System

Abstract

An object of the system according to the embodiment is to propose an optimal cleaning method, tool, and detergent according to the type of dirt.SOLUTION: A system according to an embodiment includes a contamination analysis unit, a proposal unit, a list generation unit, and a link provision unit. The contamination analysis unit analyzes a photograph of contamination taken by a user. The suggestion unit suggests an optimal cleaning method, a tool to be used, and a detergent based on the type of contamination analyzed by the contamination analysis unit. The list generation unit generates a list of necessary tools and detergents based on the cleaning method proposed by the proposal unit. The link providing unit provides a link or information that can be purchased at an online store or a nearby store based on the list generated by the list generating 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 select the optimal cleaning method, tools, and detergent depending on the type of dirt, making it difficult to clean efficiently.

[0005] The system according to the embodiment aims to propose the most suitable cleaning method, tools, and detergent depending on the type of dirt. [Means for solving the problem]

[0006] The system according to the embodiment includes a stain analysis unit, a suggestion unit, a list generation unit, and a link provision unit. The stain analysis unit analyzes photos of stains taken by the user. The suggestion unit suggests optimal cleaning methods, tools, and detergents to use based on the types of stains analyzed by the stain analysis unit. The list generation unit generates a list of necessary tools and detergents based on the cleaning methods suggested by the suggestion unit. The link provision unit provides links and information for purchasing products at online stores or nearby stores based on the list generated by the list generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable cleaning method, tools, and detergent depending on the type of dirt. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The cleaning support system according to an embodiment of the present invention analyzes photos of dirt taken by a user, suggests optimal cleaning methods, tools, and detergents, and allows the user to easily obtain the necessary tools and detergents. This eliminates the user's cleaning worries and enables efficient and effective cleaning.

[0029] A cleaning assistance system according to an embodiment includes a stain analysis unit, a suggestion unit, a list generation unit, and a link provision unit. The stain analysis unit analyzes a photo of a stain taken by a user. For example, the stain analysis unit identifies the type of stain using a generation AI. The generation AI distinguishes between types such as oil stains, water stains, and mold. The suggestion unit suggests an optimal cleaning method, tools, and detergents to use based on the type of stain analyzed by the stain analysis unit. For example, the suggestion unit suggests an optimal cleaning method using a generation AI. For example, the generation AI suggests using a specific detergent for oil stains. The list generation unit generates a list of necessary tools and detergents based on the cleaning method suggested by the suggestion unit. For example, the list generation unit generates a list of necessary tools and detergents using a generation AI. The generation AI lists, for example, the names and quantities of tools and detergents needed for cleaning. The link provision unit provides links and information for purchasing tools and detergents at online stores and nearby stores based on the list generated by the list generation unit. For example, the link provision unit provides links and information for online stores and nearby stores using a generation AI. The generation AI provides, for example, links to online stores or nearby stores specified by the user. This allows the cleaning assistance system according to the embodiment to eliminate the user's cleaning worries and enable efficient and effective cleaning. For example, the user can learn the optimal cleaning method for each type of dirt and easily obtain the necessary tools and detergents. Furthermore, by using the links to online stores or nearby stores, the user can easily purchase the necessary tools and detergents.

[0030] The dirt analysis unit analyzes the components of the dirt and can recommend the optimal detergent based on the components. The dirt analysis unit uses, for example, generative AI to analyze the components of the dirt. For example, generative AI analyzes a photo of the dirt and identifies the components of the dirt. For example, in the case of an oily stain, generative AI detects the oily components and recommends the optimal detergent. For component analysis, image analysis technology is used to estimate the components based on the color and texture of the dirt. This allows for effective cleaning by recommending the optimal detergent based on the components of the dirt.

[0031] The dirt analysis unit displays the extent and depth of dirt in a 3D model, making it possible to suggest more detailed cleaning methods. The dirt analysis unit, for example, uses generative AI to display the extent and depth of dirt in a 3D model. For example, generative AI analyzes a photo of dirt and displays the extent and depth of the dirt in a 3D model. For example, it displays the extent and thickness of the dirt in three dimensions, allowing the user to understand it visually. This makes it possible to suggest more detailed cleaning methods by displaying the extent and depth of dirt in a 3D model.

[0032] The dirt analysis unit can refer to past cleaning history and suggest effective cleaning methods for similar dirt. The dirt analysis unit can, for example, use generation AI to refer to past cleaning history and suggest effective cleaning methods for similar dirt. For example, if the same type of dirt has been found in the past, the generation AI will make a suggestion based on the cleaning method used at that time. In this way, by referring to past cleaning history, it is possible to suggest effective cleaning methods for similar dirt.

[0033] The dirt analysis unit can simulate how dirt looks under different lighting conditions and suggest the optimal cleaning time. The dirt analysis unit can, for example, use a generation AI to simulate how dirt looks under different lighting conditions and suggest the optimal cleaning time. For example, the generation AI compares how dirt looks under natural light in the daytime and artificial light at night. This allows the optimal cleaning time to be suggested by simulating how dirt looks under different lighting conditions.

[0034] The list generation unit can automatically recognize existing tools or detergents in the user's home and suggest ways to utilize them. For example, the list generation unit can automatically recognize tools and detergents in the user's home using a generation AI and suggest ways to utilize them. For example, the generation AI can use a camera to scan tools and detergents and identify those that can be used. This allows the user to use existing tools and detergents in their home, reducing unnecessary purchases and cleaning more efficiently.

[0035] The list generation unit can add environmentally friendly options to the list of suggested tools and detergents, and suggest environmentally friendly cleaning methods. For example, the list generation unit uses the generation AI to add eco-friendly options to the list of suggested tools and detergents. For example, the generation AI suggests environmentally friendly detergents and reusable cleaning tools. This makes it possible to suggest eco-friendly options and do environmentally friendly cleaning.

[0036] The list generation unit can add instructions for making DIY cleaning tools to the list of suggested tools and detergents. For example, the list generation unit adds instructions for making DIY cleaning tools to the list of suggested tools and detergents using the generation AI. For example, the generation AI provides recipes for cleaning tools that can be easily made using materials found at home. This allows for effective cleaning while keeping costs down by suggesting how to make DIY cleaning tools.

[0037] The list generation unit can add information about what is available at local recycle shops or flea markets to the list of suggested tools and detergents. For example, the list generation unit uses the generation AI to add information about what is available at local recycle shops or flea markets to the list of suggested tools and detergents. For example, the generation AI displays the locations and business hours of nearby recycle shops. This makes it possible to increase eco-friendly choices by providing information about what is available at local recycle shops and flea markets.

[0038] The suggestion unit can monitor the user's cleaning actions in real time and provide optimal actions. The suggestion unit, for example, uses a generation AI to monitor the user's cleaning actions in real time and provide feedback on optimal actions. For example, the generation AI analyzes the user's movements using a camera or sensor and instructs the user on efficient actions. This enables efficient cleaning by monitoring the user's cleaning actions in real time and providing feedback on optimal actions.

[0039] The suggestion unit can analyze the cleaning progress in real time and dynamically suggest the next area to clean. The suggestion unit, for example, uses a generation AI to analyze the cleaning progress in real time and dynamically suggest the next area to clean. For example, the generation AI automatically recognizes an area where cleaning has been completed and indicates the next area to clean. This enables efficient cleaning by analyzing the cleaning progress in real time and dynamically suggesting the next area to clean.

[0040] The suggestion unit can add a task sharing function to the suggested cleaning method that encourages cooperation with family members or housemates. For example, the suggestion unit adds a task sharing function to the suggested cleaning method using the generation AI that encourages cooperation with family members or housemates. For example, the generation AI automatically assigns cleaning tasks appropriate for each member. By adding a task sharing function that encourages cooperation with family members or housemates, efficient cleaning becomes possible.

[0041] The suggestion unit can add suggestions for interior decoration after cleaning to the suggested cleaning method, thereby increasing motivation to clean. For example, the suggestion unit uses the generation AI to add suggestions for interior design after cleaning to the suggested cleaning method. For example, the generation AI provides ideas for the layout and decoration of the room after cleaning is completed. In this way, adding suggestions for interior design after cleaning can increase motivation to clean.

[0042] The suggestion unit can analyze the movement patterns of the automatic vacuum cleaner and update the optimal cleaning route in real time. The suggestion unit, for example, uses a generation AI to analyze the movement patterns of the automatic vacuum cleaner and update the optimal cleaning route in real time. For example, the generation AI automatically adjusts the route to avoid obstacles. This enables efficient cleaning by analyzing the movement patterns of the automatic vacuum cleaner and updating the optimal cleaning route in real time.

[0043] The suggestion unit can analyze the sensor information of the automatic vacuum cleaner and provide advanced guidance to avoid obstacles. The suggestion unit, for example, uses a generation AI to analyze the sensor information of the automatic vacuum cleaner and provide advanced navigation to avoid obstacles. For example, the generation AI automatically sets a route to avoid furniture and walls. This enables efficient cleaning by analyzing the sensor information of the automatic vacuum cleaner and providing advanced navigation to avoid obstacles.

[0044] The suggestion unit can analyze the movement patterns of the automatic vacuum cleaner and suggest a safe cleaning route that takes into account the movements of pets or children. For example, the suggestion unit uses a generation AI to analyze the movement patterns of the automatic vacuum cleaner and suggest a safe cleaning route that takes into account the movements of pets or children. For example, the generation AI sets a route that avoids areas where pets or children are present. This allows the suggestion of a safe cleaning route that takes into account the movements of pets or children, allowing the user to clean with peace of mind.

[0045] The suggestion unit can analyze the operation pattern of the automatic vacuum cleaner and suggest adjustments to minimize the noise generated during cleaning. For example, the suggestion unit uses a generation AI to analyze the operation pattern of the automatic vacuum cleaner and suggest settings to minimize the noise generated during cleaning. For example, the generation AI provides settings for quiet mode or for cleaning at specific times of the day. This allows cleaning to be performed in a quiet environment by suggesting settings to minimize the noise generated during cleaning.

[0046] The suggestion unit can analyze the progress of the dirt and propose measures to prevent recurrence. For example, the suggestion unit uses a generation AI to analyze the progress of the dirt and propose preventive measures to prevent recurrence. For example, if dirt is likely to recur in a specific location, the generation AI suggests regular cleaning of that location. This enables effective cleaning by analyzing the progress of the dirt and proposing preventive measures to prevent recurrence.

[0047] The suggestion unit can analyze the progress of dirt and propose a cleaning schedule according to a specific season or weather conditions. For example, the suggestion unit uses a generation AI to analyze the progress of dirt and propose a cleaning schedule according to a specific season or weather conditions. For example, the generation AI instructs the user to focus on cleaning to prevent mold during the rainy season. This allows for effective cleaning by proposing a cleaning schedule according to a specific season or weather conditions.

[0048] The suggestion unit can analyze the progress of dirt and provide a benchmark by comparing it with the cleaning histories of other users. The suggestion unit, for example, uses a generation AI to analyze the progress of dirt and provide a benchmark by comparing it with the cleaning histories of other users. For example, the generation AI compares the cleaning frequency and effectiveness for the same type of dirt. This allows for effective cleaning by providing a benchmark by comparing it with the cleaning histories of other users.

[0049] The suggestion unit can analyze the progress of the dirt and provide a video of an effective cleaning method for a specific dirt. The suggestion unit, for example, uses a generation AI to analyze the progress of the dirt and provide a video of an effective cleaning method for a specific dirt. For example, the generation AI shows a video of specific cleaning procedures for oil stains. In this way, by providing a video of an effective cleaning method for a specific dirt, it becomes easy to understand visually and enables effective cleaning.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The list generator can automatically recognize existing tools or detergents in the user's home and suggest ways to utilize them. For example, the generator AI can use a camera to scan tools and detergents and identify which ones are usable. This allows the user to use existing tools and detergents in their home, reducing unnecessary purchases and cleaning more efficiently.

[0052] The suggestion unit can add a task sharing function to the proposed cleaning method that encourages cooperation among family members or housemates. For example, the generation AI automatically assigns cleaning tasks appropriate for each member. This allows for efficient cleaning by adding a task sharing function that encourages cooperation among family members or housemates.

[0053] The suggestion unit analyzes the movement patterns of the automatic vacuum cleaner and updates the optimal cleaning route in real time. For example, the generation AI automatically adjusts the route to avoid obstacles. This allows the automatic vacuum cleaner to analyze its movement patterns and update the optimal cleaning route in real time, enabling efficient cleaning.

[0054] The suggestion unit can analyze the progress of the dirt and propose measures to prevent recurrence. For example, if dirt is likely to recur in a particular location, the generative AI will suggest regular cleaning of that location. This allows for effective cleaning by analyzing the progress of the dirt and proposing preventive measures to prevent recurrence.

[0055] The suggestion section can analyze the progress of dirt and propose a cleaning schedule according to specific seasons or weather conditions. For example, the generation AI can instruct users to focus on cleaning to prevent mold during the rainy season. This allows for effective cleaning by proposing a cleaning schedule according to specific seasons and weather conditions.

[0056] The suggestion unit can analyze the progress of the dirt and provide effective cleaning methods for specific stains in the form of video. For example, the generation AI can show specific cleaning procedures for oil stains in the form of a video. This makes it easy to understand visually how to clean specific stains, enabling effective cleaning.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The dirt analysis unit analyzes the photo of the dirt taken by the user. For example, the dirt analysis unit uses generative AI to identify the type of dirt, such as oil stains, water stains, or mold. Step 2: The suggestion unit proposes the optimal cleaning method, tools, and detergents to use based on the type of dirt analyzed by the dirt analysis unit. For example, the suggestion unit uses generative AI to propose the optimal cleaning method, and suggests using a specific detergent for oil stains. Step 3: The list generation unit generates a list of necessary tools and detergents based on the cleaning method proposed by the suggestion unit. For example, the list generation unit uses a generation AI to generate a list of necessary tools and detergents, listing the names and quantities of the tools and detergents needed for cleaning. Step 4: The link providing unit provides links and information for purchases at online stores and nearby stores based on the list generated by the list generating unit. For example, the link providing unit uses a generation AI to provide links and information for online stores and nearby stores, and provides links to online stores and nearby stores specified by the user.

[0059] (Example 2) The cleaning support system according to an embodiment of the present invention analyzes photos of dirt taken by a user, suggests optimal cleaning methods, tools, and detergents, and allows the user to easily obtain the necessary tools and detergents. This eliminates the user's cleaning worries and enables efficient and effective cleaning.

[0060] A cleaning assistance system according to an embodiment includes a stain analysis unit, a suggestion unit, a list generation unit, and a link provision unit. The stain analysis unit analyzes a photo of a stain taken by a user. For example, the stain analysis unit identifies the type of stain using a generation AI. The generation AI distinguishes between types such as oil stains, water stains, and mold. The suggestion unit suggests an optimal cleaning method, tools, and detergents to use based on the type of stain analyzed by the stain analysis unit. For example, the suggestion unit suggests an optimal cleaning method using a generation AI. For example, the generation AI suggests using a specific detergent for oil stains. The list generation unit generates a list of necessary tools and detergents based on the cleaning method suggested by the suggestion unit. For example, the list generation unit generates a list of necessary tools and detergents using a generation AI. The generation AI lists, for example, the names and quantities of tools and detergents needed for cleaning. The link provision unit provides links and information for purchasing tools and detergents at online stores and nearby stores based on the list generated by the list generation unit. For example, the link provision unit provides links and information for online stores and nearby stores using a generation AI. The generation AI provides, for example, links to online stores or nearby stores specified by the user. This allows the cleaning assistance system according to the embodiment to eliminate the user's cleaning worries and enable efficient and effective cleaning. For example, the user can learn the optimal cleaning method for each type of dirt and easily obtain the necessary tools and detergents. Furthermore, by using the links to online stores or nearby stores, the user can easily purchase the necessary tools and detergents.

[0061] The dirt analysis unit analyzes the components of the dirt and can recommend the optimal detergent based on the components. The dirt analysis unit uses, for example, generative AI to analyze the components of the dirt. For example, generative AI analyzes a photo of the dirt and identifies the components of the dirt. For example, in the case of an oily stain, generative AI detects the oily components and recommends the optimal detergent. For component analysis, image analysis technology is used to estimate the components based on the color and texture of the dirt. This allows for effective cleaning by recommending the optimal detergent based on the components of the dirt.

[0062] The dirt analysis unit displays the extent and depth of dirt in a 3D model, making it possible to suggest more detailed cleaning methods. The dirt analysis unit, for example, uses generative AI to display the extent and depth of dirt in a 3D model. For example, generative AI analyzes a photo of dirt and displays the extent and depth of the dirt in a 3D model. For example, it displays the extent and thickness of the dirt in three dimensions, allowing the user to understand it visually. This makes it possible to suggest more detailed cleaning methods by displaying the extent and depth of dirt in a 3D model.

[0063] The suggestion unit can analyze the user's emotions, evaluate the stress level regarding dirt, and suggest cleaning methods to reduce stress. The suggestion unit, for example, uses a generation AI to analyze the user's emotions and evaluate the stress level regarding dirt. For example, the generation AI analyzes the user's facial expressions and voice and quantifies the stress level. This allows the burden of cleaning to be reduced by analyzing the user's emotions and suggesting cleaning methods to reduce stress.

[0064] The dirt analysis unit can refer to past cleaning history and suggest effective cleaning methods for similar dirt. The dirt analysis unit can, for example, use generation AI to refer to past cleaning history and suggest effective cleaning methods for similar dirt. For example, if the same type of dirt has been found in the past, the generation AI will make a suggestion based on the cleaning method used at that time. In this way, by referring to past cleaning history, it is possible to suggest effective cleaning methods for similar dirt.

[0065] The dirt analysis unit can simulate how dirt looks under different lighting conditions and suggest the optimal cleaning time. The dirt analysis unit can, for example, use a generation AI to simulate how dirt looks under different lighting conditions and suggest the optimal cleaning time. For example, the generation AI compares how dirt looks under natural light in the daytime and artificial light at night. This allows the optimal cleaning time to be suggested by simulating how dirt looks under different lighting conditions.

[0066] The suggestion unit can analyze the user's emotions and suggest music or messages to motivate them when cleaning. The suggestion unit, for example, uses a generation AI to analyze the user's emotions and suggest music to motivate them when cleaning. For example, the generation AI provides a playlist tailored to the user's preferences. This improves cleaning efficiency by analyzing the user's emotions and suggesting music or messages to motivate them.

[0067] The list generation unit can automatically recognize existing tools or detergents in the user's home and suggest ways to utilize them. For example, the list generation unit can automatically recognize tools and detergents in the user's home using a generation AI and suggest ways to utilize them. For example, the generation AI can use a camera to scan tools and detergents and identify those that can be used. This allows the user to use existing tools and detergents in their home, reducing unnecessary purchases and cleaning more efficiently.

[0068] The list generation unit can add environmentally friendly options to the list of suggested tools and detergents, and suggest environmentally friendly cleaning methods. For example, the list generation unit uses the generation AI to add eco-friendly options to the list of suggested tools and detergents. For example, the generation AI suggests environmentally friendly detergents and reusable cleaning tools. This makes it possible to suggest eco-friendly options and do environmentally friendly cleaning.

[0069] The list generation unit can add instructions for making DIY cleaning tools to the list of suggested tools and detergents. For example, the list generation unit adds instructions for making DIY cleaning tools to the list of suggested tools and detergents using the generation AI. For example, the generation AI provides recipes for cleaning tools that can be easily made using materials found at home. This allows for effective cleaning while keeping costs down by suggesting how to make DIY cleaning tools.

[0070] The list generation unit can add information about what is available at local recycle shops or flea markets to the list of suggested tools and detergents. For example, the list generation unit uses the generation AI to add information about what is available at local recycle shops or flea markets to the list of suggested tools and detergents. For example, the generation AI displays the locations and business hours of nearby recycle shops. This makes it possible to increase eco-friendly choices by providing information about what is available at local recycle shops and flea markets.

[0071] The list generation unit can use the emotion estimation function to provide discount information for online stores to reduce the burden felt by the user when shopping. The list generation unit, for example, uses a generation AI to analyze the user's emotions and provide online shopping discount information to reduce the burden felt when shopping. For example, the generation AI displays discount coupons for specific stores or products. In this way, the burden of shopping can be reduced by using the emotion estimation function to provide online shopping discount information to reduce the burden felt by the user when shopping.

[0072] The suggestion unit can monitor the user's cleaning actions in real time and provide optimal actions. The suggestion unit, for example, uses a generation AI to monitor the user's cleaning actions in real time and provide feedback on optimal actions. For example, the generation AI analyzes the user's movements using a camera or sensor and instructs the user on efficient actions. This enables efficient cleaning by monitoring the user's cleaning actions in real time and providing feedback on optimal actions.

[0073] The suggestion unit can analyze the cleaning progress in real time and dynamically suggest the next area to clean. The suggestion unit, for example, uses a generation AI to analyze the cleaning progress in real time and dynamically suggest the next area to clean. For example, the generation AI automatically recognizes an area where cleaning has been completed and indicates the next area to clean. This enables efficient cleaning by analyzing the cleaning progress in real time and dynamically suggesting the next area to clean.

[0074] The suggestion unit can analyze the user's emotions, evaluate the user's fatigue when cleaning, and suggest when to take a break. The suggestion unit, for example, uses a generation AI to analyze the user's emotions and evaluate the level of fatigue when cleaning. For example, the generation AI analyzes the user's facial expressions and voice and quantifies the level of fatigue. This enables efficient cleaning by analyzing the user's emotions, evaluating the level of fatigue, and suggesting when to take a break.

[0075] The suggestion unit can add a task sharing function to the suggested cleaning method that encourages cooperation with family members or housemates. For example, the suggestion unit adds a task sharing function to the suggested cleaning method using the generation AI that encourages cooperation with family members or housemates. For example, the generation AI automatically assigns cleaning tasks appropriate for each member. By adding a task sharing function that encourages cooperation with family members or housemates, efficient cleaning becomes possible.

[0076] The suggestion unit can add suggestions for interior decoration after cleaning to the suggested cleaning method, thereby increasing motivation to clean. For example, the suggestion unit uses the generation AI to add suggestions for interior design after cleaning to the suggested cleaning method. For example, the generation AI provides ideas for the layout and decoration of the room after cleaning is completed. In this way, adding suggestions for interior design after cleaning can increase motivation to clean.

[0077] The suggestion unit can analyze the user's emotions and suggest cleaning methods that incorporate game elements to make cleaning more enjoyable. For example, the suggestion unit uses a generation AI to analyze the user's emotions and suggest cleaning methods that incorporate game elements to make cleaning more enjoyable. For example, the generation AI introduces a system that earns points according to the progress of cleaning. This can increase motivation to clean by incorporating game elements to make cleaning more enjoyable.

[0078] The suggestion unit can analyze the movement patterns of the automatic vacuum cleaner and update the optimal cleaning route in real time. The suggestion unit, for example, uses a generation AI to analyze the movement patterns of the automatic vacuum cleaner and update the optimal cleaning route in real time. For example, the generation AI automatically adjusts the route to avoid obstacles. This enables efficient cleaning by analyzing the movement patterns of the automatic vacuum cleaner and updating the optimal cleaning route in real time.

[0079] The suggestion unit can analyze the sensor information of the automatic vacuum cleaner and provide advanced guidance to avoid obstacles. The suggestion unit, for example, uses a generation AI to analyze the sensor information of the automatic vacuum cleaner and provide advanced navigation to avoid obstacles. For example, the generation AI automatically sets a route to avoid furniture and walls. This enables efficient cleaning by analyzing the sensor information of the automatic vacuum cleaner and providing advanced navigation to avoid obstacles.

[0080] The suggestion unit can analyze the user's emotions and provide an alert function to reduce the anxiety felt by the automatic vacuum cleaner when it operates. The suggestion unit, for example, uses a generation AI to analyze the user's emotions and provide a notification function to reduce the anxiety felt by the automatic vacuum cleaner when it operates. For example, the generation AI sends notifications about the progress of cleaning and when the cleaning is completed. This allows the user to clean with peace of mind by analyzing the user's emotions and providing a notification function to reduce the anxiety felt by the automatic vacuum cleaner when it operates.

[0081] The suggestion unit can analyze the movement patterns of the automatic vacuum cleaner and suggest a safe cleaning route that takes into account the movements of pets or children. For example, the suggestion unit uses a generation AI to analyze the movement patterns of the automatic vacuum cleaner and suggest a safe cleaning route that takes into account the movements of pets or children. For example, the generation AI sets a route that avoids areas where pets or children are present. This allows the suggestion of a safe cleaning route that takes into account the movements of pets or children, allowing the user to clean with peace of mind.

[0082] The suggestion unit can analyze the operation pattern of the automatic vacuum cleaner and suggest adjustments to minimize the noise generated during cleaning. For example, the suggestion unit uses a generation AI to analyze the operation pattern of the automatic vacuum cleaner and suggest settings to minimize the noise generated during cleaning. For example, the generation AI provides settings for quiet mode or for cleaning at specific times of the day. This allows cleaning to be performed in a quiet environment by suggesting settings to minimize the noise generated during cleaning.

[0083] The suggestion unit can analyze the user's emotions and provide relaxing music to reduce stress when monitoring the operation of the automatic vacuum cleaner. The suggestion unit, for example, uses a generation AI to analyze the user's emotions and provide relaxing music to reduce stress when monitoring the operation of the automatic vacuum cleaner. For example, the generation AI plays relaxing music that matches the user's preferences. In this way, by providing relaxing music to reduce stress when monitoring the operation of the automatic vacuum cleaner, the user can clean in a relaxed manner.

[0084] The suggestion unit can analyze the progress of the dirt and propose measures to prevent recurrence. For example, the suggestion unit uses a generation AI to analyze the progress of the dirt and propose preventive measures to prevent recurrence. For example, if dirt is likely to recur in a specific location, the generation AI suggests regular cleaning of that location. This enables effective cleaning by analyzing the progress of the dirt and proposing preventive measures to prevent recurrence.

[0085] The suggestion unit can analyze the progress of dirt and propose a cleaning schedule according to a specific season or weather conditions. For example, the suggestion unit uses a generation AI to analyze the progress of dirt and propose a cleaning schedule according to a specific season or weather conditions. For example, the generation AI instructs the user to focus on cleaning to prevent mold during the rainy season. This allows for effective cleaning by proposing a cleaning schedule according to a specific season or weather conditions.

[0086] The suggestion unit can analyze the user's emotions and provide advice to reduce the anxiety felt by the user regarding the progress of the dirt. The suggestion unit, for example, uses a generation AI to analyze the user's emotions and provide advice to reduce the anxiety felt by the progress of the dirt. For example, the generation AI suggests a cleaning method that makes it less likely for the dirt to recur. This allows the user to clean with peace of mind by providing advice to reduce the anxiety felt by the progress of the dirt.

[0087] The suggestion unit can analyze the progress of dirt and provide a benchmark by comparing it with the cleaning histories of other users. The suggestion unit, for example, uses a generation AI to analyze the progress of dirt and provide a benchmark by comparing it with the cleaning histories of other users. For example, the generation AI compares the cleaning frequency and effectiveness for the same type of dirt. This allows for effective cleaning by providing a benchmark by comparing it with the cleaning histories of other users.

[0088] The suggestion unit can analyze the progress of the dirt and provide a video of an effective cleaning method for a specific dirt. The suggestion unit, for example, uses a generation AI to analyze the progress of the dirt and provide a video of an effective cleaning method for a specific dirt. For example, the generation AI shows a video of specific cleaning procedures for oil stains. In this way, by providing a video of an effective cleaning method for a specific dirt, it becomes easy to understand visually and enables effective cleaning.

[0089] The suggestion unit can analyze the user's emotions and provide a reminder function to reduce stress when monitoring the progress of dirt. The suggestion unit, for example, uses the generation AI to analyze the user's emotions and provide a reminder function to reduce stress when monitoring the progress of dirt. For example, the generation AI sends reminders for regular cleaning. This allows the user to clean with peace of mind by providing a reminder function to reduce stress when monitoring the progress of dirt.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The suggestion unit can analyze the user's emotions and suggest music or messages to motivate them while cleaning. For example, the generation AI can provide a playlist tailored to the user's preferences. This improves cleaning efficiency by analyzing the user's emotions and suggesting music or messages to motivate them.

[0092] The list generator can automatically recognize existing tools or detergents in the user's home and suggest ways to utilize them. For example, the generator AI can use a camera to scan tools and detergents and identify which ones are usable. This allows the user to use existing tools and detergents in their home, reducing unnecessary purchases and cleaning more efficiently.

[0093] The suggestion unit can analyze the user's emotions, evaluate their fatigue while cleaning, and suggest break times. For example, the generation AI can analyze the user's facial expressions and voice and quantify their fatigue level. This allows the system to analyze the user's emotions, evaluate their fatigue level, and suggest break times, enabling efficient cleaning.

[0094] The suggestion unit can add a task sharing function to the proposed cleaning method that encourages cooperation among family members or housemates. For example, the generation AI automatically assigns cleaning tasks appropriate for each member. This allows for efficient cleaning by adding a task sharing function that encourages cooperation among family members or housemates.

[0095] The suggestion unit can analyze the user's emotions and suggest cleaning methods that incorporate playful elements to make cleaning more enjoyable. For example, the generation AI can introduce a system that earns points based on the cleaning progress. This can increase motivation to clean by incorporating game elements to make cleaning more enjoyable.

[0096] The suggestion unit analyzes the movement patterns of the automatic vacuum cleaner and updates the optimal cleaning route in real time. For example, the generation AI automatically adjusts the route to avoid obstacles. This allows the automatic vacuum cleaner to analyze its movement patterns and update the optimal cleaning route in real time, enabling efficient cleaning.

[0097] The suggestion unit can analyze the progress of the dirt and propose measures to prevent recurrence. For example, if dirt is likely to recur in a particular location, the generative AI will suggest regular cleaning of that location. This allows for effective cleaning by analyzing the progress of the dirt and proposing preventive measures to prevent recurrence.

[0098] The suggestion unit can analyze the user's emotions and provide advice to reduce anxiety about the progress of the stain. For example, the generation AI can suggest cleaning methods that are less likely to cause the stain to recur. This allows the user to clean with peace of mind by providing advice to reduce anxiety about the progress of the stain.

[0099] The suggestion section can analyze the progress of dirt and propose a cleaning schedule according to specific seasons or weather conditions. For example, the generation AI can instruct users to focus on cleaning to prevent mold during the rainy season. This allows for effective cleaning by proposing a cleaning schedule according to specific seasons and weather conditions.

[0100] The suggestion unit can analyze the progress of the dirt and provide effective cleaning methods for specific stains in the form of video. For example, the generation AI can show specific cleaning procedures for oil stains in the form of a video. This makes it easy to understand visually how to clean specific stains, enabling effective cleaning.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The dirt analysis unit analyzes the photo of the dirt taken by the user. For example, the dirt analysis unit uses generative AI to identify the type of dirt, such as oil stains, water stains, or mold. Step 2: The suggestion unit proposes the optimal cleaning method, tools, and detergents to use based on the type of dirt analyzed by the dirt analysis unit. For example, the suggestion unit uses generative AI to propose the optimal cleaning method, and suggests using a specific detergent for oil stains. Step 3: The list generation unit generates a list of necessary tools and detergents based on the cleaning method proposed by the suggestion unit. For example, the list generation unit uses a generation AI to generate a list of necessary tools and detergents, listing the names and quantities of the tools and detergents needed for cleaning. Step 4: The link providing unit provides links and information for purchases at online stores and nearby stores based on the list generated by the list generating unit. For example, the link providing unit uses a generation AI to provide links and information for online stores and nearby stores, and provides links to online stores and nearby stores specified by the user.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 dirt analysis unit that analyzes a photo of dirt taken by a user; a suggestion unit that suggests an optimal cleaning method, tools to be used, and detergent based on the type of dirt analyzed by the dirt analysis unit; a list generation unit that generates a list of necessary tools and detergents based on the cleaning method suggested by the suggestion unit; a link providing unit that provides links and information for purchasing at online stores and nearby stores based on the list generated by the list generating unit. A system characterized by:

2. The dirt analysis unit Analyzes the components of the dirt and suggests the best detergent based on the components 2. The system of claim 1.

3. The list generation unit Automatically recognize existing tools or cleaning products in the user's home and suggest ways to utilize them 2. The system of claim 1.

4. The proposal unit Monitors user cleaning behavior in real time and provides optimal behavior 2. The system of claim 1.

5. The proposal unit Analyzes the movement patterns of the automatic vacuum cleaner and updates the optimal cleaning path in real time 2. The system of claim 1.

6. The proposal unit Analyzes user emotions, evaluates stress caused by dirt, and suggests cleaning methods to reduce stress 2. The system of claim 1.

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