the accuracy of the determination based on the correlation of collected data during the determination. The determination unit can, for example, analyze the correlation between smartphone usage time and the operating status of home appliances to make a highly accurate determination. The determination unit can also analyze the correlation between the frequency of use of a specific app and the operating status of home appliances to make a highly accurate determination. The determination unit can also analyze the correlation between a resident's lifestyle pattern and data to make a highly accurate determination. For example, the determination unit can analyze the correlation between smartphone usage time and the operating status of home appliances to accurately determine whether the resident is "slacking" or "lethargy." The determination unit can also analyze the correlation between the frequency of use of a specific app and the operating status of home appliances to accurately determine whether the resident is "slacking" or "lethargy." The determination unit can also analyze the correlation between a resident's lifestyle pattern and data to accurately determine whether the resident is "slacking" or "lethargy." As a result, the determination unit can improve the accuracy of the determination by taking into account the correlation of collected data. Some or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or without AI. For example, the judgment unit can make a judgment using an AI model that takes the correlations between collected data as input and improves the accuracy of the judgment.

[0066] The determination unit can determine whether a resident is "slacking" or "lethargic" based on the resident's attribute information during the determination. The determination unit can adjust the determination criteria based on, for example, the resident's age and gender. The determination unit can also adjust the determination criteria based on the resident's occupation and lifestyle. The determination unit can also adjust the determination criteria based on the resident's health condition and past behavioral history. For example, the determination unit can determine whether a resident is "slacking" or "lethargic" based on the resident's age and gender. The determination unit can also determine whether a resident is "slacking" or "lethargic" based on the resident's occupation and lifestyle. The determination unit can also determine whether a resident is "slacking" or "lethargic" based on the resident's health condition and past behavioral history. This allows the determination unit to make a more appropriate determination by taking the resident's attribute information into consideration. Some or all of the above-described processing in the determination unit may be performed, for example, using AI or without AI. For example, the judgment unit can input the resident's attribute information and make a judgment using an AI model that determines whether the resident is "slacking" or "lethargic."

[0067] The determination unit can estimate the resident's emotions and adjust the display method of the determination result based on the estimated resident's emotions. For example, if the resident is nervous, the determination unit can provide a simple, highly visible display method. Furthermore, if the resident is relaxed, the determination unit can provide a display method that includes detailed information. Furthermore, if the resident is in a hurry, the determination unit can provide a display method that focuses on the main points. This allows the determination unit to adjust the display method of the determination result according to the resident's emotions, enabling a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data for estimating the resident's emotions and adjust the display method of the determination result using an AI model for emotion estimation.

[0068] At the time of the determination, the determination unit can determine whether the resident is "slacking" or "lethargy" based on the geographical distribution of the resident. For example, the determination unit can determine whether the resident is slacking off on housework when the resident is at home. Furthermore, the determination unit can determine whether the resident is lethargic when the resident is out. Furthermore, the determination unit can make a determination based on behavior related to a specific location when the resident is in that location. For example, the determination unit can determine whether the resident is slacking off on housework based on the operation status of home appliances when the resident is at home. Furthermore, the determination unit can determine whether the resident is lethargic based on the smartphone usage history when the resident is out. Furthermore, the determination unit can determine whether the resident is "slacking off" or "lethargy" based on behavior related to that location when the resident is in a specific location. This allows the determination unit to make a more appropriate determination by taking the geographical distribution of the resident into consideration. Some or all of the above-described processing by the determination unit may be performed, for example, using AI or without AI. For example, the judgment unit can input the geographical distribution of residents and make a judgment using an AI model that determines whether a person is "slacking off" or "lethargic."

[0069] The determination unit can improve the accuracy of the determination based on related literature when making the determination. The determination unit can, for example, refer to the latest research papers and update the determination criteria. The determination unit can also improve the determination algorithm based on related academic literature. The determination unit can also improve the accuracy of the determination by referring to specialized books. For example, the determination unit can update the criteria for determining whether a resident is "slacking" or "lethargy" by referring to the latest research papers. The determination unit can also improve the algorithm for determining whether a resident is "slacking" or "lethargy" by referring to related academic literature. The determination unit can also improve the accuracy of determining whether a resident is "slacking" or "lethargy" by referring to specialized books. As a result, the determination unit improves the accuracy of the determination by referring to related literature. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can make a determination using an AI model that uses related literature as input and improves the accuracy of the determination.

[0070] The providing unit can estimate the resident's emotions and adjust the way words are used to enhance motivation based on the estimated resident's emotions. For example, if the resident is feeling stressed, the providing unit can encourage them with kind words. Also, if the resident is relaxed, the providing unit can motivate them with positive words. Furthermore, if the resident is busy, the providing unit can select concise and effective words. This allows the providing unit to provide a more effective message by adjusting the way words are used to enhance motivation according to the resident's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input data for estimating the resident's emotions and adjust the way words are used to enhance motivation using an AI model for emotion estimation.

[0071] When providing a message, the providing unit can select an appropriate message by analyzing the resident's past response history. For example, the providing unit can prioritize providing messages to which the resident responded favorably in the past. The providing unit can also select the most effective message based on the resident's past response history. Furthermore, the providing unit can analyze the resident's response pattern and provide the optimal message. For example, the providing unit can prioritize providing messages to which the resident responded favorably in the past. The providing unit can also select the most effective message based on the resident's past response history. Furthermore, the providing unit can analyze the resident's response pattern and provide the optimal message. In this way, the providing unit can select the optimal message and provide it effectively by analyzing the resident's past response history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide a message using an AI model that inputs the resident's past response history and selects the optimal message.

[0072] The providing unit can customize the content of the message based on the resident's current living situation when providing the message. For example, if the resident is busy, the providing unit can provide a short and to-the-point message. Furthermore, if the resident is relaxed, the providing unit can provide a message including detailed explanations. Furthermore, if the resident is tired, the providing unit can provide a message including words of encouragement. In this way, the providing unit can provide a more effective message by customizing the content of the message based on the resident's current living situation. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide a message using an AI model that inputs the resident's current living situation and customizes the content of the message.

[0073] The providing unit can estimate the resident's emotions and determine the priority of messages based on the estimated resident's emotions. For example, if the resident is feeling stressed, the providing unit can prioritize providing messages related to relaxation. Furthermore, if the resident is relaxed, the providing unit can prioritize providing positive messages. Furthermore, if the resident is busy, the providing unit can prioritize providing important messages. In this way, the providing unit can prioritize messages based on the resident's emotions, thereby providing important messages preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input data for estimating the resident's emotions and determine the priority of messages using an AI model for emotion estimation.

[0074] The providing unit can provide an appropriate message based on the resident's geographical location information at the time of providing. For example, if the resident is at home, the providing unit can provide a message regarding housework to be done at home. Furthermore, if the resident is out, the providing unit can provide a message regarding housework to be done after returning home. Furthermore, if the resident is in a specific location, the providing unit can provide a message related to the location. For example, if the resident is at home, the providing unit can provide a message regarding the time to vacuum or do laundry. Furthermore, if the resident is out, the providing unit can provide a reminder regarding housework to be done after returning home. Furthermore, if the resident is in a specific location, the providing unit can provide a message related to the location. In this way, the providing unit can provide an optimal message by taking the resident's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide a message using an AI model that inputs the resident's geographical location information and provides an appropriate message.

[0075] The providing unit may adjust the content of the message based on the resident's social media activity when providing the message. For example, the providing unit may adjust the message based on the content frequently posted by the resident on social media. The providing unit may also adjust the timing of the message based on the resident's social media activity time. Furthermore, the providing unit may adjust the content of the message based on the resident's social media interests. For example, the providing unit may provide a message related to a hobby or topic of interest based on the content frequently posted by the resident on social media. The providing unit may also adjust the timing of the message based on the resident's social media activity time. Furthermore, the providing unit may provide a relevant message based on the resident's social media interests. In this way, the providing unit can efficiently provide relevant messages by analyzing the resident's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide a message using an AI model that uses the resident's social media activity as input and adjusts the content of the message. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, determination unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects smartphone usage history and the operating status of home appliances. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine whether the resident is "slacking" or "lethargic." The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the resident with words to increase motivation based on the determination result. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, determination unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects smartphone usage history and the operating status of home appliances. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine whether the resident is "slacking" or "lethargic." The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the resident with motivational words based on the determination result. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, determination unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects smartphone usage history and the operating status of home appliances. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine whether the resident is "slacking" or "lethargic." The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the resident with words to increase motivation based on the determination result. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, determination unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects smartphone usage history and the operating status of home appliances. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine whether the resident is "slacking" or "lethargic." The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the resident with words to increase motivation based on the determination result.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The housework promotion system can also collect health data of residents and recommend housework based on their health status. For example, the collection unit can collect the resident's heart rate and sleep data, and the determination unit can analyze this data to determine the resident's level of fatigue. If the resident is tired, the provision unit can send a message such as "Take it easy today and rest." If the resident is healthy, the provision unit can send a message such as "Get your housework done while you're still healthy." This makes it possible to recommend appropriate housework based on the resident's health status.

[0078] The housework promotion system can also collect schedule data from residents and suggest the timing of housework based on the schedule. For example, the collection unit can collect schedule data from the resident's calendar app or planner, and the determination unit can analyze this data to determine the optimal timing for housework. The provision unit can send messages such as "It would be efficient to clean at this time" during times when the resident is free. Also, if the resident is busy, it can send messages such as "Do the housework at the next available time." This makes it possible to suggest efficient housework based on the resident's schedule.

[0079] The housework promotion system can further suggest housework based on the resident's hobbies and interests. For example, the collection unit collects data on the resident's hobbies and interests, and the determination unit analyzes this data to suggest housework that the resident will enjoy. If the resident likes cooking, the provision unit can send a message such as "Why not try a new recipe?". If the resident likes gardening, the provision unit can send a message such as "Try taking care of your garden." This makes it possible to suggest fun housework that suits the resident's hobbies and interests.

[0080] The housework promotion system can further suggest how to divide up housework based on the resident's family structure. For example, the collection unit can collect family structure data about the resident, and the determination unit can analyze this data to suggest how to divide up housework. The provision unit can suggest housework that all family members can participate in and send a message such as "Let's clean up together as a family." It can also suggest housework that is suitable for a specific family member and send a message such as "Why not ask your child to help out?" This allows all family members to work together to do housework.

[0081] The housework promotion system can further analyze the resident's past housework history and suggest efficient housework methods. For example, the collection unit collects the resident's past housework history data, and the determination unit analyzes this data to suggest efficient housework methods. The provision unit can send a message such as "Cleaning in this way is efficient" based on the housework methods that the resident has used successfully in the past. It can also send a message such as "Avoid this method" to encourage the resident to avoid housework methods that have failed in the past. This allows the resident to perform housework efficiently.

[0082] The housework promotion system can further estimate the resident's emotions and determine the priority of housework based on the estimated resident's emotions. For example, the collection unit can collect resident's emotion data, and the determination unit can analyze this data and determine the priority of housework based on the resident's emotions. The provision unit can send a message such as "Let's start with housework that will relax you" if the resident is feeling stressed. Also, if the resident is relaxed, it can send a message such as "Let's get the housework done efficiently." In this way, appropriate housework priorities are determined according to the resident's emotions.

[0083] The housework promotion system can further suggest housework tasks based on the resident's geographical location information. For example, the collection unit can collect the resident's geographical location information, and the determination unit can analyze this data to suggest housework tasks based on the resident's current location. The provision unit can send a message such as "Let's clean up now" when the resident is at home, or a message such as "Let's do the laundry after we get home" when the resident is out. This makes it possible to suggest appropriate housework tasks based on the resident's current location.

[0084] The housework promotion system can further estimate the resident's emotions and suggest housework methods based on the estimated resident's emotions. For example, the collection unit can collect resident's emotion data, and the determination unit can analyze this data and suggest housework methods based on the resident's emotions. If the resident is feeling stressed, the provision unit can send a message such as "Listen to relaxing music while you clean." If the resident is relaxed, the provision unit can send a message such as "Get your housework done efficiently." In this way, appropriate housework methods are suggested according to the resident's emotions.

[0085] The housework promotion system can further analyze the social media activities of residents and suggest housework based on their responses on social media. For example, the collection unit collects social media activity data of residents, and the determination unit analyzes this data to suggest housework that the residents are interested in on social media. If the resident posts on social media that "cleaning is fun," the provision unit can send a message such as "Let's clean." Or, if the resident posts that "I like cooking," the provision unit can send a message such as "Let's try a new recipe." This makes it possible to suggest housework based on the residents' social media activities.

[0086] The housework promotion system can further estimate the resident's emotions and monitor the progress of housework based on the estimated resident's emotions. For example, the collection unit can collect resident's emotion data, and the determination unit can analyze this data and monitor the progress of housework based on the resident's emotions. The provision unit can send a message such as "Don't push yourself too hard" if the resident is feeling stressed. Also, if the resident is relaxed, it can send a message such as "You're making good progress." This makes it possible to appropriately monitor the progress of housework according to the resident's emotions.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The collection unit collects the resident's smartphone usage history or the operation status of home appliances. For example, it collects data such as smartphone usage time, frequency of use of specific apps, and operation time and frequency of use of home appliances. Specifically, it collects smartphone usage time as total usage time per day or usage time during specific time periods, and collects frequency of use of specific apps as frequency of use of social networking apps, game apps, business apps, etc. It also collects operation time of home appliances as air conditioner operation time and television viewing time. Step 2: The judgment unit analyzes the collected data and determines whether the resident is "slacking" or "lethargic." For example, if a resident uses their smartphone for a long period of time or if their home appliances have not been used for a certain period of time, it will determine that the resident is "slacking" or "lethargic." Specifically, if a resident uses their smartphone for more than an hour or if their home appliances have not been used for more than a day, it can determine that the resident is "slacking" or "lethargic." The processing in the judgment unit can sometimes be performed using AI, and an AI model can be used that uses the collected data as input and determines whether the resident is "slacking" or "lethargic." Step 3: The provider provides motivational words based on the assessment results. For example, it sends messages such as "Why don't you take a short break and start doing some housework?" or "It's time to vacuum" to the resident. Specifically, it can send the message "Why don't you take a short break and start doing some housework?" after a long period of inactivity or at a specific time of day, and send the message "It's time to vacuum" if the vacuum cleaner has not been used for a certain period of time. The processing in the provider can be performed using AI, and an AI model can be used that uses the assessment results as input and generates motivational words.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0160] [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects smartphone usage history of residents or operation status of home appliances; a determination unit that analyzes the data collected by the collection unit and determines whether the resident is slacking or lethargic; a provision unit that provides words that improve motivation based on the determination result obtained by the determination unit. A system characterized by:

2. The collecting unit Collect data on smartphone usage time or frequency of use of specific apps, and operating time or frequency of use of home appliances 2. The system of claim 1.

3. The determination unit The collected data is analyzed, and if a resident spends a long time using their smartphone or if their home appliances are not in operation for a certain period of time, it is determined that they are slacking or lethargic.

2. The system of claim 1.

4. The collecting unit Infer residents' emotions and adjust data collection timing based on the estimated emotions of residents 2. The system of claim 1.

5. The collecting unit Analyze residents' past smartphone usage history and home appliance operation status to select the appropriate collection method 2. The system of claim 1.

6. The collecting unit Filtering data collection based on residents' current living situation or areas of interest 2. The system of claim 1.

7. The collecting unit Estimate resident sentiment and prioritize data collection based on the estimated sentiment 2. The system of claim 1.

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