System

Abstract

An object of a system according to an embodiment is to recommend an appropriate cleaning method for home appliances and furniture and maintain a motivation for cleaning.SOLUTION: A system according to an embodiment includes a registration unit, a recommendation unit, an evaluation unit, and a generation unit. The registration unit registers home electric appliances and furniture. The recommendation unit recommends an appropriate cleaning method for the home appliances and furniture registered by the registration unit. The evaluation unit evaluates a state after cleaning based on the cleaning method recommended by the recommendation unit. The generation unit generates a catalog book based on the information evaluated by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to understand the appropriate cleaning method for home appliances and furniture, making it difficult to maintain motivation to clean.

[0005] The system according to the embodiment aims to recommend appropriate cleaning methods for home appliances and furniture, and to maintain motivation to clean. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a recommendation unit, an evaluation unit, and a generation unit. The registration unit registers home appliances and furniture. The recommendation unit recommends appropriate cleaning methods for the home appliances and furniture registered by the registration unit. The evaluation unit evaluates the state of the appliances and furniture after cleaning based on the cleaning methods recommended by the recommendation unit. The generation unit generates a catalog book based on the information evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend appropriate cleaning methods for home appliances and furniture, thereby maintaining motivation to clean. [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 cleaning support system according to an embodiment of the present invention is a system that uses videos and images to teach users the appropriate time, location, and method for cleaning home appliances and furniture, thereby enabling them to efficiently clean their home appliances and furniture and maintain their condition.

[0029] A cleaning assistance system according to an embodiment includes a registration unit, a recommendation unit, an evaluation unit, and a generation unit. The registration unit registers home appliances and furniture. For example, when a user uploads a photo of an air conditioner, the registration unit recognizes the manufacturer and model of the air conditioner and provides an appropriate cleaning method. The registration unit can also register other home appliances and furniture, such as refrigerators and washing machines, in a similar manner. The recommendation unit recommends appropriate cleaning methods for the home appliances and furniture registered by the registration unit. For example, in the case of an air conditioner, specific instructions such as "clean the filter once every three months" are provided with videos and images. The recommendation unit can also recommend methods for cleaning the interior of a refrigerator or the drum of a washing machine. The evaluation unit evaluates the condition of the appliance after cleaning based on the cleaning method recommended by the recommendation unit. For example, when a user uploads a photo of the appliance after cleaning, the evaluation unit analyzes the photo and calculates a market value based on the current cleanliness, age, and frequency of use. The evaluation unit can also recommend the next cleaning date based on the condition of the appliance after cleaning. The generation unit generates a catalog book based on the information evaluated by the evaluation unit. For example, the generation unit generates a catalog book that lists the home appliances registered by the user and organizes the cleaning methods and timings for each so that they can be seen at a glance. The generation unit can also customize the contents of the catalog book according to the user's preferences. This allows the cleaning support system according to the embodiment to efficiently support the user's cleaning. For example, by regularly cleaning the filter of an air conditioner, the user can maintain the performance of the air conditioner and save on electricity bills. Furthermore, by keeping furniture in good condition, the furniture can obtain a high market value when it is sold in the future.

[0030] The registration unit uses image recognition technology and voice recognition technology, allowing users to complete registration simply by verbally describing the product name and features. For example, if a user takes a photo of an air conditioner and simultaneously verbally describes it as "a Daikin air conditioner," the app analyzes the voice and automatically registers the product name and features. Similarly, if a user uploads a photo of a refrigerator and verbally describes it as "a Panasonic refrigerator with a 500-liter capacity," the app analyzes the information and registers detailed product information. Similarly, if a user takes a photo of a washing machine and verbally describes it as "a Hitachi drum-type washing machine," the app analyzes the voice and automatically registers the product name and features. This significantly reduces the effort required for registration, allowing users to complete registration simply by verbally describing the product name and features.

[0031] The registration unit allows the user to simultaneously input the product's usage history and maintenance history, enabling recommendations based on more detailed information. For example, when a user uploads a photo of an air conditioner, the registration unit allows the user to input the past filter cleaning history and usage time, and the app recommends the optimal cleaning time based on that information. Furthermore, when a user uploads a photo of a refrigerator, the registration unit allows the user to input the past maintenance history and usage frequency, and the app recommends the optimal cleaning method based on that information. Furthermore, when a user uploads a photo of a washing machine, the registration unit allows the user to input the past usage history and maintenance history, and the app recommends the optimal cleaning time and method based on that information. This makes it possible to make recommendations based on usage history and maintenance history, and to provide more accurate cleaning methods.

[0032] The recommendation section can combine seasonal and weather information to suggest the optimal cleaning time. For example, the generation AI may use seasonal information to recommend cleaning the air conditioner filter in early spring and suggest measures to combat pollen. The recommendation section may also use weather information to recommend cleaning windows before the rainy season and suggest measures to combat mold. The recommendation section may also use seasonal information to recommend cleaning fallen leaves in the garden in autumn and suggest preparations for winter. This allows for effective cleaning by suggesting the optimal cleaning time taking into account seasonal and weather information.

[0033] The recommendation section can promote environmentally friendly cleaning, including suggesting the use of eco-friendly cleaning methods and products. For example, the recommendation section may suggest eco-friendly cleaning methods using the generation AI, recommending natural cleaning methods using baking soda and vinegar. The recommendation section may also suggest environmentally friendly cleaning products using the generation AI, recommending reusable cleaning cloths and eco-friendly detergents. The recommendation section may also suggest eco-friendly cleaning methods using the generation AI, recommending cleaning techniques to reduce water usage. This promotes eco-friendly cleaning by suggesting environmentally friendly cleaning methods.

[0034] The evaluation unit can perform a more detailed condition evaluation by uploading a video in addition to the photo taken after cleaning. For example, the evaluation unit allows a user to upload a video of the air conditioner filter after cleaning, and the generation AI analyzes the video to perform a more detailed condition evaluation. The evaluation unit also allows a user to upload a video of the inside of a refrigerator after cleaning, and the generation AI analyzes the video to perform a more detailed condition evaluation. The evaluation unit also allows a user to upload a video of the inside of a washing machine after cleaning, and the generation AI analyzes the video to perform a more detailed condition evaluation. In this way, uploading videos makes it possible to perform a more detailed condition evaluation.

[0035] The evaluation unit can reflect local secondhand market data and auction data in real time when calculating market value. For example, the evaluation unit uses the generation AI to collect local secondhand market data in real time and calculate the market value of a cleaned air conditioner. The evaluation unit also uses the generation AI to collect auction data in real time and calculate the market value of a cleaned refrigerator. The evaluation unit also uses the generation AI to collect local secondhand market data in real time and calculate the market value of a cleaned washing machine. This allows for more accurate calculation of market value by reflecting local secondhand market data and auction data in real time.

[0036] The generation unit can automatically add the product's maintenance history and usage history to the catalog book, providing more detailed information. For example, the generation unit's generation AI automatically adds the user's air conditioner maintenance history to the catalog book and recommends the next maintenance period. The generation unit's generation AI also automatically adds the user's refrigerator usage history to the catalog book and recommends the optimal cleaning period. The generation unit's generation AI also automatically adds the user's washing machine maintenance history to the catalog book and recommends the next maintenance period. In this way, by automatically adding maintenance history and usage history, more detailed information can be provided.

[0037] The generation unit can add a function that enables customization of the contents of the catalog book according to the user's preferences. For example, the generation unit provides a function that enables a user to customize the display order and contents according to their preferences when adding air conditioner cleaning methods to the catalog book. The generation unit also provides a function that enables a user to customize the display order and contents according to their preferences when adding refrigerator cleaning methods to the catalog book. The generation unit also provides a function that enables a user to customize the display order and contents according to their preferences when adding washing machine cleaning methods to the catalog book. This allows customization according to the user's preferences, making it possible to provide a catalog book that is easier to use.

[0038] The generation unit can add a function that allows the contents of the catalog book to be shared with other family members or friends. For example, the generation unit provides a function that allows the user to share the contents of the catalog book with family members and manage the cleaning schedule for the whole family. The generation unit also provides a function that allows the user to share the contents of the catalog book with friends and set reminders for joint maintenance. The generation unit also provides a function that allows the user to share the contents of the catalog book with family members and manage the maintenance history for the whole family and recommend the optimal cleaning time. This makes it possible to jointly perform maintenance and cleaning by sharing the contents of the catalog book.

[0039] The generation unit can automatically add product purchase history and warranty information to the catalog book, making management easier. For example, the generation unit allows the generation AI to automatically add the purchase history and warranty information of a user's air conditioner to the catalog book, making management easier. The generation unit also allows the generation AI to automatically add the purchase history and warranty information of a user's refrigerator to the catalog book, making management easier. The generation unit also allows the generation AI to automatically add the purchase history and warranty information of a user's washing machine to the catalog book, making management easier. In this way, automatic addition of purchase history and warranty information makes management easier.

[0040] The motivation maintenance unit can introduce a reward system according to the level of cleaning achievement, and provide incentives to the user. For example, the motivation maintenance unit provides a reward system in which the generation AI awards points according to the level of cleaning achievement, and the points can be accumulated and exchanged for gift cards or coupons. The motivation maintenance unit also provides a reward system in which the generation AI awards badges according to the level of cleaning achievement, and rewards can be received by collecting specific badges. The motivation maintenance unit also provides a reward system in which the generation AI displays a ranking according to the level of cleaning achievement, and rewards can be received by ranking high. In this way, by introducing a reward system according to the level of cleaning achievement, the user's motivation is maintained.

[0041] The motivation maintenance unit can add a function that visualizes the cleaning progress, allowing the user to feel a sense of accomplishment. For example, the motivation maintenance unit allows the generation AI to visualize the cleaning progress in graphs or charts, allowing the user to feel a sense of accomplishment. The motivation maintenance unit also allows the generation AI to display the cleaning progress on a timeline, allowing the user to feel a sense of accomplishment. The motivation maintenance unit also allows the generation AI to display the cleaning progress on a calendar, allowing the user to feel a sense of accomplishment. In this way, visualizing the cleaning progress allows the user to feel a sense of accomplishment.

[0042] The motivation maintenance unit can add a function to share cleaning progress with family and friends and promote competition and cooperation. For example, the motivation maintenance unit provides a function that allows the generation AI to share cleaning progress with family and friends and promote competition and cooperation. The motivation maintenance unit also provides a function that allows the generation AI to share cleaning progress with family and friends and set reminders to jointly complete cleaning tasks. The motivation maintenance unit also provides a function that allows the generation AI to share cleaning progress with family and friends and distribute rewards according to the level of cleaning completion. In this way, sharing cleaning progress promotes competition and cooperation and maintains motivation.

[0043] The motivation maintenance unit adds a function that allows the user to share the progress of cleaning on social media and receive feedback from other users. For example, the motivation maintenance unit provides a function that allows the generation AI to share the progress of cleaning on social media and receive feedback from other users. For example, the generation AI can post before and after photos of cleaning and receive comments and likes from other users. The motivation maintenance unit also provides a function that allows the generation AI to share the progress of cleaning on social media and receive feedback from other users. For example, the generation AI can share the progress of cleaning in real time and receive messages of encouragement from other users. The motivation maintenance unit also provides a function that allows the generation AI to share the progress of cleaning on social media and receive feedback from other users. For example, the generation AI can post the results of cleaning and receive advice and suggestions from other users. In this way, the user's motivation is maintained through sharing and feedback on social media.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The cleaning assistance system can also include a health management unit that monitors the user's health condition. For example, it can measure the user's heart rate and blood pressure while cleaning, and if an abnormality is detected, it can display an alert urging the user to take a break. The health management unit can also record the user's exercise volume and calculate the amount of calories burned through cleaning. Furthermore, the health management unit can recommend appropriate cleaning times and frequency based on the user's health condition. This allows the user to clean efficiently while maintaining their health.

[0046] The cleaning support system can further include a lifestyle rhythm analysis unit that analyzes the user's lifestyle rhythm. For example, it can analyze the user's wake-up time, bedtime, and meal timings and recommend optimal cleaning times based on the results. The lifestyle rhythm analysis unit can also set cleaning reminders based on the user's lifestyle pattern. Furthermore, the lifestyle rhythm analysis unit can determine cleaning priorities and propose efficient cleaning plans based on the user's lifestyle rhythm. This allows the user to clean efficiently in accordance with their lifestyle rhythm.

[0047] The cleaning assistance system can further include a cleaning tool suggestion unit that suggests cleaning tools according to the user's preferences. For example, if the user prefers eco-friendly cleaning tools, the cleaning tool suggestion unit can suggest reusable cleaning cloths and eco-friendly detergents. Also, if the user prefers the latest cleaning technology, the cleaning tool suggestion unit can suggest the latest cleaning robots and high-performance vacuum cleaners. Furthermore, the cleaning tool suggestion unit can recommend the most suitable cleaning tools according to the user's cleaning style. This allows the user to clean efficiently using cleaning tools that suit their preferences.

[0048] The cleaning support system can further include a schedule suggestion unit that analyzes the user's cleaning history and proposes an optimal cleaning schedule. For example, it can analyze the date and frequency of the user's past cleaning and recommend the next cleaning time based on that. The schedule suggestion unit can also propose an optimal cleaning schedule taking into account the user's lifestyle and emotional state. Furthermore, the schedule suggestion unit can determine cleaning priorities based on the user's cleaning history and propose an efficient cleaning plan. This allows the user to perform efficient cleaning that suits their lifestyle.

[0049] The cleaning assistance system may further include a progress monitoring unit that monitors the user's cleaning progress in real time. For example, the system may record the time from when the user starts cleaning until when the user finishes cleaning and display the progress in a graph or chart. The progress monitoring unit may also display the tasks the user completes while cleaning in real time, allowing the user to feel a sense of accomplishment. Furthermore, the progress monitoring unit may recommend the next task to be done based on the user's cleaning progress. This allows the user to grasp the cleaning progress in real time and clean efficiently.

[0050] The cleaning assistance system may further include a sharing unit that allows a user to share their cleaning progress with other users. For example, the user may share their cleaning progress on a social networking site (SNS) and receive feedback from other users. The sharing unit may also allow the user to share their cleaning progress with family and friends and set reminders for collaborative cleaning. Furthermore, the sharing unit may provide a function that promotes competition and cooperation with other users based on the user's cleaning progress. This allows the user to clean efficiently while sharing their cleaning progress and receiving feedback from other users.

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

[0052] Step 1: The registration unit registers home appliances and furniture. For example, when a user takes and uploads a photo of an air conditioner, the registration unit recognizes the manufacturer and model of the air conditioner and provides appropriate cleaning instructions. The registration unit can also register other home appliances and furniture, such as refrigerators and washing machines, in the same way. Step 2: The recommendation unit recommends appropriate cleaning methods for the home appliances and furniture registered by the registration unit. For example, for an air conditioner, it provides specific instructions with videos and images, such as "clean the filter once every three months." The recommendation unit can also recommend methods for cleaning the inside of a refrigerator or the drum of a washing machine. Step 3: The evaluation unit evaluates the state of the item after cleaning based on the cleaning method recommended by the recommendation unit. For example, when a user uploads a photo of the item after cleaning, the evaluation unit analyzes the photo and calculates the market value based on the current cleanliness, age, and frequency of use. The evaluation unit can also recommend the next time to clean the item based on the state of the item after cleaning. Step 4: The generation unit generates a catalog book based on the information evaluated by the evaluation unit. For example, a catalog book is generated that lists the home appliances registered by the user and the cleaning method and timing for each product so that they can be seen at a glance. The generation unit can also customize the contents of the catalog book according to the user's preferences.

[0053] (Example 2) A cleaning support system according to an embodiment of the present invention is a system that uses videos and images to teach users the appropriate time, location, and method for cleaning home appliances and furniture, thereby enabling them to efficiently clean their home appliances and furniture and maintain their condition.

[0054] A cleaning assistance system according to an embodiment includes a registration unit, a recommendation unit, an evaluation unit, and a generation unit. The registration unit registers home appliances and furniture. For example, when a user uploads a photo of an air conditioner, the registration unit recognizes the manufacturer and model of the air conditioner and provides an appropriate cleaning method. The registration unit can also register other home appliances and furniture, such as refrigerators and washing machines, in a similar manner. The recommendation unit recommends appropriate cleaning methods for the home appliances and furniture registered by the registration unit. For example, in the case of an air conditioner, specific instructions such as "clean the filter once every three months" are provided with videos and images. The recommendation unit can also recommend methods for cleaning the interior of a refrigerator or the drum of a washing machine. The evaluation unit evaluates the condition of the appliance after cleaning based on the cleaning method recommended by the recommendation unit. For example, when a user uploads a photo of the appliance after cleaning, the evaluation unit analyzes the photo and calculates a market value based on the current cleanliness, age, and frequency of use. The evaluation unit can also recommend the next cleaning date based on the condition of the appliance after cleaning. The generation unit generates a catalog book based on the information evaluated by the evaluation unit. For example, the generation unit generates a catalog book that lists the home appliances registered by the user and organizes the cleaning methods and timings for each so that they can be seen at a glance. The generation unit can also customize the contents of the catalog book according to the user's preferences. This allows the cleaning support system according to the embodiment to efficiently support the user's cleaning. For example, by regularly cleaning the filter of an air conditioner, the user can maintain the performance of the air conditioner and save on electricity bills. Furthermore, by keeping furniture in good condition, the furniture can obtain a high market value when it is sold in the future.

[0055] The registration unit uses image recognition technology and voice recognition technology, allowing users to complete registration simply by verbally describing the product name and features. For example, if a user takes a photo of an air conditioner and simultaneously verbally describes it as "a Daikin air conditioner," the app analyzes the voice and automatically registers the product name and features. Similarly, if a user uploads a photo of a refrigerator and verbally describes it as "a Panasonic refrigerator with a 500-liter capacity," the app analyzes the information and registers detailed product information. Similarly, if a user takes a photo of a washing machine and verbally describes it as "a Hitachi drum-type washing machine," the app analyzes the voice and automatically registers the product name and features. This significantly reduces the effort required for registration, allowing users to complete registration simply by verbally describing the product name and features.

[0056] The registration unit allows the user to simultaneously input the product's usage history and maintenance history, enabling recommendations based on more detailed information. For example, when a user uploads a photo of an air conditioner, the registration unit allows the user to input the past filter cleaning history and usage time, and the app recommends the optimal cleaning time based on that information. Furthermore, when a user uploads a photo of a refrigerator, the registration unit allows the user to input the past maintenance history and usage frequency, and the app recommends the optimal cleaning method based on that information. Furthermore, when a user uploads a photo of a washing machine, the registration unit allows the user to input the past usage history and maintenance history, and the app recommends the optimal cleaning time and method based on that information. This makes it possible to make recommendations based on usage history and maintenance history, and to provide more accurate cleaning methods.

[0057] The registration unit can use the emotion estimation function to provide an interface for reducing stress and anxiety felt by the user during registration. For example, when a user registers a home appliance, the registration unit uses the emotion estimation function to detect stress or anxiety and displays relaxing music or an encouraging message. Furthermore, when a user registers furniture, the registration unit uses the emotion estimation function to detect anxiety and provides a simple operation guide or help function. Furthermore, when a user registers a home appliance, the registration unit uses the emotion estimation function to detect stress and provides an interface for simplifying the registration process. This reduces the stress and anxiety felt by the user during registration, making the registration process more comfortable.

[0058] The recommendation section can combine seasonal and weather information to suggest the optimal cleaning time. For example, the generation AI may use seasonal information to recommend cleaning the air conditioner filter in early spring and suggest measures to combat pollen. The recommendation section may also use weather information to recommend cleaning windows before the rainy season and suggest measures to combat mold. The recommendation section may also use seasonal information to recommend cleaning fallen leaves in the garden in autumn and suggest preparations for winter. This allows for effective cleaning by suggesting the optimal cleaning time taking into account seasonal and weather information.

[0059] The recommendation section can promote environmentally friendly cleaning, including suggesting the use of eco-friendly cleaning methods and products. For example, the recommendation section may suggest eco-friendly cleaning methods using the generation AI, recommending natural cleaning methods using baking soda and vinegar. The recommendation section may also suggest environmentally friendly cleaning products using the generation AI, recommending reusable cleaning cloths and eco-friendly detergents. The recommendation section may also suggest eco-friendly cleaning methods using the generation AI, recommending cleaning techniques to reduce water usage. This promotes eco-friendly cleaning by suggesting environmentally friendly cleaning methods.

[0060] The recommendation unit uses the emotion estimation function to suggest a cleaning method that corresponds to the user's emotional state, thereby reducing cleaning stress. For example, the recommendation unit uses the emotion estimation function to suggest a quick and effective cleaning method when the user is feeling stressed. Furthermore, the recommendation unit uses the emotion estimation function to suggest a cleaning method that uses aromas that have a relaxing effect when the user is relaxed. Furthermore, the recommendation unit uses the emotion estimation function to suggest the use of a cleaning robot when the user is tired, thereby reducing the burden on the user. In this way, cleaning stress is reduced by suggesting a cleaning method that corresponds to the user's emotional state.

[0061] The evaluation unit can perform a more detailed condition evaluation by uploading a video in addition to the photo taken after cleaning. For example, the evaluation unit allows a user to upload a video of the air conditioner filter after cleaning, and the generation AI analyzes the video to perform a more detailed condition evaluation. The evaluation unit also allows a user to upload a video of the inside of a refrigerator after cleaning, and the generation AI analyzes the video to perform a more detailed condition evaluation. The evaluation unit also allows a user to upload a video of the inside of a washing machine after cleaning, and the generation AI analyzes the video to perform a more detailed condition evaluation. In this way, uploading videos makes it possible to perform a more detailed condition evaluation.

[0062] The evaluation unit can reflect local secondhand market data and auction data in real time when calculating market value. For example, the evaluation unit uses the generation AI to collect local secondhand market data in real time and calculate the market value of a cleaned air conditioner. The evaluation unit also uses the generation AI to collect auction data in real time and calculate the market value of a cleaned refrigerator. The evaluation unit also uses the generation AI to collect local secondhand market data in real time and calculate the market value of a cleaned washing machine. This allows for more accurate calculation of market value by reflecting local secondhand market data and auction data in real time.

[0063] The evaluation unit can use the emotion estimation function to evaluate the sense of accomplishment and satisfaction the user feels after cleaning and reflect that in the market value. The evaluation unit, for example, uses the emotion estimation function to evaluate the sense of accomplishment the user feels after cleaning and reflects the emotion score in the market value. For example, if the sense of accomplishment is high, the market value is increased. The evaluation unit also uses the emotion estimation function to evaluate the satisfaction the user feels after cleaning and reflects the emotion score in the market value. For example, if the satisfaction is high, the market value is increased. The evaluation unit also uses the emotion estimation function to evaluate the sense of accomplishment and satisfaction the user feels after cleaning and calculates the market value based on that data. For example, if the emotion score is high, the market value is increased. In this way, by reflecting the user's emotions in the market value, it is possible to provide an evaluation that is more valuable to the user.

[0064] The generation unit can automatically add the product's maintenance history and usage history to the catalog book, providing more detailed information. For example, the generation unit's generation AI automatically adds the user's air conditioner maintenance history to the catalog book and recommends the next maintenance period. The generation unit's generation AI also automatically adds the user's refrigerator usage history to the catalog book and recommends the optimal cleaning period. The generation unit's generation AI also automatically adds the user's washing machine maintenance history to the catalog book and recommends the next maintenance period. In this way, by automatically adding maintenance history and usage history, more detailed information can be provided.

[0065] The generation unit can add a function that enables customization of the contents of the catalog book according to the user's preferences. For example, the generation unit provides a function that enables a user to customize the display order and contents according to their preferences when adding air conditioner cleaning methods to the catalog book. The generation unit also provides a function that enables a user to customize the display order and contents according to their preferences when adding refrigerator cleaning methods to the catalog book. The generation unit also provides a function that enables a user to customize the display order and contents according to their preferences when adding washing machine cleaning methods to the catalog book. This allows customization according to the user's preferences, making it possible to provide a catalog book that is easier to use.

[0066] The generation unit can use the emotion estimation function to analyze the emotional state of the user when browsing the catalog book and propose an optimal information display method. For example, the generation unit uses the emotion estimation function to analyze the emotional state of the user when browsing the catalog book and propose a display method that allows the user to relax. The generation unit also uses the emotion estimation function to analyze the emotional state of the user when browsing the catalog book and propose a display method that reduces stress. The generation unit also uses the emotion estimation function to analyze the emotional state of the user when browsing the catalog book and propose a display method that elicits positive emotions. This improves the catalog book browsing experience by proposing an optimal information display method according to the user's emotional state.

[0067] The generation unit can add a function that allows the contents of the catalog book to be shared with other family members or friends. For example, the generation unit provides a function that allows the user to share the contents of the catalog book with family members and manage the cleaning schedule for the whole family. The generation unit also provides a function that allows the user to share the contents of the catalog book with friends and set reminders for joint maintenance. The generation unit also provides a function that allows the user to share the contents of the catalog book with family members and manage the maintenance history for the whole family and recommend the optimal cleaning time. This makes it possible to jointly perform maintenance and cleaning by sharing the contents of the catalog book.

[0068] The generation unit can automatically add product purchase history and warranty information to the catalog book, making management easier. For example, the generation unit allows the generation AI to automatically add the purchase history and warranty information of a user's air conditioner to the catalog book, making management easier. The generation unit also allows the generation AI to automatically add the purchase history and warranty information of a user's refrigerator to the catalog book, making management easier. The generation unit also allows the generation AI to automatically add the purchase history and warranty information of a user's washing machine to the catalog book, making management easier. In this way, automatic addition of purchase history and warranty information makes management easier.

[0069] The generation unit can use the emotion estimation function to incorporate elements that interest the user into the contents of the catalog book, thereby promoting viewing. For example, the generation unit uses the emotion estimation function to add cleaning methods and product information that interest the user to the catalog book, thereby promoting viewing. The generation unit also uses the emotion estimation function to add maintenance information that interest the user to the catalog book, thereby promoting viewing. The generation unit also uses the emotion estimation function to add cleaning tips and hints that interest the user to the catalog book, thereby promoting viewing. In this way, by incorporating elements that interest the user, viewing of the catalog book is promoted.

[0070] The motivation maintenance unit can introduce a reward system according to the level of cleaning achievement, and provide incentives to the user. For example, the motivation maintenance unit provides a reward system in which the generation AI awards points according to the level of cleaning achievement, and the points can be accumulated and exchanged for gift cards or coupons. The motivation maintenance unit also provides a reward system in which the generation AI awards badges according to the level of cleaning achievement, and rewards can be received by collecting specific badges. The motivation maintenance unit also provides a reward system in which the generation AI displays a ranking according to the level of cleaning achievement, and rewards can be received by ranking high. In this way, by introducing a reward system according to the level of cleaning achievement, the user's motivation is maintained.

[0071] The motivation maintenance unit can add a function that visualizes the cleaning progress, allowing the user to feel a sense of accomplishment. For example, the motivation maintenance unit allows the generation AI to visualize the cleaning progress in graphs or charts, allowing the user to feel a sense of accomplishment. The motivation maintenance unit also allows the generation AI to display the cleaning progress on a timeline, allowing the user to feel a sense of accomplishment. The motivation maintenance unit also allows the generation AI to display the cleaning progress on a calendar, allowing the user to feel a sense of accomplishment. In this way, visualizing the cleaning progress allows the user to feel a sense of accomplishment.

[0072] The motivation maintenance unit can use the emotion estimation function to suggest a motivation maintenance method according to the user's emotional state. For example, the motivation maintenance unit uses the emotion estimation function to suggest a relaxing cleaning method when the user is feeling stressed. The motivation maintenance unit also uses the emotion estimation function to suggest a quick and effective cleaning method when the user is tired. The motivation maintenance unit also uses the emotion estimation function to suggest a cleaning method using an aroma that has a relaxing effect when the user is relaxed. In this way, by suggesting a motivation maintenance method according to the user's emotional state, cleaning motivation is maintained.

[0073] The motivation maintenance unit can add a function to share cleaning progress with family and friends and promote competition and cooperation. For example, the motivation maintenance unit provides a function that allows the generation AI to share cleaning progress with family and friends and promote competition and cooperation. The motivation maintenance unit also provides a function that allows the generation AI to share cleaning progress with family and friends and set reminders to jointly complete cleaning tasks. The motivation maintenance unit also provides a function that allows the generation AI to share cleaning progress with family and friends and distribute rewards according to the level of cleaning completion. In this way, sharing cleaning progress promotes competition and cooperation and maintains motivation.

[0074] The motivation maintenance unit adds a function that allows the user to share the progress of cleaning on social media and receive feedback from other users. For example, the motivation maintenance unit provides a function that allows the generation AI to share the progress of cleaning on social media and receive feedback from other users. For example, the generation AI can post before and after photos of cleaning and receive comments and likes from other users. The motivation maintenance unit also provides a function that allows the generation AI to share the progress of cleaning on social media and receive feedback from other users. For example, the generation AI can share the progress of cleaning in real time and receive messages of encouragement from other users. The motivation maintenance unit also provides a function that allows the generation AI to share the progress of cleaning on social media and receive feedback from other users. For example, the generation AI can post the results of cleaning and receive advice and suggestions from other users. In this way, the user's motivation is maintained through sharing and feedback on social media.

[0075] The motivation maintenance unit can incorporate gamification elements using the emotion estimation function to make cleaning more enjoyable for the user. For example, the motivation maintenance unit can use the emotion estimation function to suggest cleaning challenges that will make cleaning more enjoyable for the user, allowing the user to feel a sense of accomplishment. For example, points can be earned by completing specific cleaning tasks. The motivation maintenance unit can also use the emotion estimation function to suggest cleaning games that will make cleaning more enjoyable for the user, turning cleaning into a fun activity. For example, a game can be provided in which a character grows according to the progress of cleaning. The motivation maintenance unit can also use the emotion estimation function to suggest cleaning rankings that will make cleaning more enjoyable for the user, allowing the user to compete with other users. For example, the ranking increases according to the degree of cleaning achievement. In this way, by incorporating gamification elements, the user can enjoy cleaning.

[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 cleaning assistance system can also include a health management unit that monitors the user's health condition. For example, it can measure the user's heart rate and blood pressure while cleaning, and if an abnormality is detected, it can display an alert urging the user to take a break. The health management unit can also record the user's exercise volume and calculate the amount of calories burned through cleaning. Furthermore, the health management unit can recommend appropriate cleaning times and frequency based on the user's health condition. This allows the user to clean efficiently while maintaining their health.

[0078] The cleaning support system can further include a lifestyle rhythm analysis unit that analyzes the user's lifestyle rhythm. For example, it can analyze the user's wake-up time, bedtime, and meal timings and recommend optimal cleaning times based on the results. The lifestyle rhythm analysis unit can also set cleaning reminders based on the user's lifestyle pattern. Furthermore, the lifestyle rhythm analysis unit can determine cleaning priorities and propose efficient cleaning plans based on the user's lifestyle rhythm. This allows the user to clean efficiently in accordance with their lifestyle rhythm.

[0079] The cleaning assistance system can further include a cleaning tool suggestion unit that suggests cleaning tools according to the user's preferences. For example, if the user prefers eco-friendly cleaning tools, the cleaning tool suggestion unit can suggest reusable cleaning cloths and eco-friendly detergents. Also, if the user prefers the latest cleaning technology, the cleaning tool suggestion unit can suggest the latest cleaning robots and high-performance vacuum cleaners. Furthermore, the cleaning tool suggestion unit can recommend the most suitable cleaning tools according to the user's cleaning style. This allows the user to clean efficiently using cleaning tools that suit their preferences.

[0080] The cleaning assistance system may further include a music providing unit that provides cleaning music according to the user's emotional state. For example, if the user is feeling stressed, relaxing music may be played. The music providing unit may also play up-tempo music if the user wants to cheer up. Furthermore, the music providing unit may create an optimal music playlist based on the user's emotional state and play the music while cleaning. This allows the user to enjoy cleaning while listening to music that suits their emotional state.

[0081] The cleaning assistance system may further include an advice providing unit that provides cleaning advice according to the user's emotional state. For example, if the user is not motivated to clean, an encouraging message may be displayed. The advice providing unit may also suggest an easy cleaning method if the user feels anxious about cleaning. Furthermore, the advice providing unit may provide optimal cleaning advice based on the user's emotional state, thereby reducing cleaning stress. This allows the user to clean efficiently while receiving advice according to their emotional state.

[0082] The cleaning support system can further include a schedule suggestion unit that analyzes the user's cleaning history and proposes an optimal cleaning schedule. For example, it can analyze the date and frequency of the user's past cleaning and recommend the next cleaning time based on that. The schedule suggestion unit can also propose an optimal cleaning schedule taking into account the user's lifestyle and emotional state. Furthermore, the schedule suggestion unit can determine cleaning priorities based on the user's cleaning history and propose an efficient cleaning plan. This allows the user to perform efficient cleaning that suits their lifestyle.

[0083] The cleaning assistance system can further include a task division unit that suggests how to divide a cleaning task according to the user's emotional state. For example, if the user is feeling stressed, the cleaning task can be divided into small steps to make the user feel a sense of accomplishment. The task division unit can also suggest a larger cleaning task if the user is relaxed. Furthermore, the task division unit can suggest an optimal method for dividing the cleaning task based on the user's emotional state, thereby reducing cleaning stress. This allows the user to efficiently perform cleaning tasks according to their emotional state.

[0084] The cleaning assistance system may further include a progress monitoring unit that monitors the user's cleaning progress in real time. For example, the system may record the time from when the user starts cleaning until when the user finishes cleaning and display the progress in a graph or chart. The progress monitoring unit may also display the tasks the user completes while cleaning in real time, allowing the user to feel a sense of accomplishment. Furthermore, the progress monitoring unit may recommend the next task to be done based on the user's cleaning progress. This allows the user to grasp the cleaning progress in real time and clean efficiently.

[0085] The cleaning assistance system may further include a reward providing unit that provides a cleaning reward according to the user's emotional state. For example, when the user completes cleaning, the reward providing unit may use the emotion estimation function to evaluate the user's sense of accomplishment and award points or badges based on the evaluation. The reward providing unit may also use the emotion estimation function to evaluate the user's motivation when cleaning and provide benefits based on the evaluation. Furthermore, the reward providing unit may suggest optimal rewards based on the user's emotional state and maintain the user's motivation for cleaning. This allows the user to enjoy cleaning while receiving rewards according to their emotional state.

[0086] The cleaning assistance system may further include a sharing unit that allows a user to share their cleaning progress with other users. For example, the user may share their cleaning progress on a social networking site (SNS) and receive feedback from other users. The sharing unit may also allow the user to share their cleaning progress with family and friends and set reminders for collaborative cleaning. Furthermore, the sharing unit may provide a function that promotes competition and cooperation with other users based on the user's cleaning progress. This allows the user to clean efficiently while sharing their cleaning progress and receiving feedback from other users.

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

[0088] Step 1: The registration unit registers home appliances and furniture. For example, when a user takes and uploads a photo of an air conditioner, the registration unit recognizes the manufacturer and model of the air conditioner and provides appropriate cleaning instructions. The registration unit can also register other home appliances and furniture, such as refrigerators and washing machines, in the same way. Step 2: The recommendation unit recommends appropriate cleaning methods for the home appliances and furniture registered by the registration unit. For example, for an air conditioner, it provides specific instructions with videos and images, such as "clean the filter once every three months." The recommendation unit can also recommend methods for cleaning the inside of a refrigerator or the drum of a washing machine. Step 3: The evaluation unit evaluates the state of the item after cleaning based on the cleaning method recommended by the recommendation unit. For example, when a user uploads a photo of the item after cleaning, the evaluation unit analyzes the photo and calculates the market value based on the current cleanliness, age, and frequency of use. The evaluation unit can also recommend the next time to clean the item based on the state of the item after cleaning. Step 4: The generation unit generates a catalog book based on the information evaluated by the evaluation unit. For example, a catalog book is generated that lists the home appliances registered by the user and the cleaning method and timing for each product so that they can be seen at a glance. The generation unit can also customize the contents of the catalog book according to the user's preferences.

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

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

[0093] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

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

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] 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.

[0117] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 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 registration department for registering home appliances and furniture; a recommendation unit that recommends an appropriate cleaning method for the home appliances and the furniture registered by the registration unit; an evaluation unit that evaluates a state after cleaning based on the cleaning method recommended by the recommendation unit; a generation unit that generates a catalog book based on the information evaluated by the evaluation unit. A system characterized by:

2. The registration unit Using voice recognition technology in addition to image recognition technology, registration can be completed simply by the user verbally describing the product name and its features.

2. The system of claim 1.

3. The registration unit It will also be possible to input product usage history and maintenance history at the same time, enabling recommendations based on more detailed information.

2. The system of claim 1.

4. The registration unit Provide an interface that reduces the stress and anxiety users feel when registering 2. The system of claim 1.

5. The recommendation unit Combines seasonal and weather information to suggest the best time to clean 2. The system of claim 1.

6. The recommendation unit Promote environmentally friendly cleaning, including suggestions for using eco-friendly cleaning methods and products 2. The system of claim 1.

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