System

Abstract

An object of a system according to an embodiment is to appropriately record and compare states of a rental property at the time of moving in and at the time of moving out to prevent a trouble.SOLUTION: A system includes an image acquisition unit, an analysis unit, a comparison unit, a recommendation unit, and an advice unit. The image acquisition unit acquires an image captured by the smartphone camera. The analysis unit analyzes the image acquired by the image acquisition unit. The comparison unit compares the image at the time of moving in and the image at the time of moving out analyzed by the analysis unit. The recommendation unit recommends the furniture home appliance based on a result of the comparison by the comparison unit. The advice unit provides advice for using the room without soiling the room on the basis of the image analyzed by the analysis 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] Conventional technology has the problem of making it difficult to properly record and compare the condition of rental properties at the time of occupancy and at the time of occupancy, and to prevent problems.

[0005] The system according to the embodiment aims to prevent trouble by properly recording and comparing the condition of a rental property at the time of moving in and moving out. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, an analysis unit, a comparison unit, a recommendation unit, and an advice unit. The image acquisition unit acquires images taken with a smartphone camera. The analysis unit analyzes the images acquired by the image acquisition unit. The comparison unit compares the images analyzed by the analysis unit between the images taken at the time of moving in and the images taken at the time of moving out. The recommendation unit recommends furniture and home appliances based on the results of the comparison by the comparison unit. The advice unit provides advice on how to keep a room clean based on the images analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately record and compare the condition of a rental property at the time of moving in and moving out, thereby preventing trouble. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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) A system according to an embodiment of the present invention is designed to alleviate concerns about living in a rental property. This system records the current state of the property using images taken with a smartphone camera, and then takes new images when the tenant moves out and compares them with the images taken at the time of moving in to calculate a reasonable cost. Based on the floor plan and photographed data, the system also provides recommendations for furniture and appliances that are suitable for the room, not just for rental properties, and advice on how to keep the room clean. This allows the system to alleviate concerns about living in a rental property and support a comfortable lifestyle.

[0029] The system according to the embodiment includes an image acquisition unit, an analysis unit, a comparison unit, a recommendation unit, and an advice unit. The image acquisition unit acquires images captured with a smartphone camera. For example, it can acquire images of the overall room or specific furniture or equipment. The image acquisition unit can also acquire images through a smartphone camera application. The analysis unit analyzes the acquired images. For example, it uses image recognition technology to record the detailed condition of walls, floors, ceilings, equipment, etc. The analysis unit can also extract image features using an AI algorithm and record the current status. The comparison unit compares images analyzed by the analysis unit taken at the time of occupancy with those taken at the time of occupancy. For example, it calculates the degree of similarity between the images and detects whether there have been any changes. The comparison unit can also identify areas of change and record the details. The recommendation unit recommends furniture and appliances based on the results of the comparison by the comparison unit. For example, it can suggest sofas and tables of optimal sizes based on the size and layout of the room. The recommendation unit can also make recommendations taking into account the user's preferences and past purchase history. The advice unit provides advice on how to keep the room clean based on the images analyzed by the analysis unit. For example, it suggests how often to clean and what cleaning tools to use. The advice unit can also provide tips for daily maintenance. This allows the system to eliminate concerns when living in a rental property and support a comfortable life. For example, by recording the current situation in detail when moving in, it is possible to prevent problems when moving out. It can also create a comfortable living environment by recommending furniture and appliances that suit the room. Furthermore, by receiving advice on how to keep the room clean, it is possible to keep the room clean for a long period of time.

[0030] The analysis unit automatically generates a 3D model of the room from images, allowing for detailed current status records. For example, when a resident moves in, the analysis unit takes photos of each part of the room with a smartphone camera and loads the images into the generation AI. The generation AI analyzes the images and automatically generates a 3D model of the room. For example, it accurately reproduces the position and condition of the walls, floor, and ceiling, allowing for detailed current status records. This allows for detailed recording of the current status of the room.

[0031] When analyzing images, the analysis unit simultaneously records environmental data such as room temperature and humidity, allowing it to predict future deterioration. For example, when a resident moves in, the analysis unit takes photos of various parts of the room with a smartphone camera and loads the images into the generation AI. When analyzing images, the generation AI simultaneously records environmental data such as room temperature and humidity. For example, it measures the room temperature and humidity with a sensor and records them together with the image data. This makes it possible to predict deterioration in the room and propose appropriate countermeasures.

[0032] The analysis unit records the current situation at the time of occupancy in voice memos and video format, which the generation AI can then analyze and convert into text. For example, the analysis unit takes photos of various parts of the room with a smartphone camera when the occupant moves in and loads the images into the generation AI. The generation AI records not only images, but also voice memos and video formats, which it then analyzes and converts into text. For example, it converts the contents of a resident's voice explanation into text. This allows the current situation to be recorded in voice memos and video formats and converted into text.

[0033] The analysis unit can share the current status record at the time of occupancy with other residents, promoting community-based information exchange. For example, the analysis unit takes photos of each part of the room with a smartphone camera when a resident moves in and loads the images into the generation AI. The generation AI can then share the current status record at the time of occupancy with other residents, promoting community-based information exchange. For example, information can be shared with other residents living in the same property. This promotes information exchange between residents and can suggest improvements to the property.

[0034] When the comparison unit compares images taken at the time of occupancy and at the time of departure, the AI ​​can automatically identify areas that can be repaired and calculate the repair costs. For example, when the occupant moves out, the comparison unit takes photos of each area of ​​the room with a smartphone camera and loads the images into the generation AI. The generation AI compares the images taken at the time of occupancy and at the time of departure, automatically identifies areas that can be repaired, and calculates the repair costs. For example, it identifies scratches on the walls and stains on the floor and calculates the repair costs. This makes it possible to identify areas that can be repaired and calculate the repair costs.

[0035] When comparing images, the comparison unit takes into account the frequency and method of use of the room, allowing for more accurate cost calculations. For example, when a resident moves out, the comparison unit takes photos of each part of the room with a smartphone camera and loads the images into the generation AI. When comparing images, the generation AI takes into account the frequency and method of use of the room, allowing for more accurate cost calculations. For example, if the living room is used frequently, the generation AI will take into account its deterioration when calculating costs. This allows for more accurate cost calculations that take into account the room's usage status.

[0036] The comparison unit compares the comparison results at the time of move-out with data on other rental properties and can calculate costs based on market prices. For example, the comparison unit takes photos of each part of the room with a smartphone camera when the tenant moves out and loads the images into the generation AI. The generation AI compares the comparison results at the time of move-out with data on other rental properties and calculates costs based on market prices. For example, it references repair cost data for other properties in the same area. This allows costs to be calculated based on market prices.

[0037] The comparison unit allows the tenant and owner to share the comparison results at the time of move-out in real time, enabling a cost calculation that is satisfactory to both parties. For example, the comparison unit takes photos of each part of the room with a smartphone camera when the tenant moves out, and loads the images into the generation AI. The generation AI then shares the comparison results at the time of move-out with the tenant and owner in real time, enabling a cost calculation that is satisfactory to both parties. For example, the comparison results can be shared on an online platform so that both parties can view them. This allows a cost calculation that is satisfactory to both the tenant and the owner.

[0038] The recommendation unit can analyze the design and color of the room and make suggestions for interior coordination. For example, when moving in, the recommendation unit takes photos of various parts of the room with a smartphone camera and loads the images into the generation AI. The generation AI analyzes the design and color of the room and makes suggestions for interior coordination. For example, it suggests furniture and interior items that match the wall color and floor material. This makes it possible to suggest interior coordination based on the room's design and color.

[0039] The recommendation unit makes recommendations for furniture and appliances based on the reviews and ratings of other tenants, thereby increasing reliability. For example, when a tenant moves in, the recommendation unit takes photos of each area of ​​the room with a smartphone camera and loads the images into the generation AI. The generation AI makes recommendations for furniture and appliances based on the reviews and ratings of other tenants, thereby increasing reliability. For example, it can suggest furniture that has been highly rated by other tenants. This allows for highly reliable recommendations based on the reviews and ratings of other tenants.

[0040] The recommendation unit dynamically updates its furniture and appliance recommendations according to the season and trends, making it possible to make the latest suggestions. For example, when a resident moves in, the recommendation unit takes photos of each part of the room with a smartphone camera and loads the images into the generation AI. The generation AI dynamically updates its furniture and appliance recommendations according to the season and trends, making the latest suggestions. For example, it suggests interior items that match the season. This makes it possible to suggest the latest furniture and appliances that match the season and trends.

[0041] The advice unit can analyze images of the room, identify areas that are prone to getting dirty, and suggest specific cleaning methods. For example, when a resident moves in, the advice unit takes photos of each area of ​​the room with a smartphone camera and loads the images into the generating AI. The generating AI then analyzes the images of the room, identifies areas that are prone to getting dirty, and suggests specific cleaning methods. For example, it suggests ways to prevent grease stains in the kitchen and mold in the bathroom. This makes it possible to identify areas that are prone to getting dirty and suggest specific cleaning methods.

[0042] The advice unit can monitor room usage and automatically generate a regular maintenance schedule. For example, when a resident moves in, the advice unit takes photos of each part of the room with a smartphone camera and loads the images into the generation AI. The generation AI monitors room usage and automatically generates a regular maintenance schedule. For example, it suggests cleaning schedules for the kitchen and bathroom. This makes it possible to monitor room usage and automatically generate a regular maintenance schedule.

[0043] The advice unit can customize its advice for keeping the room clean based on the experience and knowledge of other residents. For example, when a resident moves in, the advice unit takes photos of various parts of the room with a smartphone camera and loads the images into the generating AI. The generating AI then customizes its advice for keeping the room clean based on the experience and knowledge of other residents. For example, it can suggest cleaning methods that other residents use. This makes it possible to provide customized advice based on the experience and knowledge of other residents.

[0044] The advice unit dynamically updates its advice for keeping the room clean according to the season and weather, making optimal suggestions. For example, when a resident moves in, the advice unit takes photos of various parts of the room with a smartphone camera and loads the images into the generating AI. The generating AI dynamically updates its advice for keeping the room clean according to the season and weather, making optimal suggestions. For example, it suggests measures to prevent mold during the rainy season. This makes it possible to provide optimal advice according to the season and weather.

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

[0046] When performing image analysis, the analysis unit also records the acoustic characteristics of the room and can make suggestions for future acoustic improvements. For example, it measures the room's reverberation and noise levels and suggests appropriate acoustic measures. This allows the room's acoustic environment to be optimized, supporting a more comfortable lifestyle.

[0047] The recommendation unit can suggest optimal lighting arrangements based on the room layout. For example, it can suggest the optimal position and type of lighting fixtures depending on the size of the room and the arrangement of furniture. This helps maintain uniform brightness throughout the room, providing a comfortable living environment.

[0048] The advice module analyzes images of the room and can suggest the placement and type of plants. For example, it can suggest the most suitable houseplants based on the amount of light and humidity in the room. This can improve the air quality in the room and provide a relaxing environment.

[0049] The advice unit can analyze images of a room and suggest optimal storage methods, such as furniture and storage ideas for making the most effective use of space in the room. This helps keep the room tidy and provides a comfortable living environment.

[0050] The advice unit can analyze images of the room and suggest optimal security measures. For example, it can suggest installing security cameras or security systems based on the location of windows and doors in the room. This ensures the safety of residents and provides a safe and secure living environment.

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

[0052] Step 1: The image acquisition unit acquires images taken with a smartphone camera. For example, it can acquire an image of the entire room or specific furniture or equipment. The image acquisition unit can also acquire images through a smartphone camera application. Step 2: The analysis unit analyzes the captured images. For example, image recognition technology is used to record the detailed condition of walls, floors, ceilings, and equipment. The analysis unit can also use AI algorithms to extract image features and record the current situation. Step 3: The comparison unit compares the images analyzed by the analysis unit at the time of occupancy and the time of vacating. For example, it calculates the degree of similarity between the images and detects whether there are any changes. The comparison unit can also identify any changes and record the details. Step 4: The recommendation unit recommends furniture and home appliances based on the results of the comparison by the comparison unit. For example, it may suggest sofas and tables of optimal sizes based on the size and layout of the room. The recommendation unit can also make recommendations taking into account the user's preferences and past purchase history. Step 5: The advice unit provides advice on how to keep the room clean based on the images analyzed by the analysis unit. For example, it suggests how often to clean and what cleaning tools to use. The advice unit can also provide tips for daily maintenance.

[0053] (Example 2) A system according to an embodiment of the present invention is designed to alleviate concerns about living in a rental property. This system records the current state of the property using images taken with a smartphone camera, and then takes new images when the tenant moves out and compares them with the images taken at the time of moving in to calculate a reasonable cost. Based on the floor plan and photographed data, the system also provides recommendations for furniture and appliances that are suitable for the room, not just for rental properties, and advice on how to keep the room clean. This allows the system to alleviate concerns about living in a rental property and support a comfortable lifestyle.

[0054] The system according to the embodiment includes an image acquisition unit, an analysis unit, a comparison unit, a recommendation unit, and an advice unit. The image acquisition unit acquires images captured with a smartphone camera. For example, it can acquire images of the overall room or specific furniture or equipment. The image acquisition unit can also acquire images through a smartphone camera application. The analysis unit analyzes the acquired images. For example, it uses image recognition technology to record the detailed condition of walls, floors, ceilings, equipment, etc. The analysis unit can also extract image features using an AI algorithm and record the current status. The comparison unit compares images analyzed by the analysis unit taken at the time of occupancy with those taken at the time of occupancy. For example, it calculates the degree of similarity between the images and detects whether there have been any changes. The comparison unit can also identify areas of change and record the details. The recommendation unit recommends furniture and appliances based on the results of the comparison by the comparison unit. For example, it can suggest sofas and tables of optimal sizes based on the size and layout of the room. The recommendation unit can also make recommendations taking into account the user's preferences and past purchase history. The advice unit provides advice on how to keep the room clean based on the images analyzed by the analysis unit. For example, it suggests how often to clean and what cleaning tools to use. The advice unit can also provide tips for daily maintenance. This allows the system to eliminate concerns when living in a rental property and support a comfortable life. For example, by recording the current situation in detail when moving in, it is possible to prevent problems when moving out. It can also create a comfortable living environment by recommending furniture and appliances that suit the room. Furthermore, by receiving advice on how to keep the room clean, it is possible to keep the room clean for a long period of time.

[0055] The analysis unit automatically generates a 3D model of the room from images, allowing for detailed current status records. For example, when a resident moves in, the analysis unit takes photos of each part of the room with a smartphone camera and loads the images into the generation AI. The generation AI analyzes the images and automatically generates a 3D model of the room. For example, it accurately reproduces the position and condition of the walls, floor, and ceiling, allowing for detailed current status records. This allows for detailed recording of the current status of the room.

[0056] When analyzing images, the analysis unit simultaneously records environmental data such as room temperature and humidity, allowing it to predict future deterioration. For example, when a resident moves in, the analysis unit takes photos of various parts of the room with a smartphone camera and loads the images into the generation AI. When analyzing images, the generation AI simultaneously records environmental data such as room temperature and humidity. For example, it measures the room temperature and humidity with a sensor and records them together with the image data. This makes it possible to predict deterioration in the room and propose appropriate countermeasures.

[0057] The analysis unit uses the emotion estimation function to analyze the resident's emotions and prioritizes detailed recording of areas of particular concern. For example, when the resident moves in, the analysis unit takes photos of each area of ​​the room with a smartphone camera and loads the images into the generation AI. The generation AI uses the emotion estimation function to analyze the resident's emotions and prioritizes detailed recording of areas of particular concern. For example, it can focus on recording areas that make the resident feel anxious. This reduces the resident's anxiety and allows for detailed recording.

[0058] The analysis unit records the current situation at the time of occupancy in voice memos and video format, which the generation AI can then analyze and convert into text. For example, the analysis unit takes photos of various parts of the room with a smartphone camera when the occupant moves in and loads the images into the generation AI. The generation AI records not only images, but also voice memos and video formats, which it then analyzes and converts into text. For example, it converts the contents of a resident's voice explanation into text. This allows the current situation to be recorded in voice memos and video formats and converted into text.

[0059] The analysis unit can share the current status record at the time of occupancy with other residents, promoting community-based information exchange. For example, the analysis unit takes photos of each part of the room with a smartphone camera when a resident moves in and loads the images into the generation AI. The generation AI can then share the current status record at the time of occupancy with other residents, promoting community-based information exchange. For example, information can be shared with other residents living in the same property. This promotes information exchange between residents and can suggest improvements to the property.

[0060] The analysis unit uses the emotion estimation function to detect areas where residents feel particularly anxious in real time and encourage them to keep detailed records. For example, when residents move in, the analysis unit takes photos of each area of ​​the room with a smartphone camera and loads the images into the generation AI. The generation AI uses the emotion estimation function to detect areas where residents feel particularly anxious in real time and encourages them to keep detailed records. For example, it automatically highlights areas where residents feel anxious. This reduces the residents' anxiety and encourages them to keep detailed records.

[0061] When the comparison unit compares images taken at the time of occupancy and at the time of departure, the AI ​​can automatically identify areas that can be repaired and calculate the repair costs. For example, when the occupant moves out, the comparison unit takes photos of each area of ​​the room with a smartphone camera and loads the images into the generation AI. The generation AI compares the images taken at the time of occupancy and at the time of departure, automatically identifies areas that can be repaired, and calculates the repair costs. For example, it identifies scratches on the walls and stains on the floor and calculates the repair costs. This makes it possible to identify areas that can be repaired and calculate the repair costs.

[0062] When comparing images, the comparison unit takes into account the frequency and method of use of the room, allowing for more accurate cost calculations. For example, when a resident moves out, the comparison unit takes photos of each part of the room with a smartphone camera and loads the images into the generation AI. When comparing images, the generation AI takes into account the frequency and method of use of the room, allowing for more accurate cost calculations. For example, if the living room is used frequently, the generation AI will take into account its deterioration when calculating costs. This allows for more accurate cost calculations that take into account the room's usage status.

[0063] The comparison unit uses the emotion estimation function to analyze the emotions of the person moving out and perform a detailed cost calculation for areas where they feel particularly anxious. For example, the comparison unit takes photos of each area of ​​the room with a smartphone camera when the person moves out and loads the images into the generation AI. The generation AI uses the emotion estimation function to analyze the emotions of the person moving out and perform a detailed cost calculation for areas where they feel particularly anxious. For example, it can focus on calculating costs for areas where the person feels anxious. This reduces the person's anxiety and allows for a detailed cost calculation.

[0064] The comparison unit compares the comparison results at the time of move-out with data on other rental properties and can calculate costs based on market prices. For example, the comparison unit takes photos of each part of the room with a smartphone camera when the tenant moves out and loads the images into the generation AI. The generation AI compares the comparison results at the time of move-out with data on other rental properties and calculates costs based on market prices. For example, it references repair cost data for other properties in the same area. This allows costs to be calculated based on market prices.

[0065] The comparison unit allows the tenant and owner to share the comparison results at the time of move-out in real time, enabling a cost calculation that is satisfactory to both parties. For example, the comparison unit takes photos of each part of the room with a smartphone camera when the tenant moves out, and loads the images into the generation AI. The generation AI then shares the comparison results at the time of move-out with the tenant and owner in real time, enabling a cost calculation that is satisfactory to both parties. For example, the comparison results can be shared on an online platform so that both parties can view them. This allows a cost calculation that is satisfactory to both the tenant and the owner.

[0066] The comparison unit uses the emotion estimation function to detect areas in real time where the resident feels particularly anxious and prompts for a detailed cost calculation. For example, the comparison unit takes photos of each area of ​​the room with a smartphone camera when the resident moves out and loads the images into the generation AI. The generation AI uses the emotion estimation function to detect areas in real time where the resident feels particularly anxious and prompts for a detailed cost calculation. For example, it automatically highlights areas where the resident feels anxious. This reduces the resident's anxiety and allows for a detailed cost calculation.

[0067] The recommendation unit can analyze the design and color of the room and make suggestions for interior coordination. For example, when moving in, the recommendation unit takes photos of various parts of the room with a smartphone camera and loads the images into the generation AI. The generation AI analyzes the design and color of the room and makes suggestions for interior coordination. For example, it suggests furniture and interior items that match the wall color and floor material. This makes it possible to suggest interior coordination based on the room's design and color.

[0068] The recommendation unit uses the emotion estimation function to analyze the resident's preferences and emotions, and can recommend the furniture and appliances that will provide the highest level of satisfaction. For example, when the resident moves in, the recommendation unit takes photos of each area of ​​the room with a smartphone camera and loads the images into the generation AI. The generation AI then uses the emotion estimation function to analyze the resident's preferences and emotions, and recommends the furniture and appliances that will provide the highest level of satisfaction. For example, it can suggest furniture with colors and designs that the resident prefers. This makes it possible to recommend the furniture and appliances that will provide the highest level of satisfaction based on the resident's preferences and emotions.

[0069] The recommendation unit makes recommendations for furniture and appliances based on the reviews and ratings of other tenants, thereby increasing reliability. For example, when a tenant moves in, the recommendation unit takes photos of each area of ​​the room with a smartphone camera and loads the images into the generation AI. The generation AI makes recommendations for furniture and appliances based on the reviews and ratings of other tenants, thereby increasing reliability. For example, it can suggest furniture that has been highly rated by other tenants. This allows for highly reliable recommendations based on the reviews and ratings of other tenants.

[0070] The recommendation unit dynamically updates its furniture and appliance recommendations according to the season and trends, making it possible to make the latest suggestions. For example, when a resident moves in, the recommendation unit takes photos of each part of the room with a smartphone camera and loads the images into the generation AI. The generation AI dynamically updates its furniture and appliance recommendations according to the season and trends, making the latest suggestions. For example, it suggests interior items that match the season. This makes it possible to suggest the latest furniture and appliances that match the season and trends.

[0071] The recommendation unit uses the emotion estimation function to detect in real time which furniture and appliances the resident will particularly like and make recommendations. For example, when the resident moves in, the recommendation unit takes photos of each part of the room with a smartphone camera and loads the images into the generation AI. The generation AI uses the emotion estimation function to detect in real time which furniture and appliances the resident will particularly like and make recommendations. For example, it can suggest furniture with a design that the resident prefers. This makes it possible to recommend furniture and appliances that the resident will particularly like in real time.

[0072] The advice unit can analyze images of the room, identify areas that are prone to getting dirty, and suggest specific cleaning methods. For example, when a resident moves in, the advice unit takes photos of each area of ​​the room with a smartphone camera and loads the images into the generating AI. The generating AI then analyzes the images of the room, identifies areas that are prone to getting dirty, and suggests specific cleaning methods. For example, it suggests ways to prevent grease stains in the kitchen and mold in the bathroom. This makes it possible to identify areas that are prone to getting dirty and suggest specific cleaning methods.

[0073] The advice unit can monitor room usage and automatically generate a regular maintenance schedule. For example, when a resident moves in, the advice unit takes photos of each part of the room with a smartphone camera and loads the images into the generation AI. The generation AI monitors room usage and automatically generates a regular maintenance schedule. For example, it suggests cleaning schedules for the kitchen and bathroom. This makes it possible to monitor room usage and automatically generate a regular maintenance schedule.

[0074] The advice unit can use the emotion estimation function to suggest cleaning and maintenance methods to reduce stress for residents. For example, when a resident moves in, the advice unit takes photos of various parts of the room with a smartphone camera and loads the images into the generation AI. The generation AI then uses the emotion estimation function to suggest cleaning and maintenance methods to reduce stress for residents. For example, it can suggest easy cleaning methods. This makes it possible to suggest cleaning and maintenance methods to reduce stress for residents.

[0075] The advice unit can customize its advice for keeping the room clean based on the experience and knowledge of other residents. For example, when a resident moves in, the advice unit takes photos of various parts of the room with a smartphone camera and loads the images into the generating AI. The generating AI then customizes its advice for keeping the room clean based on the experience and knowledge of other residents. For example, it can suggest cleaning methods that other residents use. This makes it possible to provide customized advice based on the experience and knowledge of other residents.

[0076] The advice unit dynamically updates its advice for keeping the room clean according to the season and weather, making optimal suggestions. For example, when a resident moves in, the advice unit takes photos of various parts of the room with a smartphone camera and loads the images into the generating AI. The generating AI dynamically updates its advice for keeping the room clean according to the season and weather, making optimal suggestions. For example, it suggests measures to prevent mold during the rainy season. This makes it possible to provide optimal advice according to the season and weather.

[0077] The advice unit uses the emotion estimation function to detect areas that the resident is particularly concerned about in real time and can provide specific advice. For example, when the resident moves in, the advice unit takes photos of each area of ​​the room with a smartphone camera and loads the images into the generation AI. The generation AI uses the emotion estimation function to detect areas that the resident is particularly concerned about in real time and can provide specific advice. For example, it can suggest cleaning methods for areas that the resident is particularly concerned about. This makes it possible to provide specific advice for areas that the resident is particularly concerned about.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] When performing image analysis, the analysis unit also records the acoustic characteristics of the room and can make suggestions for future acoustic improvements. For example, it measures the room's reverberation and noise levels and suggests appropriate acoustic measures. This allows the room's acoustic environment to be optimized, supporting a more comfortable lifestyle.

[0080] The recommendation unit can suggest optimal lighting arrangements based on the room layout. For example, it can suggest the optimal position and type of lighting fixtures depending on the size of the room and the arrangement of furniture. This helps maintain uniform brightness throughout the room, providing a comfortable living environment.

[0081] The advice module analyzes images of the room and can suggest the placement and type of plants. For example, it can suggest the most suitable houseplants based on the amount of light and humidity in the room. This can improve the air quality in the room and provide a relaxing environment.

[0082] The analysis unit uses the emotion estimation function to analyze the stress level of residents and propose interior design ideas that will help them relax. For example, it can suggest furniture with colors and designs that will help residents relax. This reduces stress for residents and provides a comfortable living environment.

[0083] The recommendation unit uses emotion estimation to analyze the preferences and emotions of residents and suggest the most suitable entertainment equipment. For example, it can suggest the most suitable TV or speaker based on the residents' favorite movies and music. This improves the residents' entertainment experience.

[0084] The advice unit can analyze images of a room and suggest optimal storage methods, such as furniture and storage ideas for making the most effective use of space in the room. This helps keep the room tidy and provides a comfortable living environment.

[0085] The analysis unit uses the emotion estimation function to analyze the emotions of residents and propose maintenance schedules for areas of particular concern. For example, it can propose regular maintenance for areas that cause residents anxiety. This reduces residents' anxiety and provides a comfortable living environment.

[0086] The recommendation unit uses the emotion estimation function to analyze the emotions of residents and suggest the most suitable relaxation items. For example, it can suggest aroma diffusers or massage chairs that will help residents relax. This can improve the relaxation experience for residents.

[0087] The advice unit can analyze images of the room and suggest optimal security measures. For example, it can suggest installing security cameras or security systems based on the location of windows and doors in the room. This ensures the safety of residents and provides a safe and secure living environment.

[0088] The analysis unit uses the emotion estimation function to analyze the emotions of residents and propose interior design solutions for areas of particular concern. For example, it can make suggestions for improving the design of areas that make residents feel uneasy. This can reduce residents' anxiety and provide a comfortable living environment.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The image acquisition unit acquires images taken with a smartphone camera. For example, it can acquire an image of the entire room or specific furniture or equipment. The image acquisition unit can also acquire images through a smartphone camera application. Step 2: The analysis unit analyzes the captured images. For example, image recognition technology is used to record the detailed condition of walls, floors, ceilings, and equipment. The analysis unit can also use AI algorithms to extract image features and record the current situation. Step 3: The comparison unit compares the images analyzed by the analysis unit at the time of occupancy and the time of vacating. For example, it calculates the degree of similarity between the images and detects whether there are any changes. The comparison unit can also identify any changes and record the details. Step 4: The recommendation unit recommends furniture and home appliances based on the results of the comparison by the comparison unit. For example, it may suggest sofas and tables of optimal sizes based on the size and layout of the room. The recommendation unit can also make recommendations taking into account the user's preferences and past purchase history. Step 5: The advice unit provides advice on how to keep the room clean based on the images analyzed by the analysis unit. For example, it suggests how often to clean and what cleaning tools to use. The advice unit can also provide tips for daily maintenance.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

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

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

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

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

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

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

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

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

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

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

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

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

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

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

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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. an image acquisition unit that acquires an image taken by a smartphone camera; an analysis unit that analyzes the image acquired by the image acquisition unit; a comparison unit that compares the images analyzed by the analysis unit at the time of moving in with the images analyzed at the time of moving out; a recommendation unit that recommends furniture and home appliances based on the results of the comparison by the comparison unit; an advice unit that provides advice on how to use the room without making it dirty based on the image analyzed by the analysis unit. A system characterized by:

2. The analysis unit A 3D model of the room is automatically generated from the image, and a detailed current status record is created.

2. The system of claim 1.

3. The analysis unit The current status of the property upon moving in is recorded in the form of voice memos and videos, which are then analyzed by the AI ​​generator and converted into text.

2. The system of claim 1.

4. The comparison unit By comparing the images taken at the time of occupancy and at the time of occupancy, the AI ​​automatically identifies areas that can be repaired and calculates the repair costs.

2. The system of claim 1.

5. The recommendation unit Based on the image and floor plan data of the room, we simulate furniture placement and propose the optimal layout.

2. The system of claim 1.

6. The advice unit Analyze the image of the room, identify areas that are prone to getting dirty, and suggest specific cleaning methods 2. The system of claim 1.

7. The analysis unit Using the emotion estimation function, the system analyzes the emotions of residents and records details of areas of particular concern.

2. The system of claim 1.

8. The comparison unit Using emotion estimation function, we analyze the emotions of those who are leaving and calculate the costs in detail for areas where they are particularly anxious.

2. The system of claim 1.

Images